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Zhang G, Gao Q, Zhan Q, Wang L, Song B, Chen Y, Bian Y, Ma C, Lu J, Shao C. Label-free differentiation of pancreatic pathologies from normal pancreas utilizing end-to-end three-dimensional multimodal networks on CT. Clin Radiol 2024; 79:e1159-e1166. [PMID: 38969545 DOI: 10.1016/j.crad.2024.06.006] [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: 12/22/2023] [Revised: 05/10/2024] [Accepted: 06/05/2024] [Indexed: 07/07/2024]
Abstract
AIMS To investigate the utilization of an end-to-end multimodal convolutional model in the rapid and accurate diagnosis of pancreatic diseases using abdominal CT images. MATERIALS AND METHODS In this study, a novel lightweight label-free end-to-end multimodal network (eeMulNet) model was proposed for the rapid and precise diagnosis of abnormal pancreas. The eeMulNet consists of two steps: pancreatic region localization and multimodal CT diagnosis integrating textual and image data. A research dataset comprising 715 CT scans with various types of pancreas diseases and 228 CT scans from a control group was collected. The training set and independent test set for the multimodal classification network were randomly divided in an 8:2 ratio (755 for training and 188 for testing). RESULTS The eeMulNet model demonstrated outstanding performance on an independent test set of 188 CT scans (Normal: 45, Abnormal: 143), with an area under the curve (AUC) of 1.0, accuracy of 100%, and sensitivity of 100%. The average testing duration per patient was 41.04 seconds, while the classification network took only 0.04 seconds. CONCLUSIONS The proposed eeMulNet model offers a promising approach for the diagnosis of pancreatic diseases. It can support the identification of suspicious cases during daily radiology work and enhance the accuracy of pancreatic disease diagnosis. The codes and models of eeMulNet are publicly available at Rudeguy1/eeMulNet (github.com).
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Affiliation(s)
- G Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Q Gao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Q Zhan
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - L Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - B Song
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - Y Chen
- College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - Y Bian
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - C Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China; College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China.
| | - J Lu
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
| | - C Shao
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai 200433, China.
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Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, Alamoodi AH, Albahri OS, Hasan AF, Bai J, Gilliland L, Peng J, Branni M, Shuker T, Cutbush K, Santamaría J, Moreira C, Ouyang C, Duan Y, Manoufali M, Jomaa M, Gupta A, Abbosh A, Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artif Intell Med 2024; 155:102935. [PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935] [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: 06/01/2023] [Revised: 03/18/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia.
| | - Khamael Al-Dulaimi
- Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Asma Salhi
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Mohammed A Fadhel
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - A S Albahri
- Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - A H Alamoodi
- Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - O S Albahri
- Australian Technical and Management College, Melbourne, Australia
| | - Amjad F Hasan
- Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Luke Gilliland
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Jing Peng
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Marco Branni
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Tristan Shuker
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Kenneth Cutbush
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén 23071, Spain
| | - Catarina Moreira
- Data Science Institute, University of Technology Sydney, Australia
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Ye Duan
- School of Computing, Clemson University, Clemson, 29631, SC, USA
| | - Mohamed Manoufali
- CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Mohammad Jomaa
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Ashish Gupta
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Du T, Sun Y, Wang X, Jiang T, Xu N, Boukhers Z, Grzegorzek M, Sun H, Li C. A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients. Front Med (Lausanne) 2024; 11:1444708. [PMID: 39188873 PMCID: PMC11345710 DOI: 10.3389/fmed.2024.1444708] [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: 06/06/2024] [Accepted: 07/05/2024] [Indexed: 08/28/2024] Open
Abstract
Background Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. Method This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. Result The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Conclusion Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.
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Affiliation(s)
- Tianming Du
- College of Medicine and Biological information Engineering, Northeastern University, Shenyang, China
| | - Yihao Sun
- College of Medicine and Biological information Engineering, Northeastern University, Shenyang, China
| | - Xinghao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tao Jiang
- Institute of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ning Xu
- School of Arts and Design, Liaoning Petrochemical University, Fushun, Liaoning, China
| | - Zeyd Boukhers
- Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany
| | - Marcin Grzegorzek
- Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
- German Research Center for Artificial Intelligence, Lübeck, Germany
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological information Engineering, Northeastern University, Shenyang, China
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4
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Siddiqi R, Javaid S. Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey. J Imaging 2024; 10:176. [PMID: 39194965 DOI: 10.3390/jimaging10080176] [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: 06/11/2024] [Revised: 07/15/2024] [Accepted: 07/19/2024] [Indexed: 08/29/2024] Open
Abstract
This paper addresses the significant problem of identifying the relevant background and contextual literature related to deep learning (DL) as an evolving technology in order to provide a comprehensive analysis of the application of DL to the specific problem of pneumonia detection via chest X-ray (CXR) imaging, which is the most common and cost-effective imaging technique available worldwide for pneumonia diagnosis. This paper in particular addresses the key period associated with COVID-19, 2020-2023, to explain, analyze, and systematically evaluate the limitations of approaches and determine their relative levels of effectiveness. The context in which DL is applied as both an aid to and an automated substitute for existing expert radiography professionals, who often have limited availability, is elaborated in detail. The rationale for the undertaken research is provided, along with a justification of the resources adopted and their relevance. This explanatory text and the subsequent analyses are intended to provide sufficient detail of the problem being addressed, existing solutions, and the limitations of these, ranging in detail from the specific to the more general. Indeed, our analysis and evaluation agree with the generally held view that the use of transformers, specifically, vision transformers (ViTs), is the most promising technique for obtaining further effective results in the area of pneumonia detection using CXR images. However, ViTs require extensive further research to address several limitations, specifically the following: biased CXR datasets, data and code availability, the ease with which a model can be explained, systematic methods of accurate model comparison, the notion of class imbalance in CXR datasets, and the possibility of adversarial attacks, the latter of which remains an area of fundamental research.
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Affiliation(s)
- Raheel Siddiqi
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
| | - Sameena Javaid
- Computer Science Department, Karachi Campus, Bahria University, Karachi 73500, Pakistan
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Lourenço AA, Amaral PHR, Paim AAO, Marques GF, Gomes-de-Pontes L, da Mata CPSM, da Fonseca FG, Pérez JCG, Coelho-dos-Reis JGA. Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data. Front Public Health 2024; 12:1347334. [PMID: 38807995 PMCID: PMC11130428 DOI: 10.3389/fpubh.2024.1347334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/30/2024] [Indexed: 05/30/2024] Open
Abstract
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.
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Affiliation(s)
- Alice Aparecida Lourenço
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Adriana Alves Oliveira Paim
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Geovane Ferreira Marques
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leticia Gomes-de-Pontes
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Flávio Guimarães da Fonseca
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- CT Vacinas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Juan Carlos González Pérez
- Departamento de Física, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Jordana Grazziela Alves Coelho-dos-Reis
- Laboratório de Virologia Básica e Aplicada, Instituto de Ciências Biológicas, Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Kashif M, Acharya S, Khalil A. Molecular Interactions Leading to Advancements in the Techniques for COVID-19 Detection: A Review. J AOAC Int 2024; 107:519-528. [PMID: 38310327 DOI: 10.1093/jaoacint/qsae010] [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: 11/11/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 02/05/2024]
Abstract
Since 2019 the world has been in a combat with the highly contagious disease COVID-19 which is caused by the rapid transmission of the SARS-CoV-2 virus (severe acute respiratory syndrome coronavirus 2). Detection of this disease in an early stage helps to control its spread and management. To combat this epidemic with one-time effective medication, improved quick analytical procedures must be developed and validated. The requirement for accurate and precise analytical methods for the diagnosis of the virus and antibodies in infected patients has been a matter of concern. The global impact of this virus has motivated scientists and researchers to investigate and develop various analytical diagnostic techniques. This review includes the study of standard methods which are reliable and accredited for the analytical recognition of the said virus. For early detection of SARS-CoV-2 RNA, RT-PCR (Real-time reverse transcriptase-polymerase chain reaction) is an accurate method among other methods and, thus, considered as the "gold standard" technique. Here, we outline the most extensively used analytical methods for diagnosing COVID-19, along with a brief description of each technique and its analytical aspects/perspective.
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Affiliation(s)
- Mohammad Kashif
- Aligarh Muslim University, Analytical Chemistry Section, Department of Chemistry, Aligarh, Uttar Pradesh 202002, India
| | - Swati Acharya
- Aligarh Muslim University, Analytical Chemistry Section, Department of Chemistry, Aligarh, Uttar Pradesh 202002, India
| | - Adila Khalil
- Aligarh Muslim University, Analytical Chemistry Section, Department of Chemistry, Aligarh, Uttar Pradesh 202002, India
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Malik H, Anees T. Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS One 2024; 19:e0296352. [PMID: 38470893 DOI: 10.1371/journal.pone.0296352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 03/14/2024] Open
Abstract
Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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Chavoshi M, Zamani S, Mirshahvalad SA. Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis. Clin Imaging 2024; 107:110092. [PMID: 38301371 DOI: 10.1016/j.clinimag.2024.110092] [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: 10/13/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/03/2024]
Abstract
PURPOSE Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia on CT of the chest, these results have not been collectively evaluated. We performed a meta-analysis of original articles comparing the performance of DL models versus radiologists in detecting COVID-19 pneumonia. METHODS A systematic search was conducted on the three main medical literature databases, Scopus, Web of Science, and PubMed, for articles published as of February 1st, 2023. We included original scientific articles that compared DL models trained to detect COVID-19 pneumonia on CT to radiologists. Meta-analysis was performed to determine DL versus radiologist performance in terms of model sensitivity and specificity, taking into account inter and intra-study heterogeneity. RESULTS Twenty-two articles met the inclusion criteria. Based on the meta-analytic calculations, DL models had significantly higher pooled sensitivity (0.933 vs. 0.829, p < 0.001) compared to radiologists with similar pooled specificity (0.905 vs. 0.897, p = 0.746). In the differentiation of COVID-19 versus community-acquired pneumonia, the DL models had significantly higher sensitivity compared to radiologists (0.915 vs. 0.836, p = 0.001). CONCLUSIONS DL models have high performance for screening of COVID-19 pneumonia on chest CT, offering the possibility of these models for augmenting radiologists in clinical practice.
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Affiliation(s)
- Mohammadreza Chavoshi
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sara Zamani
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Seyed Ali Mirshahvalad
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada.
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9
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Bumm R, Zaffino P, Lasso A, Estépar RSJ, Pieper S, Wasserthal J, Spadea MF, Latshang T, Kawel-Boehm N, Wäckerlin A, Werner R, Hässig G, Furrer M, Kikinis R. Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients. J Thorac Dis 2024; 16:1009-1020. [PMID: 38505008 PMCID: PMC10944742 DOI: 10.21037/jtd-23-1150] [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: 07/25/2023] [Accepted: 12/15/2023] [Indexed: 03/21/2024]
Abstract
Background The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments. Methods A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using CT scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analysed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (P<0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (P<0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and the rating by radiological experts. Conclusions The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
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Affiliation(s)
- Rudolf Bumm
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, University “Magna Graecia” of Catanzaro, Catanzaro, Italy
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen’s University, Kingston, Canada
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jakob Wasserthal
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Maria Francesca Spadea
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Tsogyal Latshang
- Department of Pneumonology, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Nadine Kawel-Boehm
- Department of Radiology, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Adrian Wäckerlin
- Department of Intensive Care Medicine, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Raphael Werner
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Gabriela Hässig
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Markus Furrer
- Department of Thoracic Surgery, Cantonal Hospital of Graubünden, Chur, Switzerland
| | - Ron Kikinis
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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10
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Zolya MA, Baltag C, Bratu DV, Coman S, Moraru SA. COVID-19 Detection and Diagnosis Model on CT Scans Based on AI Techniques. Bioengineering (Basel) 2024; 11:79. [PMID: 38247956 PMCID: PMC10813639 DOI: 10.3390/bioengineering11010079] [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: 12/12/2023] [Revised: 12/31/2023] [Accepted: 01/08/2024] [Indexed: 01/23/2024] Open
Abstract
The end of 2019 could be mounted in a rudimentary framing of a new medical problem, which globally introduces into the discussion a fulminant outbreak of coronavirus, consequently spreading COVID-19 that conducted long-lived and persistent repercussions. Hence, the theme proposed to be solved arises from the field of medical imaging, where a pulmonary CT-based standardized reporting system could be addressed as a solution. The core of it focuses on certain impediments such as the overworking of doctors, aiming essentially to solve a classification problem using deep learning techniques, namely, if a patient suffers from COVID-19, viral pneumonia, or is healthy from a pulmonary point of view. The methodology's approach was a meticulous one, denoting an empirical character in which the initial stage, given using data processing, performs an extraction of the lung cavity from the CT scans, which is a less explored approach, followed by data augmentation. The next step is comprehended by developing a CNN in two scenarios, one in which there is a binary classification (COVID and non-COVID patients), and the other one is represented by a three-class classification. Moreover, viral pneumonia is addressed. To obtain an efficient version, architectural changes were gradually made, involving four databases during this process. Furthermore, given the availability of pre-trained models, the transfer learning technique was employed by incorporating the linear classifier from our own convolutional network into an existing model, with the result being much more promising. The experimentation encompassed several models including MobileNetV1, ResNet50, DenseNet201, VGG16, and VGG19. Through a more in-depth analysis, using the CAM technique, MobilneNetV1 differentiated itself via the detection accuracy of possible pulmonary anomalies. Interestingly, this model stood out as not being among the most used in the literature. As a result, the following values of evaluation metrics were reached: loss (0.0751), accuracy (0.9744), precision (0.9758), recall (0.9742), AUC (0.9902), and F1 score (0.9750), from 1161 samples allocated for each of the three individual classes.
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Affiliation(s)
- Maria-Alexandra Zolya
- Department of Automatics and Information Technology, Transilvania University of Brasov, 500036 Brașov, Romania; (C.B.); (D.-V.B.); (S.C.); (S.-A.M.)
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11
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Farhat F, Sohail SS, Alam MT, Ubaid S, Shakil, Ashhad M, Madsen DØ. COVID-19 and beyond: leveraging artificial intelligence for enhanced outbreak control. Front Artif Intell 2023; 6:1266560. [PMID: 38028660 PMCID: PMC10663297 DOI: 10.3389/frai.2023.1266560] [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: 07/25/2023] [Accepted: 10/02/2023] [Indexed: 12/01/2023] Open
Abstract
COVID-19 has brought significant changes to our political, social, and technological landscape. This paper explores the emergence and global spread of the disease and focuses on the role of Artificial Intelligence (AI) in containing its transmission. To the best of our knowledge, there has been no scientific presentation of the early pictorial representation of the disease's spread. Additionally, we outline various domains where AI has made a significant impact during the pandemic. Our methodology involves searching relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to identify the ways AI has addressed pandemic-related challenges and its potential for further assistance. While research suggests that AI has not fully realized its potential against COVID-19, likely due to data quality and diversity limitations, we review and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We also propose ways to maximize the utilization of AI's capabilities in this regard.
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Affiliation(s)
- Faiza Farhat
- Department of Zoology, Aligarh Muslim University, Aligarh, India
| | | | - Mohammed Talha Alam
- Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
| | - Syed Ubaid
- Faculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Shakil
- Faculty of Electronic and Information Technology, Warsaw University of Technology, Warsaw, Poland
| | - Mohd Ashhad
- Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India
| | - Dag Øivind Madsen
- USN School of Business, University of South-Eastern Norway, Hønefoss, Norway
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12
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Favaro A, Tsai YT, Butala A, Thebaud T, Villalba J, Dehak N, Moro-Velázquez L. Interpretable speech features vs. DNN embeddings: What to use in the automatic assessment of Parkinson's disease in multi-lingual scenarios. Comput Biol Med 2023; 166:107559. [PMID: 37852107 DOI: 10.1016/j.compbiomed.2023.107559] [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: 07/03/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/20/2023]
Abstract
Speech-based approaches for assessing Parkinson's Disease (PD) often rely on feature extraction for automatic classification or detection. While many studies prioritize accuracy by using non-interpretable embeddings from Deep Neural Networks, this work aims to explore the predictive capabilities and language robustness of both feature types in a systematic fashion. As interpretable features, prosodic, linguistic, and cognitive descriptors were adopted, while x-vectors, Wav2Vec 2.0, HuBERT, and TRILLsson representations were used as non-interpretable features. Mono-lingual, multi-lingual, and cross-lingual machine learning experiments were conducted leveraging six data sets comprising speech recordings from various languages: American English, Castilian Spanish, Colombian Spanish, Italian, German, and Czech. For interpretable feature-based models, the mean of the best F1-scores obtained from each language was 81% in mono-lingual, 81% in multi-lingual, and 71% in cross-lingual experiments. For non-interpretable feature-based models, instead, they were 85% in mono-lingual, 88% in multi-lingual, and 79% in cross-lingual experiments. Firstly, models based on non-interpretable features outperformed interpretable ones, especially in cross-lingual experiments. Specifically, TRILLsson provided the most stable and accurate results across tasks and data sets. Conversely, the two types of features adopted showed some level of language robustness in multi-lingual and cross-lingual experiments. Overall, these results suggest that interpretable feature-based models can be used by clinicians to evaluate the deterioration of the speech of patients with PD, while non-interpretable feature-based models can be leveraged to achieve higher detection accuracy.
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Affiliation(s)
- Anna Favaro
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America.
| | - Yi-Ting Tsai
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
| | - Ankur Butala
- Department of Neurology, The Johns Hopkins University, Baltimore, 21218, MD, United States of America; Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
| | - Thomas Thebaud
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
| | - Jesús Villalba
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
| | - Najim Dehak
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
| | - Laureano Moro-Velázquez
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, 21218, MD, United States of America
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13
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Knany HR, Elsabbagh SA, Shehata MA, Eldehna WM, Bekhit AA, Ibrahim TM. In silico screening of SARS-CoV2 helicase using African natural products: Docking and molecular dynamics approaches. Virology 2023; 587:109863. [PMID: 37586235 DOI: 10.1016/j.virol.2023.109863] [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: 02/28/2023] [Revised: 07/19/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
In the current medical era, there is an urgent necessity to identify new effective drugs to enrich the COVID-19's therapeutic arsenal. The SARS-COV-2 NSP13/helicase enzyme has been identified as a potential target for developing novel COVID-19 inhibitors. In this work, we aimed at endorsing effective natural products with potential inhibitory action towards the NSP13 through the virtual screening of 1012 natural products of botanical and marine origin from the South African Natural Compounds Database (SANCDB). The molecules were docked into the NTPase active site, and the best twelve compounds were chosen for further analysis. Thereafter, a combination of molecular dynamics simulations and MM-GBSA free energy calculations were carried out for a subset of best hits complexed with NSP13 helicase. We believe that the findings of this work will pave the way for additional research and experimental validation of some natural products as viable NSP13 helicase inhibitors.
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Affiliation(s)
- Hamada R Knany
- Department of Pharmacognosy, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt
| | - Sherif A Elsabbagh
- Biochemistry Department, Institute of Pharmacy, Eberhard-Karls University, Auf der Morgenstelle 8, 72076, Tuebingen, Germany
| | - Moustafa A Shehata
- Department of Zoology, Faculty of Science, Cairo University, Giza, 12613, Egypt
| | - Wagdy M Eldehna
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt
| | - Adnan A Bekhit
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Alexandria University, Alexandria, 21521, Egypt; Cancer Nanotechnology Research Laboratory (CNRL), Faculty of Pharmacy, Alexandria University, Alexandria, 21521, Egypt; Pharmacy Program, Allied Health Department, College of Health and Sport Sciences, University of Bahrain, P.O. Box 32038, Kingdom of Bahrain
| | - Tamer M Ibrahim
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt.
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14
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Singaram S, Mayer CH, Oosthuizen RM. Leading higher education into the fourth industrial revolution: an empirical investigation. Front Psychol 2023; 14:1242835. [PMID: 37711331 PMCID: PMC10499039 DOI: 10.3389/fpsyg.2023.1242835] [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: 06/19/2023] [Accepted: 08/14/2023] [Indexed: 09/16/2023] Open
Abstract
The Fourth Industrial Revolution (4IR) rapidly advanced at the beginning of the 21st century. Leaders within organisations need to adjust their visions, plans, organisational structures, and management with regard to the demands, challenges and opportunities of this development. This is in particular the case in higher educational institutions (HEIs), which have to adjust to the rapid changes and new demands of skills of university graduates. Leaders in HEIs must therefore be aware of the related challenges and opportunities and might have to adjust the learning and teaching environment, the skills development of students, graduates, and faculty, as well as the technological requirements to create advanced skill sets. This article is based on a qualitative research study which was conducted at a university in South Africa. In-depth, semi-structured interviews were used to explore the views of higher-education leaders at the selected university. Data were analysed through thematic analysis. It was found that leaders in HEIs need to be aware of their leadership and preferences in times of transition toward a more technologised learning environment, as well as the needs, demands, challenges and opportunities of the new workplaces, and new skill sets needed in the 4IR. The researchers made some recommendations. "Education is the key that unlocks the golden door to freedom." George Washington Carver.
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Affiliation(s)
- Shwetha Singaram
- Department of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
| | - Claude-Hélène Mayer
- Department of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg, South Africa
| | - Rudolf M. Oosthuizen
- Department of Industrial and Organisational Psychology, University of South Africa, Pretoria, South Africa
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15
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Malik H, Anees T, Al-Shamaylehs AS, Alharthi SZ, Khalil W, Akhunzada A. Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images. Diagnostics (Basel) 2023; 13:2772. [PMID: 37685310 PMCID: PMC10486427 DOI: 10.3390/diagnostics13172772] [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: 07/31/2023] [Revised: 08/14/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Chest disease refers to a variety of lung disorders, including lung cancer (LC), COVID-19, pneumonia (PNEU), tuberculosis (TB), and numerous other respiratory disorders. The symptoms (i.e., fever, cough, sore throat, etc.) of these chest diseases are similar, which might mislead radiologists and health experts when classifying chest diseases. Chest X-rays (CXR), cough sounds, and computed tomography (CT) scans are utilized by researchers and doctors to identify chest diseases such as LC, COVID-19, PNEU, and TB. The objective of the work is to identify nine different types of chest diseases, including COVID-19, edema (EDE), LC, PNEU, pneumothorax (PNEUTH), normal, atelectasis (ATE), and consolidation lung (COL). Therefore, we designed a novel deep learning (DL)-based chest disease detection network (DCDD_Net) that uses a CXR, CT scans, and cough sound images for the identification of nine different types of chest diseases. The scalogram method is used to convert the cough sounds into an image. Before training the proposed DCDD_Net model, the borderline (BL) SMOTE is applied to balance the CXR, CT scans, and cough sound images of nine chest diseases. The proposed DCDD_Net model is trained and evaluated on 20 publicly available benchmark chest disease datasets of CXR, CT scan, and cough sound images. The classification performance of the DCDD_Net is compared with four baseline models, i.e., InceptionResNet-V2, EfficientNet-B0, DenseNet-201, and Xception, as well as state-of-the-art (SOTA) classifiers. The DCDD_Net achieved an accuracy of 96.67%, a precision of 96.82%, a recall of 95.76%, an F1-score of 95.61%, and an area under the curve (AUC) of 99.43%. The results reveal that DCDD_Net outperformed the other four baseline models in terms of many performance evaluation metrics. Thus, the proposed DCDD_Net model can provide significant assistance to radiologists and medical experts. Additionally, the proposed model was also shown to be resilient by statistical evaluations of the datasets using McNemar and ANOVA tests.
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Affiliation(s)
- Hassaan Malik
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan; (H.M.); (T.A.)
| | - Tayyaba Anees
- School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan; (H.M.); (T.A.)
| | - Ahmad Sami Al-Shamaylehs
- Department of Networks and Cybersecurity, Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan;
| | - Salman Z. Alharthi
- Department of Information System, College of Computers and Information Systems, Al-Lith Campus, Umm AL-Qura University, P.O. Box 7745, AL-Lith 21955, Saudi Arabia
| | - Wajeeha Khalil
- Department of Computer Science and Information Technology, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan;
| | - Adnan Akhunzada
- College of Computing & IT, University of Doha for Science and Technology, Doha P.O. Box 24449, Qatar;
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16
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Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio SR, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Front Med (Lausanne) 2023; 10:1230733. [PMID: 37601789 PMCID: PMC10433226 DOI: 10.3389/fmed.2023.1230733] [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: 05/29/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.
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Affiliation(s)
- Fabio Pisano
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Barbara Cannas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Alessandra Fanni
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Manuela Pasella
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | | | - Sabrina Rita Giglio
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Stefano Mocci
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Luchino Chessa
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Liver Unit, Department of Internal Medicine, University Hospital of Cagliari, Cagliari, Italy
| | - Andrea Perra
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Roberto Littera
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
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Bisen AC, Agrawal S, Sanap SN, Ravi Kumar HG, Kumar N, Gupta R, Bhatta RS. COVID-19 retreats and world recovers: A silver lining in the dark cloud. HEALTH CARE SCIENCE 2023; 2:264-285. [PMID: 38939523 PMCID: PMC11080794 DOI: 10.1002/hcs2.57] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/30/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2024]
Abstract
The coronavirus disease (COVID-19), which the World Health Organization classified as the Sixth Public Health Emergency Of International Concern (PHEIC) on January 30, 2020, is no longer a PHEIC. Millions were affected due to unawareness. The increase in fatalities and shortage of medicine was the first outrage of COVID-19. As per the Johns Hopkins COVID-19 resource center database, it was observed that the disease has spread dynamically across 200+ nations worldwide affecting more than 600 million people from 2019 to 2023, and over thousands of people were victimized regularly at a 2% mortality rate (approx.). In the midway, the mutant variants of concern like omicron, and delta have also created havoc and caused significant impact on public health, global economy, and lifestyle. Since 2019, 3 years now passed and the dynamic disease statistics seem decelerated; moreover, the prevalence of COVID-19 is also fading. The Johns Hopkins resource center has also stopped recording the data of the global pandemic recently from March 10, 2023. Hence, based on the facts, we are presenting a concise report on the pandemic from 2019 to 2023, which includes a brief discussion of the global pandemic. We have highlighted global epidemiology, emphasizing the Indian COVID scenario, vaccination across the globe, and the psychosocial and geopolitical consequences of COVID-19 with a brief background to pathology, clinical management, and the worldwide response against triage. A lot has changed and still needs to change after three tough years of COVID-19. Even though science has progressed and advanced research in medicine is pointing toward future generations, there is no standard care supplied for COVID-19-like calamities. COVID-19 cases might have declined but its influence on the society is still stagnant. This COVID experience has taught us that, despite our bleak beginnings, there is always hope for the future and that we must act with foresight to improve things for future generations.
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Affiliation(s)
- Amol Chhatrapati Bisen
- Pharmaceutics and Pharmacokinetics DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadUttar PradeshIndia
| | - Sristi Agrawal
- Pharmaceutics and Pharmacokinetics DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadUttar PradeshIndia
| | - Sachin Nashik Sanap
- Pharmaceutics and Pharmacokinetics DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadUttar PradeshIndia
| | | | - Nelam Kumar
- Biochemistry and Structural Biology DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
- Present address:
International Centre for Translational Eye Research (ICTER)Institute of Physical Chemistry (IChF)Marcina Kasprzaka 44/5201‐224WarsawPoland
| | - Rajdeep Gupta
- Pharmaceutics and Pharmacokinetics DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
| | - Rabi Sankar Bhatta
- Pharmaceutics and Pharmacokinetics DivisionCSIR—Central Drug Research InstituteLucknowUttar PradeshIndia
- Academy of Scientific and Innovative Research (AcSIR)GhaziabadUttar PradeshIndia
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18
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Bumm R, Zaffino P, Lasso A, Estépar RSJ, Pieper S, Wasserthal J, Spadea MF, Latshang T, Kawel-Böhm N, Wäckerlin A, Werner R, Hässig G, Furrer M, Kikinis R. From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases. RESEARCH SQUARE 2023:rs.3.rs-3027617. [PMID: 37333197 PMCID: PMC10275043 DOI: 10.21203/rs.3.rs-3027617/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Background The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts. Methods A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against a human rating provided by radiological experts. Results The results showed a higher degree of infiltration and collapse in the lower lobes compared to the upper lobes (p < 0.05) No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobe demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, significantly more COVID-19 involvement was found when comparing the posterior vs. the anterior halves of the lungs and the lower vs. the upper half of the lungs. Patients, who required ICU admission during their treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion The findings suggest that the extent of lung involvement, particularly in the lower lobes, dorsal lungs, and lower half of the lungs, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.
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Affiliation(s)
- Rudolf Bumm
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, University "Magna Graecia" of Catanzaro, 88100 Catanzaro, Italy
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Canada
| | - Raúl San José Estépar
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jakob Wasserthal
- Clinic of Radiology & Nuclear Medicine, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Maria Francesca Spadea
- Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
| | - Tsogyal Latshang
- Department of Pneumonology, Kantonsspital Graubünden, Chur, Switzerland
| | - Nadine Kawel-Böhm
- Department of Radiology, Kantonsspital Graubünden, Chur, Switzerland
| | - Adrian Wäckerlin
- Department of Intensive Care Medicine, Kantonsspital Graubünden, Chur, Switzerland
| | - Raphael Werner
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Gabriela Hässig
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Markus Furrer
- Department of Thoracic Surgery, Kantonsspital Graubünden, Chur, Switzerland
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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19
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Sfayyih AH, Sulaiman N, Sabry AH. A review on lung disease recognition by acoustic signal analysis with deep learning networks. JOURNAL OF BIG DATA 2023; 10:101. [PMID: 37333945 PMCID: PMC10259357 DOI: 10.1186/s40537-023-00762-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.
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Affiliation(s)
- Alyaa Hamel Sfayyih
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Nasri Sulaiman
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, 43400 Serdang, Malaysia
| | - Ahmad H. Sabry
- Department of Computer Engineering, Al-Nahrain University, Al Jadriyah Bridge, 64074 Baghdad, Iraq
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20
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Vijayanandh T, Shenbagavalli A. A Hybrid Deep Neural Approach for Segmenting the COVID Affection Area from the Lungs X-Ray Images. NEW GENERATION COMPUTING 2023; 41:1-20. [PMID: 37362548 PMCID: PMC10184644 DOI: 10.1007/s00354-023-00222-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/04/2023] [Indexed: 06/28/2023]
Abstract
Nowadays, COVID severity prediction has attracted widely in medical research because of the disease severity. Hence, the image processing application is also utilized to analyze COVID severity identification using lungs X-ray images. Thus, several intelligent schemes were employed to detect the COVID-affected part of the lungs X-ray images. However, the traditional neural approaches reported less severity classification accuracy due to the image complexity score. So, the present study has presented a novel chimp-based Adaboost Severity Analysis (CbASA) implemented in the MATLAB environment. Hence, the lung's X-ray images are utilized to test the working performance of the designed model. All public imaging data sources contain more noisy features, so the noise features are removed in the initial hidden layer of the novel CbASA then the noise-free data is imported into the classification phase. Feature extraction, segmentation, and severity specification have been performed in the classification layer. Finally, the performance of the classification score has been measured and compared with other models. Subsequently, the presented novel CbASA has earned the finest classification outcome.
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Affiliation(s)
- T. Vijayanandh
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu 600062 India
| | - A. Shenbagavalli
- Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, Tamil Nadu 628503 India
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21
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Althaqafi T, Al-Ghamdi ASAM, Ragab M. Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images. Healthcare (Basel) 2023; 11:healthcare11091204. [PMID: 37174746 PMCID: PMC10177894 DOI: 10.3390/healthcare11091204] [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: 04/06/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches.
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Affiliation(s)
- Turki Althaqafi
- Information Systems Department, HECI School, Dar Al-Hekma University, Jeddah 34801, Saudi Arabia
| | - Abdullah S Al-Malaise Al-Ghamdi
- Information Systems Department, HECI School, Dar Al-Hekma University, Jeddah 34801, Saudi Arabia
- Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
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22
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Rockall AG, Shelmerdine SC, Chen M. AI and ML in radiology: Making progress. Clin Radiol 2023; 78:81-82. [PMID: 36639174 DOI: 10.1016/j.crad.2022.10.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 10/26/2022] [Indexed: 01/12/2023]
Affiliation(s)
- A G Rockall
- Department of Surgery and Cancer, Imperial College London, Hammersmith Campus, Du Cane Rd, W12 0NN, UK.
| | - S C Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, WC1N 3JH, UK
| | - M Chen
- Department of Surgery and Cancer, Imperial College London, UK
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