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Boulogne LH, Lorenz J, Kienzle D, Schön R, Ludwig K, Lienhart R, Jégou S, Li G, Chen C, Wang Q, Shi D, Maniparambil M, Müller D, Mertes S, Schröter N, Hellmann F, Elia M, Dirks I, Bossa MN, Berenguer AD, Mukherjee T, Vandemeulebroucke J, Sahli H, Deligiannis N, Gonidakis P, Huynh ND, Razzak I, Bouadjenek R, Verdicchio M, Borrelli P, Aiello M, Meakin JA, Lemm A, Russ C, Ionasec R, Paragios N, van Ginneken B, Revel MP. The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. Med Image Anal 2024; 97:103230. [PMID: 38875741 DOI: 10.1016/j.media.2024.103230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 01/11/2024] [Accepted: 06/03/2024] [Indexed: 06/16/2024]
Abstract
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.
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Affiliation(s)
- Luuk H Boulogne
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands.
| | - Julian Lorenz
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany.
| | - Daniel Kienzle
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Robin Schön
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Katja Ludwig
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | - Rainer Lienhart
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany
| | | | - Guang Li
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China.
| | - Cong Chen
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Qi Wang
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Derik Shi
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China
| | - Mayug Maniparambil
- ML-Labs, Dublin City University, N210, Marconi building, Dublin City University, Glasnevin, Dublin 9, Ireland.
| | - Dominik Müller
- University of Augsburg, Universitätsstraße 2, 86159 Augsburg, Germany; Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Silvan Mertes
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Niklas Schröter
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Fabio Hellmann
- Faculty of Applied Computer Science, University of Augsburg, Germany
| | - Miriam Elia
- Faculty of Applied Computer Science, University of Augsburg, Germany.
| | - Ine Dirks
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium.
| | - Matías Nicolás Bossa
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Abel Díaz Berenguer
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Tanmoy Mukherjee
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Jef Vandemeulebroucke
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Hichem Sahli
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Nikos Deligiannis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | - Panagiotis Gonidakis
- Vrije Universiteit Brussel, Department of Electronics and Informatics, Pleinlaan 2, 1050 Brussels, Belgium; imec, Kapeldreef 75, 3001 Leuven, Belgium
| | | | - Imran Razzak
- University of New South Wales, Sydney, Australia.
| | | | | | | | | | - James A Meakin
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Alexander Lemm
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Christoph Russ
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Razvan Ionasec
- Amazon Web Services, Marcel-Breuer-Str. 12, 80807 München, Germany
| | - Nikos Paragios
- Keya medical technology co. ltd, Floor 20, Building A, 1 Ronghua South Road, Yizhuang Economic Development Zone, Daxing District, Beijing, PR China; TheraPanacea, 75004, Paris, France
| | - Bram van Ginneken
- Radboud university medical center, P.O. Box 9101, 6500HB Nijmegen, The Netherlands
| | - Marie-Pierre Revel
- Department of Radiology, Université de Paris, APHP, Hôpital Cochin, 27 rue du Fg Saint Jacques, 75014 Paris, France
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400595. [PMID: 38958517 PMCID: PMC11423253 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Deniz Sadighbayan
- Department of BiologyFaculty of ScienceYork UniversityTorontoONM3J 1P3Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision SciencesUniversity of TorontoOntarioM5T 3A9Canada
- Institute of Health PolicyManagement and EvaluationUniversity of TorontoOntarioM5T 3M6Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
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Lin J, Yang J, Yin M, Tang Y, Chen L, Xu C, Zhu S, Gao J, Liu L, Liu X, Gu C, Huang Z, Wei Y, Zhu J. Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1312-1322. [PMID: 38448758 PMCID: PMC11300735 DOI: 10.1007/s10278-024-01066-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Abstract
We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.
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Affiliation(s)
- Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Jin Yang
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Yuxiu Tang
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Liquan Chen
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
| | - Chang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Shiqi Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China
| | - Chenqi Gu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Zhou Huang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Yao Wei
- Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Jiangsu, Suzhou 215006, China.
- Suzhou Clinical Center of Digestive Diseases, Suzhou, China.
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4
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Lu F, Zhang Z, Zhao S, Lin X, Zhang Z, Jin B, Gu W, Chen J, Wu X. CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:789-802. [PMID: 37028373 DOI: 10.1109/tcbb.2023.3253901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading.
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Shao J, Ma J, Yu Y, Zhang S, Wang W, Li W, Wang C. A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections. Innovation (N Y) 2024; 5:100648. [PMID: 39021525 PMCID: PMC11253137 DOI: 10.1016/j.xinn.2024.100648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/19/2024] [Indexed: 07/20/2024] Open
Abstract
Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904-0.916) and 0.887 (95% CI: 0.867-0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805-0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775-0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.
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Affiliation(s)
- Jun Shao
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
| | - Jiechao Ma
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong SAR, China
| | - Shu Zhang
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Wenyang Wang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
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Hiremath A, Viswanathan VS, Bera K, Shiradkar R, Yuan L, Armitage K, Gilkeson R, Ji M, Fu P, Gupta A, Lu C, Madabhushi A. Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study. Comput Biol Med 2024; 177:108643. [PMID: 38815485 PMCID: PMC11188049 DOI: 10.1016/j.compbiomed.2024.108643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/01/2024]
Abstract
Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.
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Affiliation(s)
- Amogh Hiremath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, OH, USA; Picture Health, Cleveland, OH, USA
| | | | - Kaustav Bera
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | | | - Lei Yuan
- Renmin Hospital of Wuhan University, Department of Information Center, Wuhan, Hubei, China
| | - Keith Armitage
- University Hospitals Cleveland Medical Center, Department of Infectious Diseases, Cleveland, OH, USA
| | - Robert Gilkeson
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Mengyao Ji
- Renmin Hospital of Wuhan University, Department of Gastroenterology, Wuhan, Hubei, China
| | - Pingfu Fu
- Case Western Reserve University, Department of Population and Quantitative Health Sciences, Cleveland, OH, USA
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Department of Radiology, Cleveland, OH, USA
| | - Cheng Lu
- Guangdong Provincial People's Hospital, Department of Radiology, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial People's Hospital, Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial People's Hospital, Medical Research Center, Guangdong Academy of Medical Sciences, China
| | - Anant Madabhushi
- Georgia Institute of Technology and Emory University, Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, GA, USA; Atlanta Veterans Administration Medical Center, GA, USA.
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Crombé A, Lecomte JC, Seux M, Banaste N, Gorincour G. Using the Textual Content of Radiological Reports to Detect Emerging Diseases: A Proof-of-Concept Study of COVID-19. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:620-632. [PMID: 38343242 PMCID: PMC11031522 DOI: 10.1007/s10278-023-00949-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/02/2023] [Accepted: 10/04/2023] [Indexed: 04/20/2024]
Abstract
Changes in the content of radiological reports at population level could detect emerging diseases. Herein, we developed a method to quantify similarities in consecutive temporal groupings of radiological reports using natural language processing, and we investigated whether appearance of dissimilarities between consecutive periods correlated with the beginning of the COVID-19 pandemic in France. CT reports from 67,368 consecutive adults across 62 emergency departments throughout France between October 2019 and March 2020 were collected. Reports were vectorized using time frequency-inverse document frequency (TF-IDF) analysis on one-grams. For each successive 2-week period, we performed unsupervised clustering of the reports based on TF-IDF values and partition-around-medoids. Next, we assessed the similarities between this clustering and a clustering from two weeks before according to the average adjusted Rand index (AARI). Statistical analyses included (1) cross-correlation functions (CCFs) with the number of positive SARS-CoV-2 tests and advanced sanitary index for flu syndromes (ASI-flu, from open-source dataset), and (2) linear regressions of time series at different lags to understand the variations of AARI over time. Overall, 13,235 chest CT reports were analyzed. AARI was correlated with ASI-flu at lag = + 1, + 5, and + 6 weeks (P = 0.0454, 0.0121, and 0.0042, respectively) and with SARS-CoV-2 positive tests at lag = - 1 and 0 week (P = 0.0057 and 0.0001, respectively). In the best fit, AARI correlated with the ASI-flu with a lag of 2 weeks (P = 0.0026), SARS-CoV-2-positive tests in the same week (P < 0.0001) and their interaction (P < 0.0001) (adjusted R2 = 0.921). Thus, our method enables the automatic monitoring of changes in radiological reports and could help capturing disease emergence.
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Affiliation(s)
- Amandine Crombé
- IMADIS, Lyon, France.
- SARCOTARGET Team, University of Bordeaux, Inserm, UMR1312, BRIC, BoRdeaux Institute of Oncology, 146 Rue Léo Saignat, Bordeaux, F-33076, France.
- Department of Radiology, Pellegrin University Hospital, CHU Bordeaux, Place Amélie Raba-Léon, Bordeaux, F-33076, France.
| | - Jean-Christophe Lecomte
- IMADIS, Lyon, France
- Centre Aquitain d'Imagerie médicale, Mérignac, France
- Centre Hospitalier de Saintes, Saintes, France
- Clinique Mutualiste Bordeaux Pessac, Pessac, France
| | | | - Nathan Banaste
- IMADIS, Lyon, France
- Clinique Convert, Ramsay, Bourg en Bresse, France
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Libera K, Valadian R, Vararattanavech P, Dasari SN, Dallman TJ, Weerts E, Lipman L. Inspection of chicken wings and legs for animal welfare monitoring using X-ray computed tomography, visual examination, and histopathology. Poult Sci 2024; 103:103403. [PMID: 38290340 PMCID: PMC10844867 DOI: 10.1016/j.psj.2023.103403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024] Open
Abstract
In broiler chickens, fractures of wings and legs are recorded at poultry slaughterhouses based on the time of occurrence. Prekilling (PRE) fractures occur before the death of animal, so the chicken was still able to experience pain and distress associated with the injury (an animal welfare issue). Postkilling (POST) fractures occur when the chickens are deceased and fully bled-out and consequently unable to feel pain (not an animal welfare issue). Current practice dictates that fractures are recognized visually and recorded by the animal welfare officers as mandated by European Union and/or national regulations. However, new potential monitoring solutions are desired since human inspection suffers from some significant limitations including subjectivism and fatigue. One possible solution in detecting injuries is X-ray computed tomography (CT) scanning and in this study we aim to evaluate the potential of CT scanning and visual inspection in detecting limb fractures and their causes. Eighty-three chicken wings and 60 chicken legs (n = 143) were collected from a single slaughterhouse and classified by an animal welfare officer as PRE, POST or healthy (HEAL). Samples were photographed and CT scanned at a veterinary hospital. The interpretation of CT scans along with photographs took place in 3 rounds (1. CT scans only, 2. CT scans + photographs, 3. photographs only) and was performed independently by 3 veterinarians. The consistency of the interpretation in 3 rounds was compared with the animal welfare officer's classification. Furthermore, selected samples were also analyzed by histopathological examination due to questionability of their classification (PRE/POST). In questionable samples, presence of hemorrhages was confirmed, thus they fit better as PRE. The highest consistency between raters was obtained in the 2nd round, indicating that interpretation accuracy was the highest when CT scans were combined with photographs. These results indicate that CT scanning in combination with visual inspection can be used in detecting limbs fracture and potentially applied as a tool to monitor animal welfare in poultry slaughterhouses in the future.
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Affiliation(s)
- Kacper Libera
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Roozbeh Valadian
- Department of Computational Imaging, Centrum Wiskunde & Informatica, 1098 XG Amsterdam, The Netherlands
| | - Patiharn Vararattanavech
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Sri Nithya Dasari
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Timothy J Dallman
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Erik Weerts
- Division of Pathology, Department Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, 3584 CL Utrecht, The Netherlands
| | - Len Lipman
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, 3584 CM Utrecht, The Netherlands.
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9
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Lv C, Guo W, Yin X, Liu L, Huang X, Li S, Zhang L. Innovative applications of artificial intelligence during the COVID-19 pandemic. INFECTIOUS MEDICINE 2024; 3:100095. [PMID: 38586543 PMCID: PMC10998276 DOI: 10.1016/j.imj.2024.100095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/16/2023] [Accepted: 02/18/2024] [Indexed: 04/09/2024]
Abstract
The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.
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Affiliation(s)
- Chenrui Lv
- Huazhong Agricultural University, Wuhan 430070, China
| | - Wenqiang Guo
- Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Huazhong Agricultural University, Wuhan 430070, China
| | - Liu Liu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research, Shanghai 200001, China
| | - Xinlei Huang
- Huazhong Agricultural University, Wuhan 430070, China
| | - Shimin Li
- Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Huazhong Agricultural University, Wuhan 430070, China
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10
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Waldner S, Wendelspiess E, Detampel P, Schlepütz CM, Huwyler J, Puchkov M. Advanced analysis of disintegrating pharmaceutical compacts using deep learning-based segmentation of time-resolved micro-tomography images. Heliyon 2024; 10:e26025. [PMID: 38384517 PMCID: PMC10878950 DOI: 10.1016/j.heliyon.2024.e26025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 01/30/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
The mechanism governing pharmaceutical tablet disintegration is far from fully understood. Despite the importance of controlling a formulation's disintegration process to maximize the active pharmaceutical ingredient's bioavailability and ensure predictable and consistent release profiles, the current understanding of the process is based on indirect or superficial measurements. Formulation science could, therefore, additionally deepen the understanding of the fundamental physical principles governing disintegration based on direct observations of the process. We aim to help bridge the gap by generating a series of time-resolved X-ray micro-computed tomography (μCT) images capturing volumetric images of a broad range of mini-tablet formulations undergoing disintegration. Automated image segmentation was a prerequisite to overcoming the challenges of analyzing multiple time series of heterogeneous tomographic images at high magnification. We devised and trained a convolutional neural network (CNN) based on the U-Net architecture for autonomous, rapid, and consistent image segmentation. We created our own μCT data reconstruction pipeline and parameterized it to deliver image quality optimal for our CNN-based segmentation. Our approach enabled us to visualize the internal microstructures of the tablets during disintegration and to extract parameters of disintegration kinetics from the time-resolved data. We determine by factor analysis the influence of the different formulation components on the disintegration process in terms of both qualitative and quantitative experimental responses. We relate our findings to known formulation component properties and established experimental results. Our direct imaging approach, enabled by deep learning-based image processing, delivers new insights into the disintegration mechanism of pharmaceutical tablets.
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Affiliation(s)
- Samuel Waldner
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Erwin Wendelspiess
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Pascal Detampel
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | | | - Jörg Huwyler
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
| | - Maxim Puchkov
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology, University of Basel, Klingelberstrasse 50, 4056, Basel, Switzerland
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11
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Soliman S, Soliman H, Crézé M, Brillet PY, Montani D, Savale L, Jais X, Bulifon S, Jutant EM, Rius E, Devilder M, Beurnier A, Colle R, Gasnier M, Pham T, Morin L, Noel N, Lecoq AL, Becquemont L, Figueiredo S, Harrois A, Bellin MF, Monnet X, Meyrignac O. Radiological pulmonary sequelae after COVID-19 and correlation with clinical and functional pulmonary evaluation: results of a prospective cohort. Eur Radiol 2024; 34:1037-1052. [PMID: 37572192 DOI: 10.1007/s00330-023-10044-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 05/29/2023] [Accepted: 06/20/2023] [Indexed: 08/14/2023]
Abstract
OBJECTIVES Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19. MATERIALS AND METHODS We conducted a prospective single-center study among patients hospitalized for COVID-19 between March and May 2020. Patients with residual symptoms or admitted into intensive care units were investigated 4 months after discharge by a chest CT (CCT) and pulmonary function tests (PFTs). The primary endpoint was the rate of persistent radiological fibrotic lesions after 4 months. Secondary endpoints included further CCT evaluation at 9 and 16 months, correlation of fibrotic lesions with clinical and PFT evaluation, and assessment of predictive factors. RESULTS Among the 1151 patients hospitalized for COVID-19, 169 patients performed a CCT at 4 months. CCTs showed pulmonary fibrotic lesions in 19% of the patients (32/169). These lesions were persistent at 9 months and 16 months in 97% (29/30) and 95% of patients (18/19) respectively. There was no significant clinical difference based on dyspnea scale in patients with pulmonary fibrosis. However, PFT evaluation showed significantly decreased diffusing lung capacity for carbon monoxide (p < 0.001) and total lung capacity (p < 0.001) in patients with radiological lesions. In multivariate analysis, the predictive factors of radiological pulmonary fibrotic lesions were pulmonary embolism (OR = 9.0), high-flow oxygen (OR = 6.37), and mechanical ventilation (OR = 3.49). CONCLUSION At 4 months, 19% of patients investigated after hospitalization for COVID-19 had radiological pulmonary fibrotic lesions; they persisted up to 16 months. CLINICAL RELEVANCE STATEMENT Whether COVID-19 leads to long-term pulmonary sequelae or not remains unknown. The aim of this study was to assess the prevalence of persisting radiological pulmonary fibrotic lesions in patients hospitalized for COVID-19. The prevalence of persisting lesions after COVID-19 remains unclear. We assessed this prevalence and predictive factors leading to fibrotic lesions in a large cohort. The respiratory clinical impact of these lesions was also assessed. KEY POINTS • Nineteen percent of patients hospitalized for COVID-19 had radiological fibrotic lesions at 4 months, remaining stable at 16 months. • COVID-19 fibrotic lesions did not match any infiltrative lung disease pattern. • COVID-19 fibrotic lesions were associated with pulmonary function test abnormalities but did not lead to clinical respiratory manifestation.
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Affiliation(s)
- Samer Soliman
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France.
| | - Heithem Soliman
- Service de Gastro-Entérologie, Université Paris-Cité, AP-HP Nord, Hôpital Louis Mourier, Colombes, France
| | - Maud Crézé
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Pierre-Yves Brillet
- Service de Radiologie Diagnostique, Université Sorbonne Paris-Nord, AP-HP, Hôpital Avicenne, Bobigny, France
| | - David Montani
- DMU 5, Thorinno, Service de Pneumologie Et Soins Intensifs Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Laurent Savale
- DMU 5, Thorinno, Service de Pneumologie Et Soins Intensifs Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Xavier Jais
- DMU 5, Thorinno, Service de Pneumologie Et Soins Intensifs Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Sophie Bulifon
- DMU 5, Thorinno, Service de Pneumologie Et Soins Intensifs Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Etienne-Marie Jutant
- DMU 5, Thorinno, Service de Pneumologie Et Soins Intensifs Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Emily Rius
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Matthieu Devilder
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Antoine Beurnier
- DMU 5 Thorinno, Service de Physiologie Et d'Explorations Fonctionnelles Respiratoires, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Inserm UMR_S999, Le Kremlin-Bicêtre, France
| | - Romain Colle
- DMU 11 Psychiatrie, Santé Mentale, Addictologie Et Nutrition, Service de Psychiatrie, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Équipe MOODS, INSERM U1178, CESP (Centre de Recherche en Epidémiologie Et Santé Des Populations), Le Kremlin-Bicêtre, France
| | - Matthieu Gasnier
- DMU 11 Psychiatrie, Santé Mentale, Addictologie Et Nutrition, Service de Psychiatrie, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Équipe MOODS, INSERM U1178, CESP (Centre de Recherche en Epidémiologie Et Santé Des Populations), Le Kremlin-Bicêtre, France
| | - Tài Pham
- DMU 4 CORREVE Maladies du Cœur Et Des Vaisseaux,Service de Médecine Intensive-Réanimation, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, FHU Sepsis, Le Kremlin-Bicêtre, France
| | - Luc Morin
- Service de Réanimation Pédiatrique Et Médecine Néonatale, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Santé de L'Enfant Et de L'Adolescent, Le Kremlin-Bicêtre, France
| | - Nicolas Noel
- DMU 7 Endocrinologie-Immunités-Inflammations Cancer-Urgences, Service de Médecine Interne Et Immunologie Clinique, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Anne-Lise Lecoq
- DMU 13 Santé Publique, Information Médicale, Appui À La Recherche Clinique, Centre de Recherche Clinique Paris-Saclay, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, INSERM U1018, CESP, Le Kremlin-Bicêtre, France
| | - Laurent Becquemont
- DMU 13 Santé Publique, Information Médicale, Appui À La Recherche Clinique, Centre de Recherche Clinique Paris-Saclay, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, INSERM U1018, CESP, Le Kremlin-Bicêtre, France
| | - Samy Figueiredo
- DMU 12 Anesthésie, Réanimation, Douleur, Service de Réanimation Chirurgicale, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Anatole Harrois
- DMU 12 Anesthésie, Réanimation, Douleur, Service de Réanimation Chirurgicale, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Marie-France Bellin
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
| | - Xavier Monnet
- DMU 4 CORREVE Maladies du Cœur Et Des Vaisseaux,Service de Médecine Intensive-Réanimation, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, FHU Sepsis, Le Kremlin-Bicêtre, France
| | - Olivier Meyrignac
- Service de Radiologie Diagnostique Et Interventionnelle, Université Paris-Saclay, AP-HP, Hôpital de Bicêtre, Le Kremlin-Bicêtre, France
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12
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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13
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Ahmad I, Amelio A, Merla A, Scozzari F. A survey on the role of artificial intelligence in managing Long COVID. Front Artif Intell 2024; 6:1292466. [PMID: 38274052 PMCID: PMC10808521 DOI: 10.3389/frai.2023.1292466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.
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Affiliation(s)
- Ijaz Ahmad
- Department of Human, Legal and Economic Sciences, Telematic University “Leonardo da Vinci”, Chieti, Italy
| | - Alessia Amelio
- Department of Engineering and Geology, University “G. d'Annunzio” Chieti-Pescara, Pescara, Italy
| | - Arcangelo Merla
- Department of Engineering and Geology, University “G. d'Annunzio” Chieti-Pescara, Pescara, Italy
| | - Francesca Scozzari
- Laboratory of Computational Logic and Artificial Intelligence, Department of Economic Studies, University “G. d'Annunzio” Chieti-Pescara, Pescara, Italy
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14
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Jeong Y, Jeong C, Sung KY, Moon G, Lim J. Development of AI-Based Diagnostic Algorithm for Nasal Bone Fracture Using Deep Learning. J Craniofac Surg 2024; 35:29-32. [PMID: 38294297 DOI: 10.1097/scs.0000000000009856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 10/08/2023] [Indexed: 02/01/2024] Open
Abstract
Facial bone fractures are relatively common, with the nasal bone the most frequently fractured facial bone. Computed tomography is the gold standard for diagnosing such fractures. Most nasal bone fractures can be treated using a closed reduction. However, delayed diagnosis may cause nasal deformity or other complications that are difficult and expensive to treat. In this study, the authors developed an algorithm for diagnosing nasal fractures by learning computed tomography images of facial bones with artificial intelligence through deep learning. A significant concordance with human doctors' reading results of 100% sensitivity and 77% specificity was achieved. Herein, the authors report the results of a pilot study on the first stage of developing an algorithm for analyzing fractures in the facial bone.
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Affiliation(s)
- Yeonjin Jeong
- Department of Plastic and Reconstructive Surgery, National Medical Center, Seoul, Korea
| | - Chanho Jeong
- Department of Plastic and Reconstructive Surgery, Kangwon National University Hospital, Kangwon-do, Korea
| | - Kun-Yong Sung
- Department of Plastic and Reconstructive Surgery, Kangwon National University Hospital, Kangwon-do, Korea
| | - Gwiseong Moon
- Department of Computer Science and Engineering, Kangwon National University, Kangwon-do, Korea
| | - Jinsoo Lim
- Department of Plastic and Reconstructive Surgery, College of Medicine, The Catholic University of Korea, St. Vincent's Hospital, Gyeonggi-do, Korea
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15
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Chen H, Liu P, Chen Z, Chen Q, Wen Z, Xie Z. Predicting sequenced dental treatment plans from electronic dental records using deep learning. Artif Intell Med 2024; 147:102734. [PMID: 38184358 DOI: 10.1016/j.artmed.2023.102734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/26/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older. OBJECTIVES The aim of this study is to predict sequential treatment plans from electronic dental records. METHODS We construct a clinical decision support model, MultiTP, explores the unique topology of teeth information and the variation of complicated treatments, integrates deep learning models (convolutional neural network and recurrent neural network) adaptively, and embeds the attention mechanism to produce optimal treatment plans. RESULTS MultiTP shows its promising performance with an AUC of 0.9079 and an F score of 0.8472 over five treatment plans. The interpretability analysis also indicates its capability in mining clinical knowledge from the textual data. CONCLUSIONS MultiTP's novel problem formulation, neural network framework, and interpretability analysis techniques allow for broad applications of deep learning in dental healthcare, providing valuable support for predicting dental treatment plans in the clinic and benefiting dental patients. CLINICAL IMPLICATIONS The MultiTP is an efficient tool that can be implemented in clinical practice and integrated into the existing EDR system. By predicting treatment plans for partial edentulism, the model will help dentists improve their clinical decisions.
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Affiliation(s)
- Haifan Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Pufan Liu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Zhaoxing Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Qingxiao Chen
- Peking University School and Hospital of Stomatology, Beijing, PR China; Georgia Institute of Technology, College of Computing, USA.
| | - Zaiwen Wen
- Beijing International Center for Mathematical Research, Peking University, Beijing, PR China
| | - Ziqing Xie
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
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16
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Cha MJ, Solomon JJ, Lee JE, Choi H, Chae KJ, Lee KS, Lynch DA. Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives. Radiology 2024; 310:e231643. [PMID: 38193836 PMCID: PMC10831480 DOI: 10.1148/radiol.231643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 09/06/2023] [Accepted: 09/26/2023] [Indexed: 01/10/2024]
Abstract
With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.
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Affiliation(s)
- Min Jae Cha
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Joshua J. Solomon
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Jong Eun Lee
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Hyewon Choi
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Kum Ju Chae
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - Kyung Soo Lee
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
| | - David A. Lynch
- From the Department of Radiology, Chung-Ang University Hospital,
Seoul, Korea (M.J.C., H.C.); Departments of Medicine (J.J.S.) and Radiology
(K.J.C., D.A.L.), National Jewish Health, 1400 Jackson St, Denver, CO 80206;
Department of Radiology, Chonnam National University Hospital, Gwangju, Republic
of Korea (J.E.L.); Department of Radiology, Research Institute of Clinical
Medicine of Jeonbuk National University, Biomedical Research Institute of
Jeonbuk National University Hospital, Jeonju, Republic of Korea (K.J.C); and
Department of Radiology, Sungkyunkwan University School of Medicine and Samsung
ChangWon Hospital, Gyeongsangnam, Republic of Korea (K.S.L.)
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17
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Hussain S, Songhua X, Aslam MU, Hussain F. Clinical predictions of COVID-19 patients using deep stacking neural networks. J Investig Med 2024; 72:112-127. [PMID: 37712431 DOI: 10.1177/10815589231201103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
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Affiliation(s)
- Sajid Hussain
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | - Xu Songhua
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | | | - Fida Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
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18
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Pagallo U, O’Sullivan S, Nevejans N, Holzinger A, Friebe M, Jeanquartier F, Jean-Quartier C, Miernik A. The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations. HEALTH AND TECHNOLOGY 2023; 14:1-14. [PMID: 38229886 PMCID: PMC10788319 DOI: 10.1007/s12553-023-00806-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 11/16/2023] [Indexed: 01/18/2024]
Abstract
Purpose This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon. Methods The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health. Results Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem. Conclusions The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening. Graphical Abstract
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Affiliation(s)
- Ugo Pagallo
- Law School, University of Turin, Turin, Italy
| | - Shane O’Sullivan
- Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany
| | - Nathalie Nevejans
- Ethics and Procedures Center (CDEP), Faculty of Law of Douai, University of Artois, Arras, France
| | - Andreas Holzinger
- Human-Centered AI Lab, Medical University of Graz, Graz, Austria
- University of Natural Resources and Life Sciences Vienna, Human-Centered AI Lab, Vienna, Austria
| | - Michael Friebe
- Department of Measurements and Electronics, AGH University of Science and Technology, Krak’ow, Poland
- Faculty of Medicine, Otto-von-Guericke-University, Magdeburg, Germany
- Center for Innovation and Business Development, FOM University of Applied Sciences, Essen, Germany
| | | | | | - Arkadiusz Miernik
- Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany
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19
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Henao JAG, Depotter A, Bower DV, Bajercius H, Todorova PT, Saint-James H, de Mortanges AP, Barroso MC, He J, Yang J, You C, Staib LH, Gange C, Ledda RE, Caminiti C, Silva M, Cortopassi IO, Dela Cruz CS, Hautz W, Bonel HM, Sverzellati N, Duncan JS, Reyes M, Poellinger A. A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans. Invest Radiol 2023; 58:882-893. [PMID: 37493348 PMCID: PMC10662611 DOI: 10.1097/rli.0000000000001005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans. MATERIALS AND METHODS The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion. RESULTS AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95. CONCLUSIONS A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.
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20
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Tehrani SSM, Zarvani M, Amiri P, Ghods Z, Raoufi M, Safavi-Naini SAA, Soheili A, Gharib M, Abbasi H. Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data. BMC Med Inform Decis Mak 2023; 23:265. [PMID: 37978393 PMCID: PMC10656999 DOI: 10.1186/s12911-023-02344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 10/16/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data. METHODS We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models. RESULTS Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6). CONCLUSIONS We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes. SIGNIFICANCE Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.
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Affiliation(s)
| | - Maral Zarvani
- Faculty of Engineering, Alzahra University, Tehran, Iran
| | - Paria Amiri
- University of Erlangen-Nuremberg, Bavaria, Germany
| | - Zahra Ghods
- Faculty of Engineering, Alzahra University, Tehran, Iran
| | - Masoomeh Raoufi
- Department of Radiology, School of Medicine, Imam Hossein Hospital, Shahid Beheshti, University of Medical Sciences, Tehran, Iran
| | - Seyed Amir Ahmad Safavi-Naini
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amirali Soheili
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Hamid Abbasi
- Auckland Bioengineering Institute, University of Auckland, Auckland, 1010, New Zealand.
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21
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Schaudt D, von Schwerin R, Hafner A, Riedel P, Reichert M, von Schwerin M, Beer M, Kloth C. Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset. Sci Rep 2023; 13:18299. [PMID: 37880333 PMCID: PMC10600145 DOI: 10.1038/s41598-023-45532-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.
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Affiliation(s)
- Daniel Schaudt
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany.
| | - Reinhold von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Alexander Hafner
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Pascal Riedel
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Marianne von Schwerin
- Department of Computer Science, Ulm University of Applied Science, Albert-Einstein-Allee 55, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Meinrad Beer
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
| | - Christopher Kloth
- Department of Radiology, University Hospital of Ulm, Albert-Einstein-Allee 23, 89081, Ulm, Baden-Wurttemberg, Germany
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22
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Peng AW, Dudum R, Jain SS, Maron DJ, Patel BN, Khandwala N, Eng D, Chaudhari AS, Sandhu AT, Rodriguez F. Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular Outcomes. J Am Coll Cardiol 2023; 82:1192-1202. [PMID: 37704309 PMCID: PMC11009374 DOI: 10.1016/j.jacc.2023.06.040] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/30/2023] [Accepted: 06/20/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Coronary artery calcium (CAC) is a strong predictor of cardiovascular events across all racial and ethnic groups. CAC can be quantified on nonelectrocardiography (ECG)-gated computed tomography (CT) performed for other reasons, allowing for opportunistic screening for subclinical atherosclerosis. OBJECTIVES The authors investigated whether incidental CAC quantified on routine non-ECG-gated CTs using a deep-learning (DL) algorithm provided cardiovascular risk stratification beyond traditional risk prediction methods. METHODS Incidental CAC was quantified using a DL algorithm (DL-CAC) on non-ECG-gated chest CTs performed for routine care in all settings at a large academic medical center from 2014 to 2019. We measured the association between DL-CAC (0, 1-99, or ≥100) with all-cause death (primary outcome), and the secondary composite outcomes of death/myocardial infarction (MI)/stroke and death/MI/stroke/revascularization using Cox regression. We adjusted for age, sex, race, ethnicity, comorbidities, systolic blood pressure, lipid levels, smoking status, and antihypertensive use. Ten-year atherosclerotic cardiovascular disease risk was calculated using the pooled cohort equations. RESULTS Of 5,678 adults without ASCVD (51% women, 18% Asian, 13% Hispanic/Latinx), 52% had DL-CAC >0. Those with DL-CAC ≥100 had an average 10-year ASCVD risk of 24%; yet, only 26% were on statins. After adjustment, patients with DL-CAC ≥100 had increased risk of death (HR: 1.51; 95% CI: 1.28-1.79), death/MI/stroke (HR: 1.57; 95% CI: 1.33-1.84), and death/MI/stroke/revascularization (HR: 1.69; 95% CI: 1.45-1.98) compared with DL-CAC = 0. CONCLUSIONS Incidental CAC ≥100 was associated with an increased risk of all-cause death and adverse cardiovascular outcomes, beyond traditional risk factors. DL-CAC from routine non-ECG-gated CTs identifies patients at increased cardiovascular risk and holds promise as a tool for opportunistic screening to facilitate earlier intervention.
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Affiliation(s)
- Allison W Peng
- Department of Medicine, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA. https://twitter.com/AllisonWPeng
| | - Ramzi Dudum
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - Sneha S Jain
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA
| | - David J Maron
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, California, USA
| | - Bhavik N Patel
- Department of Radiology, Mayo Clinic, Phoenix, Arizona, USA
| | | | - David Eng
- Bunkerhill Health, Palo Alto, California, USA
| | - Akshay S Chaudhari
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Department of Radiology, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Alexander T Sandhu
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA; Veteran's Affairs Palo Alto Healthcare System, Palo Alto, California, USA. https://twitter.com/ATSandhu
| | - Fatima Rodriguez
- Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA; Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, California, USA.
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23
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Ghassemi N, Shoeibi A, Khodatars M, Heras J, Rahimi A, Zare A, Zhang YD, Pachori RB, Gorriz JM. Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Appl Soft Comput 2023; 144:110511. [PMID: 37346824 PMCID: PMC10263244 DOI: 10.1016/j.asoc.2023.110511] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 08/23/2022] [Accepted: 06/08/2023] [Indexed: 06/23/2023]
Abstract
The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.
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Affiliation(s)
- Navid Ghassemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Jonathan Heras
- Department of Mathematics and Computer Science, University of La Rioja, La Rioja, Spain
| | - Alireza Rahimi
- Computer Engineering department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Ram Bilas Pachori
- Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
| | - J Manuel Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Spain
- Department of Psychiatry, University of Cambridge, UK
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24
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Santosh KC, GhoshRoy D, Nakarmi S. A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022. Healthcare (Basel) 2023; 11:2388. [PMID: 37685422 PMCID: PMC10486542 DOI: 10.3390/healthcare11172388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (COVID-19 OR Coronavirus) AND chest CT AND (deep learning OR artificial intelligence OR medical imaging) on both the PubMed Central Repository and Web of Science platforms.
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Affiliation(s)
- KC Santosh
- 2AI: Applied Artificial Intelligence Research Lab, Vermillion, SD 57069, USA
| | - Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India;
| | - Suprim Nakarmi
- Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA;
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25
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Wang C, Liu S, Tang Y, Yang H, Liu J. Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25:e46340. [PMID: 37477951 PMCID: PMC10403760 DOI: 10.2196/46340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/27/2023] [Accepted: 06/30/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Deep learning (DL) prediction models hold great promise in the triage of COVID-19. OBJECTIVE We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. METHODS We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. RESULTS A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. CONCLUSIONS DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. TRIAL REGISTRATION PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.
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Affiliation(s)
- Changyu Wang
- Department of Medical Informatics, West China Medical School, Sichuan University, Chengdu, China
- West China College of Stomatology, Sichuan University, Chengdu, China
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yu Tang
- Xiangya School of Medicine, Central South University, Changsha, China
| | - Hao Yang
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jialin Liu
- Department of Medical Informatics, West China Medical School, Sichuan University, Chengdu, China
- Information Center, West China Hospital, Sichuan University, Chengdu, China
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Chauhan N, Choi BJ. Classification of Alzheimer's Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine. Brain Sci 2023; 13:1046. [PMID: 37508978 PMCID: PMC10377329 DOI: 10.3390/brainsci13071046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification.
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Affiliation(s)
- Nishant Chauhan
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
| | - Byung-Jae Choi
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
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27
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Gerhards C, Haselmann V, Schaible SF, Ast V, Kittel M, Thiel M, Hertel A, Schoenberg SO, Neumaier M, Froelich MF. Exploring the Synergistic Potential of Radiomics and Laboratory Biomarkers for Enhanced Identification of Vulnerable COVID-19 Patients. Microorganisms 2023; 11:1740. [PMID: 37512912 PMCID: PMC10384842 DOI: 10.3390/microorganisms11071740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Severe courses and high hospitalization rates were ubiquitous during the first pandemic SARS-CoV-2 waves. Thus, we aimed to examine whether integrative diagnostics may aid in identifying vulnerable patients using crucial data and materials obtained from COVID-19 patients hospitalized between 2020 and 2021 (n = 52). Accordingly, we investigated the potential of laboratory biomarkers, specifically the dynamic cell decay marker cell-free DNA and radiomics features extracted from chest CT. METHODS Separate forward and backward feature selection was conducted for linear regression with the Intensive-Care-Unit (ICU) period as the initial target. Three-fold cross-validation was performed, and collinear parameters were reduced. The model was adapted to a logistic regression approach and verified in a validation naïve subset to avoid overfitting. RESULTS The adapted integrated model classifying patients into "ICU/no ICU demand" comprises six radiomics and seven laboratory biomarkers. The models' accuracy was 0.54 for radiomics, 0.47 for cfDNA, 0.74 for routine laboratory, and 0.87 for the combined model with an AUC of 0.91. CONCLUSION The combined model performed superior to the individual models. Thus, integrating radiomics and laboratory data shows synergistic potential to aid clinic decision-making in COVID-19 patients. Under the need for evaluation in larger cohorts, including patients with other SARS-CoV-2 variants, the identified parameters might contribute to the triage of COVID-19 patients.
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Affiliation(s)
- Catharina Gerhards
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Verena Haselmann
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Samuel F Schaible
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Volker Ast
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Maximilian Kittel
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Manfred Thiel
- Department of Anaesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Alexander Hertel
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Stefan O Schoenberg
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
| | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim of the University of Heidelberg, Theodor Kutzer Ufer 1-3, 68167 Mannheim, Germany
| | - Matthias F Froelich
- Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim of the University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany
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28
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Su J, Zhang Y, Cheng L, Zhu L, Yang R, Niu F, Yang K, Duan Y. Oribron: An Origami-Inspired Deformable Rigid Bronchoscope for Radial Support. MICROMACHINES 2023; 14:822. [PMID: 37421055 DOI: 10.3390/mi14040822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 07/09/2023]
Abstract
The structure of a traditional rigid bronchoscope includes proximal, distal, and body, representing an important means to treat hypoxic diseases. However, the body structure is too simple, resulting in the utilization rate of oxygen being usually low. In this work, we reported a deformable rigid bronchoscope (named Oribron) by adding a Waterbomb origami structure to the body. The Waterbomb's backbone is made of films, and the pneumatic actuators are placed inside it to achieve rapid deformation at low pressure. Experiments showed that Waterbomb has a unique deformation mechanism, which can transform from a small-diameter configuration (#1) to a large-diameter configuration (#2), showing excellent radial support capability. When Oribron entered or left the trachea, the Waterbomb remained in #1. When Oribron is working, the Waterbomb transforms from #1 to #2. Since #2 reduces the gap between the bronchoscope and the tracheal wall, it effectively slows down the rate of oxygen loss, thus promoting the absorption of oxygen by the patient. Therefore, we believe that this work will provide a new strategy for the integrated development of origami and medical devices.
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Affiliation(s)
- Junjie Su
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
| | - Yangyang Zhang
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
| | - Liang Cheng
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
| | - Ling Zhu
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Runhuai Yang
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
| | - Fuzhou Niu
- School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Ke Yang
- Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Yuping Duan
- School of Biomedical Engineering, Anhui Medical University, Hefei 230009, China
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29
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Chauhan J, Bedi J. EffViT-COVID: A dual-path network for COVID-19 percentage estimation. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:118939. [PMID: 36210962 PMCID: PMC9527203 DOI: 10.1016/j.eswa.2022.118939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/12/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labeled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves 0 . 9886 ± 0 . 009 , 1 . 23 ± 0 . 378 , and 3 . 12 ± 1 . 56 , PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be < 2 % . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.
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Affiliation(s)
- Joohi Chauhan
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
| | - Jatin Bedi
- Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
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30
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Xi L, Xiang M, Wu C, Pan Z, Dai J, Wang G, Li H, An Y, Li Y, Zhang Y, Wei X, He D, Wang Q. Adverse events after repair of tetralogy of Fallot: prediction models by machine learning of a retrospective cohort study in western China. Transl Pediatr 2023; 12:125-136. [PMID: 36891362 PMCID: PMC9986786 DOI: 10.21037/tp-22-246] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 12/30/2022] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND The incidence of clinical adverse events after tetralogy of Fallot (TOF) repair remains high. This study was performed to explore risk factors for adverse events and develop a prediction model through machine learning (ML) to forecast the incidence of clinical adverse events after TOF repair. METHODS A total of 281 participants who were treated with cardiopulmonary bypass (CPB) at our hospital from January 2002 to January 2022 were included in the study. Risk factors for adverse events were explored by composite and comprehensive analyses. Five artificial intelligence (AI) models were used for ML to build prediction models and screen out the model with the best performance in predicting adverse events. RESULTS CPB time, differential pressure of the right ventricular outflow tract (RVOTDP or DP), and transannular patch repair were identified as the main risk factors for adverse events. The reference point for CPB time was 116.5 minutes and that for right ventricular (RV) outflow tract differential pressure was 70 mmHg. SPO2 was a protective factor, with a reference point of 88%. By integrating the results for the training and validation cohorts, we confirmed that, among all models, the logistic regression (LR) model and Gaussian Naive Bayes (GNB) model were stable, showing good discrimination, calibration and clinical practicability. The dynamic nomogram can be used as a predictive tool for clinical application. CONCLUSIONS Differential pressure of the RV outflow tract, CPB time, and transannular patch repair are risk factors, and SPO2 is a protective factor for adverse events after complete TOF repair. In this study, models developed by ML were established to predict the incidence of adverse events.
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Affiliation(s)
- Linyun Xi
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Ming Xiang
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Chun Wu
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Zhengxia Pan
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Jiangtao Dai
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Gang Wang
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Hongbo Li
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yong An
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yonggang Li
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yuan Zhang
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoqin Wei
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Dawei He
- Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China.,Department of Urology, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Quan Wang
- Department of Cardiothoracic Surgery, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.,Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China
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Barry T, Farina JM, Chao CJ, Ayoub C, Jeong J, Patel BN, Banerjee I, Arsanjani R. The Role of Artificial Intelligence in Echocardiography. J Imaging 2023; 9:50. [PMID: 36826969 PMCID: PMC9962859 DOI: 10.3390/jimaging9020050] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/03/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.
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Affiliation(s)
- Timothy Barry
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Juan Maria Farina
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Chieh-Ju Chao
- Department of Cardiovascular Diseases, Mayo Clinic Rochester, Rochester, MN 55902, USA
| | - Chadi Ayoub
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Jiwoong Jeong
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
| | - Bhavik N. Patel
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Imon Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Phoenix, AZ 85004, USA
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Diseases, Mayo Clinic Arizona, Scottsdale, AZ 85054, USA
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32
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Li Y, Liu S. The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System. Bioengineering (Basel) 2023; 10:bioengineering10020194. [PMID: 36829688 PMCID: PMC9952300 DOI: 10.3390/bioengineering10020194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/15/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers' attention to the security and reliability of medical deep learning systems.
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33
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Jeong YJ, Wi YM, Park H, Lee JE, Kim SH, Lee KS. Current and Emerging Knowledge in COVID-19. Radiology 2023; 306:e222462. [PMID: 36625747 PMCID: PMC9846833 DOI: 10.1148/radiol.222462] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/11/2023]
Abstract
COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperinflammatory phases. Clinical features, imaging features, and management are different among the three phases. In the early stage, peripheral ground-glass opacities are predominant CT findings, and therapy directly targeting SARS-CoV-2 is effective. In the later stage, organizing pneumonia or diffuse alveolar damage pattern are predominant CT findings and anti-inflammatory therapies are more beneficial. The risk of severe disease or hospitalization is lower in breakthrough or Omicron variant infection compared with nonimmunized or Delta variant infections. The protection rates of the fourth dose of mRNA vaccination were 34% and 67% against overall infection and hospitalizations for severe illness, respectively. After acute COVID-19 pneumonia, most residual CT abnormalities gradually decreased in extent, but they may remain as linear or multifocal reticular or cystic lesions. Advanced insights into the pathophysiologic and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post-COVID-19 condition, or long COVID, also presents radiology with new challenges.
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Affiliation(s)
- Yeon Joo Jeong
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Yu Mi Wi
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Hyunjin Park
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Jong Eun Lee
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Si-Ho Kim
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
| | - Kyung Soo Lee
- From the Department of Radiology, Research Institute for Convergence
of Biomedical Science and Technology, Pusan National University Yangsan
Hospital, Pusan National University School of Medicine, Yangsan, Korea (Y.J.J.);
Division of Infectious Diseases, Department of Internal Medicine (Y.M.W.,
S.H.K.) and Department of Radiology (K.S.L.), Samsung Changwon Hospital,
Sungkyunkwan University School of Medicine (SKKU-SOM), Changwon 51353, Korea;
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Suwon, Korea (H.P.); Center for Neuroscience Imaging Research, Institute for
Basic Science, Suwon, Korea (H.P.); and Department of Radiology, Chonnam
National University Hospital, Gwangju, Korea (J.E.L.)
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Cavallazzi R, Bradley J, Chandler T, Furmanek S, Ramirez JA. Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019. Semin Respir Crit Care Med 2023; 44:75-90. [PMID: 36646087 DOI: 10.1055/s-0042-1759567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.
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Affiliation(s)
- Rodrigo Cavallazzi
- Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, University of Louisville, Norton Healthcare, Louisville, Kentucky
| | - James Bradley
- Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, University of Louisville, Norton Healthcare, Louisville, Kentucky
| | - Thomas Chandler
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
| | - Stephen Furmanek
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
| | - Julio A Ramirez
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
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Deep learning for computational cytology: A survey. Med Image Anal 2023; 84:102691. [PMID: 36455333 DOI: 10.1016/j.media.2022.102691] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/22/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022]
Abstract
Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.
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Schromm TM, Grosse CU. From 2D projections to the 3D rotation matrix: an attempt for finding a machine learning approach for the efficient evaluation of mechanical joining elements in X-ray computed tomography volume data. SN APPLIED SCIENCES 2023; 5:18. [PMCID: PMC9743106 DOI: 10.1007/s42452-022-05220-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/09/2022] [Indexed: 12/14/2022] Open
Abstract
Destructive and predominantly manual procedures are commonly used in the automotive industry for the testing of mechanical joints, such as rivets or screws. Combining X-ray computed tomography (CT) and machine learning (ML) bears the potential of a non-destructive and largely automated methodology. Assuming the desired result is a comprehensible and documentable evaluation, three basic steps need to be automatized: First, a joint must be detected and identified as such in a CT scan of the joined parts. Second, the detected region containing the joint is rotated to a predefined orientation. Third, key measures in cross-sections from the newly oriented joint are dimensioned and documented. This work deals only with the second step, the rotation. On the one hand, we present a methodology for creating a well-curated data set for the contextual machine learning application. On the other, we evaluate its performance on the well-known ResNet50. More concretely, we investigate if it is possible for a deep convolutional neural network (CNN) to learn the respective rotation matrix from three volume projections that are perpendicular to each other. Two scenarios are investigated: In one scenario we assume that future data that is presented to the network has similar rivet demographics to historic data. We therefore do not employ hold-out sets for the network evaluation. In the other scenario we assume the opposite and therefore evaluating the networks performance with hold-out sets. We show that from a machine learning point of view, a CNN like ResNet50 is well able to learn this relationship with acceptable accuracy. In most cases the validation loss dropped below 0.1 after only a couple of epochs. In one particular case, we even reached both mean and median errors lower than 0.2 for approximately 80% of the entire test set of 1600 examples using our methodology. From an application point of view, however, these low test set errors should be treated with caution since small deviations from the intended rotation matrix can cause volume warping and translation. In another case, in which we used a hold-out set, only a fraction of the median errors were below 0.2.
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Affiliation(s)
- T. M. Schromm
- Chair of Non-Destructive Testing, Technical University of Munich, Franz Langinger Str. 10, 81245 Munich, Germany
| | - C. U. Grosse
- Chair of Non-Destructive Testing, Technical University of Munich, Franz Langinger Str. 10, 81245 Munich, Germany
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Talebi A, Borumandnia N, Jafari R, Pourhoseingholi MA, Jafari NJ, Ashtari S, Roozpeykar S, RahimiBashar F, Karimi L, Guest PC, Jamialahmadi T, Vahedian-Azimi A, Gohari-Moghadam K, Sahebkar A. Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1412:237-250. [PMID: 37378771 DOI: 10.1007/978-3-031-28012-2_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
BACKGROUND The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans. METHODS This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. RESULTS The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively. CONCLUSIONS The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.
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Affiliation(s)
- Atefeh Talebi
- Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Nasrin Borumandnia
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ramezan Jafari
- Department of Radiology, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mohamad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nematollah Jonaidi Jafari
- Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Sara Ashtari
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Saeid Roozpeykar
- Department of Radiology, Health Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Farshid RahimiBashar
- Anesthesia and Critical Care Department, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leila Karimi
- Behavioral Sciences Research Center, LifeStyle Institute, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Paul C Guest
- Department of Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Laboratory of Translational Psychiatry, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Tannaz Jamialahmadi
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Vakilabad blvd., Mashhad, Iran
| | - Amir Vahedian-Azimi
- Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Keivan Gohari-Moghadam
- Medical ICU and Pulmonary unit, Shariati hospital, Tehran University of Medical Sciences, Tehran, Iran.
| | - Amirhossein Sahebkar
- Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran
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Ogier du Terrail J, Leopold A, Joly C, Béguier C, Andreux M, Maussion C, Schmauch B, Tramel EW, Bendjebbar E, Zaslavskiy M, Wainrib G, Milder M, Gervasoni J, Guerin J, Durand T, Livartowski A, Moutet K, Gautier C, Djafar I, Moisson AL, Marini C, Galtier M, Balazard F, Dubois R, Moreira J, Simon A, Drubay D, Lacroix-Triki M, Franchet C, Bataillon G, Heudel PE. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat Med 2023; 29:135-146. [PMID: 36658418 DOI: 10.1038/s41591-022-02155-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/23/2022] [Indexed: 01/21/2023]
Abstract
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Camille Franchet
- Institut Universitaire du Cancer de Toulouse (IUCT) Oncopole, Toulouse, France
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Perera GS, Rahman MA, Blazevski A, Wood A, Walia S, Bhaskaran M, Sriram S. Rapid Conductometric Detection of SARS-CoV-2 Proteins and Its Variants Using Molecularly Imprinted Polymer Nanoparticles. ADVANCED MATERIALS TECHNOLOGIES 2022; 8:2200965. [PMID: 36718387 PMCID: PMC9877662 DOI: 10.1002/admt.202200965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/20/2022] [Indexed: 06/18/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biosensors have captured more attention than the conventional methodologies for SARS-CoV-2 detection due to having cost-effective platforms and fast detection. However, these reported SARS-CoV-2 biosensors suffer from drawbacks including issues in detection sensitivity, degradation of biomaterials on the sensor's surface, and incapability to reuse the biosensors. To overcome these shortcomings, molecularly imprinted polymer nanoparticles (nanoMIPs) incorporated conductometric biosensor for highly accurate, rapid, and selective detection of two model SARS-CoV-2 proteins: (i) receptor binding domain (RBD) of the spike (S) glycoprotein and (ii) full length trimeric spike protein are introduced. In addition, these biosensors successfully responded to several other SARS-CoV-2 RBD spike protein variants including Alpha, Beta, Gamma, and Delta. Our conductometric biosensor selectively detects the two model proteins and SARS-CoV-2 RBD spike protein variant samples in real-time with sensitivity to a detection limit of 7 pg mL-1 within 10 min of sample incubation. A battery-free, wireless near-field communication (NFC) interface is incorporated with the biosensor for fast and contactless detection of SARS-CoV-2 variants. The smartphone enabled real-time detection and on-screen rapid result for SARS-CoV-2 variants can curve the outbreak due to its ability to alert the user to infection in real time.
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Affiliation(s)
- Ganganath S. Perera
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
| | - Md. Ataur Rahman
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
| | - April Blazevski
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
| | | | - Sumeet Walia
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
| | - Madhu Bhaskaran
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
| | - Sharath Sriram
- Functional Materials and Microsystems Research Group and the Micro Nano Research FacilityRMIT UniversityMelbourneVIC3001Australia
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Walston SL, Matsumoto T, Miki Y, Ueda D. Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study. Br J Radiol 2022; 95:20220058. [PMID: 36193755 PMCID: PMC9733620 DOI: 10.1259/bjr.20220058] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/19/2022] [Accepted: 08/23/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES The purpose of this study was to develop an artificial intelligence-based model to prognosticate COVID-19 patients at admission by combining clinical data and chest radiographs. METHODS This retrospective study used the Stony Brook University COVID-19 dataset of 1384 inpatients. After exclusions, 1356 patients were randomly divided into training (1083) and test datasets (273). We implemented three artificial intelligence models, which classified mortality, ICU admission, or ventilation risk. Each model had three submodels with different inputs: clinical data, chest radiographs, and both. We showed the importance of the variables using SHapley Additive exPlanations (SHAP) values. RESULTS The mortality prediction model was best overall with area under the curve, sensitivity, specificity, and accuracy of 0.79 (0.72-0.86), 0.74 (0.68-0.79), 0.77 (0.61-0.88), and 0.74 (0.69-0.79) for the clinical data-based model; 0.77 (0.69-0.85), 0.67 (0.61-0.73), 0.81 (0.67-0.92), 0.70 (0.64-0.75) for the image-based model, and 0.86 (0.81-0.91), 0.76 (0.70-0.81), 0.77 (0.61-0.88), 0.76 (0.70-0.81) for the mixed model. The mixed model had the best performance (p value < 0.05). The radiographs ranked fourth for prognostication overall, and first of the inpatient tests assessed. CONCLUSIONS These results suggest that prognosis models become more accurate if AI-derived chest radiograph features and clinical data are used together. ADVANCES IN KNOWLEDGE This AI model evaluates chest radiographs together with clinical data in order to classify patients as having high or low mortality risk. This work shows that chest radiographs taken at admission have significant COVID-19 prognostic information compared to clinical data other than age and sex.
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Affiliation(s)
| | | | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University,1-4-3 Asahi-machi, Abeno-ku, Osaka, Japan
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Choe J, Lee SM, Hwang HJ, Lee SM, Yun J, Kim N, Seo JB. Artificial Intelligence in Lung Imaging. Semin Respir Crit Care Med 2022; 43:946-960. [PMID: 36174647 DOI: 10.1055/s-0042-1755571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.
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Affiliation(s)
- Jooae Choe
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jihye Yun
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.,Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Galzin E, Roche L, Vlachomitrou A, Nempont O, Carolus H, Schmidt-Richberg A, Jin P, Rodrigues P, Klinder T, Richard JC, Tazarourte K, Douplat M, Sigal A, Bouscambert-Duchamp M, Si-Mohamed SA, Gouttard S, Mansuy A, Talbot F, Pialat JB, Rouvière O, Milot L, Cotton F, Douek P, Duclos A, Rabilloud M, Boussel L. Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 4:100018. [PMID: 37284031 PMCID: PMC9716289 DOI: 10.1016/j.redii.2022.100018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/15/2022] [Indexed: 12/03/2022]
Abstract
Objectives We evaluated the contribution of lung lesion quantification on chest CT using a clinical Artificial Intelligence (AI) software in predicting death and intensive care units (ICU) admission for COVID-19 patients. Methods For 349 patients with positive COVID-19-PCR test that underwent a chest CT scan at admittance or during hospitalization, we applied the AI for lung and lung lesion segmentation to obtain lesion volume (LV), and LV/Total Lung Volume (TLV) ratio. ROC analysis was used to extract the best CT criterion in predicting death and ICU admission. Two prognostic models using multivariate logistic regressions were constructed to predict each outcome and were compared using AUC values. The first model ("Clinical") was based on patients' characteristics and clinical symptoms only. The second model ("Clinical+LV/TLV") included also the best CT criterion. Results LV/TLV ratio demonstrated best performance for both outcomes; AUC of 67.8% (95% CI: 59.5 - 76.1) and 81.1% (95% CI: 75.7 - 86.5) respectively. Regarding death prediction, AUC values were 76.2% (95% CI: 69.9 - 82.6) and 79.9% (95%IC: 74.4 - 85.5) for the "Clinical" and the "Clinical+LV/TLV" models respectively, showing significant performance increase (+ 3.7%; p-value<0.001) when adding LV/TLV ratio. Similarly, for ICU admission prediction, AUC values were 74.9% (IC 95%: 69.2 - 80.6) and 84.8% (IC 95%: 80.4 - 89.2) respectively corresponding to significant performance increase (+ 10%: p-value<0.001). Conclusions Using a clinical AI software to quantify the COVID-19 lung involvement on chest CT, combined with clinical variables, allows better prediction of death and ICU admission.
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Affiliation(s)
- Eloise Galzin
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - Laurent Roche
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Olivier Nempont
- Philips France, 33 rue de Verdun, CS 60 055, Suresnes Cedex 92156, France
| | - Heike Carolus
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | | | - Peng Jin
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Pedro Rodrigues
- Philips Medical Systems Nederland BV (Philips Healthcare), the Netherlands
| | - Tobias Klinder
- Philips Research, Röntgenstrasse 24-26, Hamburg D-22335, Germany
| | - Jean-Christophe Richard
- Department of Critical Care Medicine, Hôpital De La Croix Rousse, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Karim Tazarourte
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Marion Douplat
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Alain Sigal
- Emergency department and SAMU 69, Hospices civils de Lyon, France
| | - Maude Bouscambert-Duchamp
- Laboratoire de Virologie, Institut des Agents Infectieux de Lyon, Centre National de Référence des virus respiratoires France Sud, Centre de Biologie et de Pathologie Nord, Hospices Civils de Lyon, Lyon F-69317, France
- Université de Lyon, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, Lyon F-69372, France
| | - Salim Aymeric Si-Mohamed
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | | | - Adeline Mansuy
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
| | - François Talbot
- Department of Information Technology, Hospices Civils de Lyon, Lyon, France
| | - Jean-Baptiste Pialat
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Olivier Rouvière
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - Laurent Milot
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- LabTAU INSERM U1032, Lyon, France
| | - François Cotton
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Philippe Douek
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
| | - Antoine Duclos
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
| | - Muriel Rabilloud
- Department of Biostatistics, Hospices Civils de Lyon, Lyon F-69003, France
- Université de Lyon, Lyon F-69000, France
- Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, UMR5558, Equipe Biostatistique-Santé, Villeurbanne F-69622, France
| | - Loic Boussel
- Department of Radiology, Hospices Civils de Lyon, Lyon, France
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, Lyon U1294, France
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Hussain MA, Mirikharaji Z, Momeny M, Marhamati M, Neshat AA, Garbi R, Hamarneh G. Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT. Comput Med Imaging Graph 2022; 102:102127. [PMID: 36257092 PMCID: PMC9540707 DOI: 10.1016/j.compmedimag.2022.102127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/23/2022] [Accepted: 09/28/2022] [Indexed: 01/27/2023]
Abstract
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.
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Affiliation(s)
| | - Zahra Mirikharaji
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
| | | | | | | | - Rafeef Garbi
- BiSICL, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
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Wang J, Zhou X, Hou Z, Xu X, Zhao Y, Chen S, Zhang J, Shao L, Yan R, Wang M, Ge M, Hao T, Tu Y, Huang H. Homogeneous ensemble models for predicting infection levels and
mortality of COVID-19 patients: Evidence from China. Digit Health 2022; 8:20552076221133692. [PMID: 36339905 PMCID: PMC9630904 DOI: 10.1177/20552076221133692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
Background Persistence of long-term COVID-19 pandemic is putting high pressure on
healthcare services worldwide for several years. This article aims to
establish models to predict infection levels and mortality of COVID-19
patients in China. Methods Machine learning models and deep learning models have been built based on the
clinical features of COVID-19 patients. The best models are selected by area
under the receiver operating characteristic curve (AUC) scores to construct
two homogeneous ensemble models for predicting infection levels and
mortality, respectively. The first-hand clinical data of 760 patients are
collected from Zhongnan Hospital of Wuhan University between 3 January and 8
March 2020. We preprocess data with cleaning, imputation, and
normalization. Results Our models obtain AUC = 0.7059 and Recall (Weighted avg) = 0.7248 in
predicting infection level, while AUC=0.8436 and Recall (Weighted avg) =
0.8486 in predicting mortality ratio. This study also identifies two sets of
essential clinical features. One is C-reactive protein (CRP) or high
sensitivity C-reactive protein (hs-CRP) and the other is chest tightness,
age, and pleural effusion. Conclusions Two homogeneous ensemble models are proposed to predict infection levels and
mortality of COVID-19 patients in China. New findings of clinical features
for benefiting the machine learning models are reported. The evaluation of
an actual dataset collected from January 3 to March 8, 2020 demonstrates the
effectiveness of the models by comparing them with state-of-the-art models
in prediction.
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Affiliation(s)
- Jiafeng Wang
- Department of Head, Neck and Thyroid Surgery, Zhejiang Provincial
People's Hospital and People's Hospital Affiliated to Hangzhou Medical College,
Hangzhou, China
| | - Xianlong Zhou
- Emergency Center, Zhongnan Hospital of Wuhan
University, Wuhan, China,Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan
University, Wuhan, China
| | - Zhitian Hou
- School of Computer Science, South China Normal
University, Guangzhou, China
| | - Xiaoya Xu
- School of Business Administration, Guangdong University of Finance &
Economics, Guangzhou, China
| | - Yueyue Zhao
- Department of Infectious Disease, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China,Graduate School of Clinical Medicine, Bengbu Medical College, Bengbu, China
| | - Shanshan Chen
- Department of Infectious Disease, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China,Graduate School of Clinical Medicine, Bengbu Medical College, Bengbu, China
| | - Jun Zhang
- Department of Orthopaedic Surgery, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China
| | - Lina Shao
- Department of Nephrology, Zhejiang Provincial People's Hospital and
People's Hospital Affiliated of Hangzhou Medical College, Hangzhou, China
| | - Rong Yan
- Department of Infectious Disease, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China
| | - Mingshan Wang
- Graduate School of Clinical Medicine, Bengbu Medical College, Bengbu, China
| | - Minghua Ge
- Department of Head, Neck and Thyroid Surgery, Zhejiang Provincial
People's Hospital and People's Hospital Affiliated to Hangzhou Medical College,
Hangzhou, China
| | - Tianyong Hao
- School of Computer Science, South China Normal
University, Guangzhou, China
| | - Yuexing Tu
- Department of Intensive Care Unit, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China,Yuexing Tu, Department of Intensive Unit,
Zhejiang Provincial People's Hospital and People’s Hospital Affiliated to
Hangzhou Medical College, Hangzhou, 310014, China.
| | - Haijun Huang
- Department of Infectious Disease, Zhejiang Provincial People's
Hospital and People's Hospital Affiliated to Hangzhou Medical College, Hangzhou,
China,Haijun Huang, Department of Infectious
Disease, Zhejiang Provincial People's Hospital and People’s Hospital Affiliated
to Hangzhou Medical College, Hangzhou, 310014, China.
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45
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Kohlakala A, Coetzer J, Bertels J, Vandermeulen D. Deep learning-based dental implant recognition using synthetic X-ray images. Med Biol Eng Comput 2022; 60:2951-2968. [PMID: 35978215 PMCID: PMC9385426 DOI: 10.1007/s11517-022-02642-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
Abstract A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract ![]()
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Affiliation(s)
- Aviwe Kohlakala
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Johannes Coetzer
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Jeroen Bertels
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Dirk Vandermeulen
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
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46
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Riley JM, Moeller PJ, Crawford AG, Schaefer JW, Cheney-Peters DR, Venkataraman CM, Li CJ, Smaltz CM, Bradley CG, Lee CY, Fitzpatrick DM, Ney DB, Zaret DS, Chalikonda DM, Mairose JD, Chauhan K, Szot MV, Jones RB, Bashir-Hamidu R, Mitsuhashi S, Kubey AA. External validation of the COVID-19 4C mortality score in an urban United States cohort. Am J Med Sci 2022; 364:409-413. [PMID: 35500663 PMCID: PMC9054702 DOI: 10.1016/j.amjms.2022.04.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 03/07/2022] [Accepted: 04/22/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND Identifying patients at risk for mortality from COVID-19 is crucial to triage, clinical decision-making, and the allocation of scarce hospital resources. The 4C Mortality Score effectively predicts COVID-19 mortality, but it has not been validated in a United States (U.S.) population. The purpose of this study is to determine whether the 4C Mortality Score accurately predicts COVID-19 mortality in an urban U.S. adult inpatient population. METHODS This retrospective cohort study included adult patients admitted to a single-center, tertiary care hospital (Philadelphia, PA) with a positive SARS-CoV-2 PCR from 3/01/2020 to 6/06/2020. Variables were extracted through a combination of automated export and manual chart review. The outcome of interest was mortality during hospital admission or within 30 days of discharge. RESULTS This study included 426 patients; mean age was 64.4 years, 43.4% were female, and 54.5% self-identified as Black or African American. All-cause mortality was observed in 71 patients (16.7%). The area under the receiver operator characteristic curve of the 4C Mortality Score was 0.85 (95% confidence interval, 0.79-0.89). CONCLUSIONS Clinicians may use the 4C Mortality Score in an urban, majority Black, U.S. inpatient population. The derivation and validation cohorts were treated in the pre-vaccine era so the 4C Score may over-predict mortality in current patient populations. With stubbornly high inpatient mortality rates, however, the 4C Score remains one of the best tools available to date to inform thoughtful triage and treatment allocation.
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Affiliation(s)
- Joshua M. Riley
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Patrick J. Moeller
- Jefferson College of Population Health, Thomas Jefferson University, Philadelphia, PA, USA
| | - Albert G. Crawford
- Jefferson College of Population Health, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joseph W. Schaefer
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Dianna R. Cheney-Peters
- Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Chantel M. Venkataraman
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Chris J. Li
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Christa M. Smaltz
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Conor G. Bradley
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Crystal Y. Lee
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Danielle M. Fitzpatrick
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - David B. Ney
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Dina S. Zaret
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Divya M. Chalikonda
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Joshua D. Mairose
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Kashyap Chauhan
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Margaret V. Szot
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Robert B. Jones
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Rukaiya Bashir-Hamidu
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Shuji Mitsuhashi
- Internal Medicine Residency, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Alan A. Kubey
- Division of Hospital Medicine, Department of Medicine, Thomas Jefferson University Hospital, Philadelphia, PA, USA,Division of Hospital Internal Medicine, Department of Internal Medicine, Rochester, MN, USA,Corresponding author at: Alan A. Kubey, MD, Division of Hospital Medicine, Thomas Jefferson University Hospital, 833 Chestnut Street, Suite 701, Philadelphia, PA 19107, USA
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Leong YX, Tan EX, Leong SX, Lin Koh CS, Thanh Nguyen LB, Ting Chen JR, Xia K, Ling XY. Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X. ACS NANO 2022; 16:13279-13293. [PMID: 36067337 DOI: 10.1021/acsnano.2c05731] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.
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Affiliation(s)
- Yong Xiang Leong
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Emily Xi Tan
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Shi Xuan Leong
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Charlynn Sher Lin Koh
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Lam Bang Thanh Nguyen
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Jaslyn Ru Ting Chen
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Xing Yi Ling
- Division of Chemistry and Biological Chemistry, School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore 637371, Singapore
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48
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Costa YMG, Silva SA, Teixeira LO, Pereira RM, Bertolini D, Britto AS, Oliveira LS, Cavalcanti GDC. COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers. SENSORS (BASEL, SWITZERLAND) 2022; 22:7303. [PMID: 36236402 PMCID: PMC9570662 DOI: 10.3390/s22197303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.
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Affiliation(s)
- Yandre M. G. Costa
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Sergio A. Silva
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | - Lucas O. Teixeira
- Departamento de Informática, Universidade Estadual de Maringá, Maringá 87020-900, Brazil
| | | | - Diego Bertolini
- Departamento Acadêmico de Ciência da Computação, Universidade Tecnológica Federal do Paraná, Campo Mourão 87301-899, Brazil
| | - Alceu S. Britto
- Departmento de Ciência da Computação, Pontifícia Universidade Católica do Paraná, Curitiba 80215-901, Brazil
| | - Luiz S. Oliveira
- Departamento de Informática, Universidade Federal do Paraná, Curitiba 81531-980, Brazil
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Guerrero C, Mazuelas S. Female Models in AI and the Fight Against COVID-19. F1000Res 2022; 11:1037. [PMID: 39296496 PMCID: PMC11409909 DOI: 10.12688/f1000research.123599.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/02/2022] [Indexed: 09/21/2024] Open
Abstract
Gender imbalance has persisted over time and is well documented in the fields of science, technology, engineering and mathematics (STEM) and singularly in artificial intelligence (AI). In this article we emphasize the importance of increasing the visibility and recognition of women researchers to attract and retain women in the AI field. We review the ratio of women in STEM and AI, its evolution through time, and the differences among disciplines. Then, we discuss the main sources of this gender imbalance highlighting the lack of female role models and the problems which may arise; such as the so called Marie Curie complex, suvivorship bias, and impostor syndrome. We also emphasize the importance of active participation of women researchers in conferences, providing statistics corresponding with the leading conferences. Finally, to support these views, we give examples of several prestigious female researchers in the field and we review their research work related to COVID-19 displayed in the workshop "Artificial Intelligence for the Fight Against COVID-19" (AI4FA COVID-19), which is an example of a more balanced participation between genders.
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50
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MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches. Bioengineering (Basel) 2022; 9:bioengineering9090457. [PMID: 36135003 PMCID: PMC9495842 DOI: 10.3390/bioengineering9090457] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/02/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022] Open
Abstract
Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.
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