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Küstner T, Qin C, Sun C, Ning L, Scannell CM. The intelligent imaging revolution: artificial intelligence in MRI and MRS acquisition and reconstruction. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01179-2. [PMID: 38900344 DOI: 10.1007/s10334-024-01179-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.Lab), Diagnostic and Interventional Radiology, University Hospital of Tuebingen, 72076, Tuebingen, Germany.
| | - Chen Qin
- Department of Electrical and Electronic Engineering, I-X Imperial College London, London, UK
| | - Changyu Sun
- Department of Chemical and Biomedical Engineering, Department of Radiology, University of Missouri-Columbia, 65201, Columbia, USA
| | - Lipeng Ning
- Brigham and Women' s Hospital, 02215, Boston, USA
| | - Cian M Scannell
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Schropp L, Sørensen APS, Devlin H, Matzen LH. Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs. EUROPEAN JOURNAL OF DENTAL EDUCATION : OFFICIAL JOURNAL OF THE ASSOCIATION FOR DENTAL EDUCATION IN EUROPE 2024; 28:490-496. [PMID: 37961027 DOI: 10.1111/eje.12973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/14/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection. MATERIALS AND METHODS The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration. RESULTS At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups. CONCLUSION Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.
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Affiliation(s)
- Lars Schropp
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
| | - Anders Peter Sejersdal Sørensen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
- Private practice, Tandlægerne Sydcentret, Kolding, Denmark
| | - Hugh Devlin
- Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, UK
| | - Louise Hauge Matzen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
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Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [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: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.
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Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
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Tucci F, Laurinavicius A, Kather JN, Eloy C. The digital revolution in pathology: Towards a smarter approach to research and treatment. TUMORI JOURNAL 2024:3008916241231035. [PMID: 38606831 DOI: 10.1177/03008916241231035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.
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Affiliation(s)
- Francesco Tucci
- School of Pathology, University of Milan, Milan, Italy
- European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Arvydas Laurinavicius
- Department of Pathology, Forensic Medicine and Pharmacology, Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
- National Centre of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Catarina Eloy
- Ipatimup - Institute of Molecular Pathology and Immunology of University of Porto, Porto, Portugal
- Medical Faculty, University of Porto, Porto, Portugal
- i3S-Instituto de Investigação e Inovação em Saúde, Porto, Portugal
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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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Pappot H, Björnsson BP, Krause O, Bæksted C, Bidstrup PE, Dalton SO, Johansen C, Knoop A, Vogelius I, Holländer-Mieritz C. Machine learning applied in patient-reported outcome research-exploring symptoms in adjuvant treatment of breast cancer. Breast Cancer 2024; 31:148-153. [PMID: 37940813 DOI: 10.1007/s12282-023-01515-9] [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: 03/01/2023] [Accepted: 10/15/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Patient-reported outcome (PRO) data may help us better understand the life of breast cancer patients. We have previously collected PRO data in a national Danish breast cancer study in patients undergoing adjuvant chemotherapy. The aim of the present post-hoc explorative study is to apply Machine Learning (ML) algorithms using permutation importance to explore how specific PRO symptoms influence nonadherence to six cycles of planned adjuvant chemotherapy in breast cancer patients. METHODS We here investigate ePRO-data from the 347 patients. The ePRO presented 42 PROCTCAE questions on 25 symptoms. Patients completed the ePRO before each cycle of chemotherapy. Number of patients with completion of the scheduled six cycles of chemotherapy were registered. Two ML models were applied. One aimed at discovering the individual relative importance of the different questions in the dataset while the second aimed at discovering the relationships between the questions. Permutation importance was used. RESULTS Out of 347 patients 238 patients remained in the final dataset, 15 patients dropped out. Two symptoms: aching joints and numbness/tingling, were the most important for dropout in the final dataset, each with an importance value of about 0.04. Model's average ROC-AUC-score being 0.706. In the second model a low performance score made the results very unreliable. CONCLUSION In conclusion, this explorative data analysis using ML methodologies in an ePRO dataset from a population of women with breast cancer treated with adjuvant chemotherapy unravels that the symptoms aching joints and numbness/tingling could be important for drop out of planned adjuvant chemotherapy.
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Affiliation(s)
- Helle Pappot
- Department of Oncology, Rigshospitalet Section 5073, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Benóný P Björnsson
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Oswin Krause
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | | | - Pernille E Bidstrup
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Institute of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Susanne O Dalton
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Christoffer Johansen
- Department of Oncology, Rigshospitalet Section 5073, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Ann Knoop
- Department of Oncology, Rigshospitalet Section 5073, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Ivan Vogelius
- Department of Oncology, Rigshospitalet Section 5073, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Cecilie Holländer-Mieritz
- Department of Oncology, Rigshospitalet Section 5073, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Wang Y, Liu L, Wang C. Trends in using deep learning algorithms in biomedical prediction systems. Front Neurosci 2023; 17:1256351. [PMID: 38027475 PMCID: PMC10665494 DOI: 10.3389/fnins.2023.1256351] [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: 07/10/2023] [Accepted: 09/25/2023] [Indexed: 12/01/2023] Open
Abstract
In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.
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Affiliation(s)
- Yanbu Wang
- School of Strength and Conditioning, Beijing Sport University, Beijing, China
| | - Linqing Liu
- Department of Physical Education, Peking University, Beijing, China
| | - Chao Wang
- Institute of Competitive Sports, Beijing Sport University, Beijing, China
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SegPC-2021: A challenge & dataset on segmentation of Multiple Myeloma plasma cells from microscopic images. Med Image Anal 2023; 83:102677. [PMID: 36403309 DOI: 10.1016/j.media.2022.102677] [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: 01/06/2022] [Revised: 08/25/2022] [Accepted: 10/27/2022] [Indexed: 11/05/2022]
Abstract
Multiple Myeloma (MM) is an emerging ailment of global concern. Its diagnosis at the early stages is critical for recovery. Therefore, efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Following the trend, a medical imaging challenge on "Segmentation of Multiple Myeloma Plasma Cells in Microscopic Images (SegPC-2021)" was organized at the IEEE International Symposium on Biomedical Imaging (ISBI), 2021, France. The challenge addressed the problem of cell segmentation in microscopic images captured from the slides prepared from the bone marrow aspirate of patients diagnosed with Multiple Myeloma. The challenge released a total of 775 images with 690 and 85 images of sizes 2040×1536 and 1920×2560 pixels, respectively, captured from two different (microscope and camera) setups. The participants had to segment the plasma cells with a separate label on each cell's nucleus and cytoplasm. This problem comprises many challenges, including a reduced color contrast between the cytoplasm and the background, and the clustering of cells with a feeble boundary separation of individual cells. To our knowledge, the SegPC-2021 challenge dataset is the largest publicly available annotated data on plasma cell segmentation in MM so far. The challenge targets a semi-automated tool to ensure the supervision of medical experts. It was conducted for a span of five months, from November 2020 to April 2021. Initially, the data was shared with 696 people from 52 teams, of which 41 teams submitted the results of their models on the evaluation portal in the validation phase. Similarly, 20 teams qualified for the last round, of which 16 teams submitted the results in the final test phase. All the top-5 teams employed DL-based approaches, and the best mIoU obtained on the final test set of 277 microscopic images was 0.9389. All these five models have been analyzed and discussed in detail. This challenge task is a step towards the target of creating an automated MM diagnostic tool.
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Zhang J, Liu F, Xu J, Zhao Q, Huang C, Yu Y, Yuan H. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. Front Endocrinol (Lausanne) 2023; 14:1132725. [PMID: 37051194 PMCID: PMC10083489 DOI: 10.3389/fendo.2023.1132725] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/14/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Acute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists. PURPOSE To design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography. MATERIALS AND METHODS The CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types. RESULTS The mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4. CONCLUSION The multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.
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Affiliation(s)
- Jianlun Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Qingqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | | | | | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
- *Correspondence: Huishu Yuan,
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Intelligent oncology: The convergence of artificial intelligence and oncology. JOURNAL OF THE NATIONAL CANCER CENTER 2022. [DOI: 10.1016/j.jncc.2022.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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Ben Hamida A, Devanne M, Weber J, Truntzer C, Derangère V, Ghiringhelli F, Forestier G, Wemmert C. Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation. Artif Intell Med 2022; 133:102407. [PMID: 36328667 DOI: 10.1016/j.artmed.2022.102407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 02/08/2023]
Abstract
Recently, Artificial Intelligence namely Deep Learning methods have revolutionized a wide range of domains and applications. Besides, Digital Pathology has so far played a major role in the diagnosis and the prognosis of tumors. However, the characteristics of the Whole Slide Images namely the gigapixel size, high resolution and the shortage of richly labeled samples have hindered the efficiency of classical Machine Learning methods. That goes without saying that traditional methods are poor in generalization to different tasks and data contents. Regarding the success of Deep learning when dealing with Large Scale applications, we have resorted to the use of such models for histopathological image segmentation tasks. First, we review and compare the classical UNet and Att-UNet models for colon cancer WSI segmentation in a sparsely annotated data scenario. Then, we introduce novel enhanced models of the Att-UNet where different schemes are proposed for the skip connections and spatial attention gates positions in the network. In fact, spatial attention gates assist the training process and enable the model to avoid irrelevant feature learning. Alternating the presence of such modules namely in our Alter-AttUNet model adds robustness and ensures better image segmentation results. In order to cope with the lack of richly annotated data in our AiCOLO colon cancer dataset, we suggest the use of a multi-step training strategy that also deals with the WSI sparse annotations and unbalanced class issues. All proposed methods outperform state-of-the-art approaches but Alter-AttUNet generates the best compromise between accurate results and light network. The model achieves 95.88% accuracy with our sparse AiCOLO colon cancer datasets. Finally, to evaluate and validate our proposed architectures we resort to publicly available WSI data: the NCT-CRC-HE-100K, the CRC-5000 and the Warwick colon cancer histopathological dataset. Respective accuracies of 99.65%, 99.73% and 79.03% were reached. A comparison with state-of-art approaches is established to view and compare the key solutions for histopathological image segmentation.
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Affiliation(s)
| | - M Devanne
- IRIMAS, University of Haute-Alsace, France
| | - J Weber
- IRIMAS, University of Haute-Alsace, France
| | - C Truntzer
- Platform of Transform in Biological Oncology, Dijon, France
| | - V Derangère
- Platform of Transform in Biological Oncology, Dijon, France
| | - F Ghiringhelli
- Platform of Transform in Biological Oncology, Dijon, France
| | | | - C Wemmert
- ICube, University of Strasbourg, France
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HFANet: hierarchical feature fusion attention network for classification of glomerular immunofluorescence images. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07676-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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13
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Jaen-Lorites JM, Perez-Pelegri M, Laparra V, Lopez-Lereu MP, Monmeneu JV, Maceira AM, Moratal D. Synthetic Generation of Cardiac MR Images Combining Convolutional Variational Autoencoders and Style Transfer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2084-2087. [PMID: 36086174 DOI: 10.1109/embc48229.2022.9871135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The number of studies in the medical field that uses machine learning and deep learning techniques has been increasing in the last years. However, these techniques require a huge amount of data that can be difficult and expensive to obtain. This specially happens with cardiac magnetic resonance (MR) images. One solution to the problem is raise the dataset size by generating synthetic data. Convolutional Variational Autoencoder (CVAe) is a deep learning technique which allows to generate synthetic images, but sometimes the synthetic images can be slightly blurred. We propose the combination of the CVAe technique combined with Style Transfer technique to generate synthetic realistic cardiac MR images. Clinical Relevance-The current work presents a tool to increase in a simple easy and fast way the cardiac magnetic resonance images dataset with which perform machine learning and deep learning studies.
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Mouazer A, Tsopra R, Sedki K, Letord C, Lamy JB. Decision-support systems for managing polypharmacy in the elderly: A scoping review. J Biomed Inform 2022; 130:104074. [PMID: 35470079 DOI: 10.1016/j.jbi.2022.104074] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 10/18/2022]
Abstract
Polypharmacy, the consuming of more than five drugs, is a public health problem. It can lead to many interactions and adverse drug reactions and is very expensive. Therapeutic guidelines for managing polypharmacy in the elderly have been issued, but are highly complex, limiting their use. Decision-support systems have therefore been developed to automate the execution of these guidelines, or to provide information about drugs adapted to the context of polypharmacy. These systems differ widely in terms of their technical design, knowledge sources and evaluation methods. We present here a scoping review of electronic systems for supporting the management, by healthcare providers, of polypharmacy in elderly patients. Most existing reviews have focused mainly on evaluation results, whereas the present review also describes the technical design of these systems and the methodologies for developing and evaluating them. A systematic bibliographic search identified 19 systems differing considerably in terms of their technical design (rule-based systems, documentary approach, mixed); outputs (textual report, alerts and/or visual approaches); and evaluations (impact on clinical practices, impact on patient outcomes, efficiency and/or user satisfaction). The evaluations performed are minimal (among all the systems identified, only one system has been evaluated according to all the criteria mentioned above) and no machine learning systems and/or conflict management systems were retrieved. This review highlights the need to develop new methodologies, combining various approaches for decision support system in polypharmacy.
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Affiliation(s)
- Abdelmalek Mouazer
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France.
| | - Rosy Tsopra
- INSERM, Université de Paris, Sorbonne Université, Centre de Recherche des Cordeliers, F-75006 Paris, France; INRIA, HeKA, INRIA Paris, France; Department of Medical Informatics, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France
| | - Karima Sedki
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France
| | - Catherine Letord
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France; Department of Biomedical Informatics, Rouen University Hospital, Normandy, France
| | - Jean-Baptiste Lamy
- Université Sorbonne Paris Nord, LIMICS, Sorbonne Université, INSERM, F-93000 Bobigny, France
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15
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Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images. SENSORS 2022; 22:s22072559. [PMID: 35408173 PMCID: PMC9002763 DOI: 10.3390/s22072559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 01/03/2023]
Abstract
In recent years, the use of deep learning-based models for developing advanced healthcare systems has been growing due to the results they can achieve. However, the majority of the proposed deep learning-models largely use convolutional and pooling operations, causing a loss in valuable data and focusing on local information. In this paper, we propose a deep learning-based approach that uses global and local features which are of importance in the medical image segmentation process. In order to train the architecture, we used extracted three-dimensional (3D) blocks from the full magnetic resonance image resolution, which were sent through a set of successive convolutional neural network (CNN) layers free of pooling operations to extract local information. Later, we sent the resulting feature maps to successive layers of self-attention modules to obtain the global context, whose output was later dispatched to the decoder pipeline composed mostly of upsampling layers. The model was trained using the Mindboggle-101 dataset. The experimental results showed that the self-attention modules allow segmentation with a higher Mean Dice Score of 0.90 ± 0.036 compared with other UNet-based approaches. The average segmentation time was approximately 0.038 s per brain structure. The proposed model allows tackling the brain structure segmentation task properly. Exploiting the global context that the self-attention modules incorporate allows for more precise and faster segmentation. We segmented 37 brain structures and, to the best of our knowledge, it is the largest number of structures under a 3D approach using attention mechanisms.
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16
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Luo X, Zhang J, Li Z, Yang R. Diagnosis of ulcerative colitis from endoscopic images based on deep learning. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103443] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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17
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Chang CW, Christian M, Chang DH, Lai F, Liu TJ, Chen YS, Chen WJ. Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis. PLoS One 2022; 17:e0264139. [PMID: 35176101 PMCID: PMC8853507 DOI: 10.1371/journal.pone.0264139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 02/03/2022] [Indexed: 01/14/2023] Open
Abstract
A pressure ulcer is an injury of the skin and underlying tissues adjacent to a bony eminence. Patients who suffer from this disease may have difficulty accessing medical care. Recently, the COVID-19 pandemic has exacerbated this situation. Automatic diagnosis based on machine learning (ML) brings promising solutions. Traditional ML requires complicated preprocessing steps for feature extraction. Its clinical applications are thus limited to particular datasets. Deep learning (DL), which extracts features from convolution layers, can embrace larger datasets that might be deliberately excluded in traditional algorithms. However, DL requires large sets of domain specific labeled data for training. Labeling various tissues of pressure ulcers is a challenge even for experienced plastic surgeons. We propose a superpixel-assisted, region-based method of labeling images for tissue classification. The boundary-based method is applied to create a dataset for wound and re-epithelialization (re-ep) segmentation. Five popular DL models (U-Net, DeeplabV3, PsPNet, FPN, and Mask R-CNN) with encoder (ResNet-101) were trained on the two datasets. A total of 2836 images of pressure ulcers were labeled for tissue classification, while 2893 images were labeled for wound and re-ep segmentation. All five models had satisfactory results. DeeplabV3 had the best performance on both tasks with a precision of 0.9915, recall of 0.9915 and accuracy of 0.9957 on the tissue classification; and a precision of 0.9888, recall of 0.9887 and accuracy of 0.9925 on the wound and re-ep segmentation task. Combining segmentation results with clinical data, our algorithm can detect the signs of wound healing, monitor the progress of healing, estimate the wound size, and suggest the need for surgical debridement.
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Affiliation(s)
- Che Wei Chang
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
- * E-mail:
| | - Mesakh Christian
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Dun Hao Chang
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
- Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Tom J. Liu
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan
- Division of Plastic Surgery, Department of Surgery, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Yo Shen Chen
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Wei Jen Chen
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan
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18
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Shu Z, Jia T, Tian H, Yan D, Yang Y, Zhou X. AIM in Alternative Medicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_57] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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19
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Luca AR, Ursuleanu TF, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Grigorovici A. Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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20
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Ursuleanu TF, Luca AR, Gheorghe L, Grigorovici R, Iancu S, Hlusneac M, Preda C, Grigorovici A. Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images. Diagnostics (Basel) 2021; 11:1373. [PMID: 34441307 PMCID: PMC8393354 DOI: 10.3390/diagnostics11081373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 07/25/2021] [Accepted: 07/27/2021] [Indexed: 12/13/2022] Open
Abstract
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their "key" features, for completion of tasks in current applications in the interpretation of medical images. The use of "key" characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.
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Affiliation(s)
- Tudor Florin Ursuleanu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
- Department of Surgery I, Regional Institute of Oncology, 700483 Iasi, Romania
| | - Andreea Roxana Luca
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department Obstetrics and Gynecology, Integrated Ambulatory of Hospital “Sf. Spiridon”, 700106 Iasi, Romania
| | - Liliana Gheorghe
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Radiology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Roxana Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Stefan Iancu
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Maria Hlusneac
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
| | - Cristina Preda
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Endocrinology, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
| | - Alexandru Grigorovici
- Faculty of General Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania; (T.F.U.); (R.G.); (S.I.); (M.H.); (C.P.); (A.G.)
- Department of Surgery VI, “Sf. Spiridon” Hospital, 700111 Iasi, Romania
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21
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AIM in Alternative Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_57-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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