51
|
Govindarajan S, Swaminathan R. Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106058. [PMID: 33789212 DOI: 10.1016/j.cmpb.2021.106058] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 03/16/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Computer aided diagnostics of Pulmonary Tuberculosis in chest radiographs relies on the differentiation of subtle and non-specific alterations in the images. In this study, an attempt has been made to identify and classify Tuberculosis conditions from healthy subjects in chest radiographs using integrated local feature descriptors and variants of extreme learning machine. METHODS Lung fields in the chest images are segmented using Reaction Diffusion Level Set method. Local feature descriptors such as Median Robust Extended Local Binary Patterns and Gradient Local Ternary Patterns are extracted. Extreme Learning Machine (ELM) and Online Sequential ELM (OSELM) classifiers are employed to identify Tuberculosis conditions and, their performances are analysed using standard metrics. RESULTS Results show that the adopted segmentation method is able to delineate lung fields in both healthy and Tuberculosis images. Extracted features are statistically significant even in images with inter and intra subject variability. Sigmoid activation function yields accuracy and sensitivity values greater than 98% for both the classifiers. Highest sensitivity is observed with OSELM for minimal significant features in detecting Tuberculosis images. CONCLUSION As ELM based method is able to differentiate the subtle changes in inter and intra subject variations of chest X-ray images, the proposed methodology seems to be useful for computer-based detection of Pulmonary Tuberculosis.
Collapse
Affiliation(s)
- Satyavratan Govindarajan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| |
Collapse
|
52
|
Mallio CA, Quattrocchi CC, Beomonte Zobel B, Parizel PM. Artificial intelligence, chest radiographs, and radiology trainees: a powerful combination to enhance the future of radiologists? Quant Imaging Med Surg 2021; 11:2204-2207. [PMID: 33937001 PMCID: PMC8047344 DOI: 10.21037/qims-20-1306] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 12/07/2020] [Indexed: 11/06/2022]
Affiliation(s)
- Carlo A. Mallio
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Carlo C. Quattrocchi
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Bruno Beomonte Zobel
- Departmental Faculty of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Paul M. Parizel
- Department of Radiology, Royal Perth Hospital and University of Western Australia Medical School, Perth, WA, Australia
| |
Collapse
|
53
|
Zhou SK, Greenspan H, Davatzikos C, Duncan JS, van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2021; 109:820-838. [PMID: 37786449 PMCID: PMC10544772 DOI: 10.1109/jproc.2021.3054390] [Citation(s) in RCA: 202] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
Collapse
Affiliation(s)
- S Kevin Zhou
- School of Biomedical Engineering, University of Science and Technology of China and Institute of Computing Technology, Chinese Academy of Sciences
| | - Hayit Greenspan
- Biomedical Engineering Department, Tel-Aviv University, Israel
| | - Christos Davatzikos
- Radiology Department and Electrical and Systems Engineering Department, University of Pennsylvania, USA
| | - James S Duncan
- Departments of Biomedical Engineering and Radiology & Biomedical Imaging, Yale University
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University and Louis Stokes Cleveland Veterans Administration Medical Center, USA
| | - Jerry L Prince
- Electrical and Computer Engineering Department, Johns Hopkins University, USA
| | - Daniel Rueckert
- Klinikum rechts der Isar, TU Munich, Germany and Department of Computing, Imperial College, UK
| | | |
Collapse
|
54
|
Puttagunta M, Ravi S. Medical image analysis based on deep learning approach. MULTIMEDIA TOOLS AND APPLICATIONS 2021; 80:24365-24398. [PMID: 33841033 PMCID: PMC8023554 DOI: 10.1007/s11042-021-10707-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/28/2020] [Accepted: 02/10/2021] [Indexed: 05/05/2023]
Abstract
Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
Collapse
Affiliation(s)
- Muralikrishna Puttagunta
- Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry, India
| | - S. Ravi
- Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry, India
| |
Collapse
|
55
|
Bohlbro AS, Hvingelby VS, Rudolf F, Wejse C, Patsche CB. Active case-finding of tuberculosis in general populations and at-risk groups: a systematic review and meta-analysis. Eur Respir J 2021; 58:13993003.00090-2021. [PMID: 33766950 DOI: 10.1183/13993003.00090-2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 03/14/2021] [Indexed: 11/05/2022]
Abstract
The World Health Organization (WHO) recommends active case-finding (ACF) of Tuberculosis (TB) in certain high-risk groups; however, more evidence is needed to elucidate the scope of ACF beyond the current recommendations. In this study we aimed to systematically review yields (the prevalence of active TB) of studies on ACF in general populations and at-risk groups.The review protocol was registered with PROSPERO (registration no.: CRD42020206856). A literature search in PubMed, Embase, and CENTRAL was performed for studies concluded after 31/12/1999 and published before 01/09/2020. Screening yields were estimated and yield/prevalence ratios (ratio between yield of study and WHO estimated prevalence of TB) were calculated to assess which groups might especially benefit from ACF. Finally, risk of bias was assessed, and heterogeneity was investigated using meta-regression and sensitivity analyses.We included 197 studies, with a total of 12 372 530 screened and 53 158 cases found. Yields were high among drug users, close contacts, the poor and marginalised, people living with HIV (PLHIV), and prison inmates across incidence strata and estimated yield/prevalence ratios in screenings of general populations tended to be >1 with an overall ratio of 1.4 and ranging between 1.0 and 1.5. Sensitivity analyses suggested that inclusion of studies at high risk of bias contributed to underestimation of yields.Despite many studies using insensitive screening methods, these results suggest that more at-risk groups should be considered for inclusion in future screening recommendations and that screening of general populations may outperform current case-finding practices, providing evidence for extending ACF beyond the current recommendations.
Collapse
Affiliation(s)
- Anders Solitander Bohlbro
- Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.,Department of Infectious Diseases, Aarhus University Hospital, Aarhus N, Denmark.,GloHAU, Center for Global Health, Department of Public Health, Aarhus University, Denmark
| | | | - Frauke Rudolf
- Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.,Department of Infectious Diseases, Aarhus University Hospital, Aarhus N, Denmark
| | - Christian Wejse
- Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.,Department of Infectious Diseases, Aarhus University Hospital, Aarhus N, Denmark.,GloHAU, Center for Global Health, Department of Public Health, Aarhus University, Denmark
| | - Cecilie Blenstrup Patsche
- Bandim Health Project, INDEPTH Network, Bissau, Guinea-Bissau.,GloHAU, Center for Global Health, Department of Public Health, Aarhus University, Denmark
| |
Collapse
|
56
|
Cao XF, Li Y, Xin HN, Zhang HR, Pai M, Gao L. Application of artificial intelligence in digital chest radiography reading for pulmonary tuberculosis screening. Chronic Dis Transl Med 2021; 7:35-40. [PMID: 34013178 PMCID: PMC8110935 DOI: 10.1016/j.cdtm.2021.02.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Indexed: 12/18/2022] Open
Abstract
Currently, the diagnosis of tuberculosis (TB) is mainly based on the comprehensive consideration of the patient's symptoms and signs, laboratory examinations and chest radiography (CXR). CXR plays a pivotal role to support the early diagnosis of TB, especially when used for TB screening and differential diagnosis. However, high cost of CXR hardware and shortage of certified radiologists poses a major challenge for CXR application in TB screening in resource limited settings. The latest development of artificial intelligence (AI) combined with the accumulation of a large number of medical images provides new opportunities for the establishment of computer-aided detection (CAD) systems in the medical applications, especially in the era of deep learning (DL) technology. Several CAD solutions are now commercially available and there is growing evidence demonstrate their value in imaging diagnosis. Recently, WHO published a rapid communication which stated that CAD may be used as an alternative to human reader interpretation of plain digital CXRs for screening and triage of TB.
Collapse
Affiliation(s)
- Xue-Fang Cao
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yuan Li
- JF Healthcare, Nanchang, Jiangxi 330072, China
| | - He-Nan Xin
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Hao-Ran Zhang
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Madhukar Pai
- McGill International TB Centre, McGill University, Montreal, Canada
| | - Lei Gao
- NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, And Center for Tuberculosis Research, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| |
Collapse
|
57
|
ter Haar Romeny BM. Introduction to Artificial Intelligence in Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_27-1] [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]
|
58
|
A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. J Imaging 2020; 6:jimaging6120131. [PMID: 34460528 PMCID: PMC8321202 DOI: 10.3390/jimaging6120131] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 11/25/2020] [Accepted: 11/25/2020] [Indexed: 12/24/2022] Open
Abstract
The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.
Collapse
|
59
|
Khan FA, Majidulla A, Tavaziva G, Nazish A, Abidi SK, Benedetti A, Menzies D, Johnston JC, Khan AJ, Saeed S. Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease. LANCET DIGITAL HEALTH 2020; 2:e573-e581. [PMID: 33328086 DOI: 10.1016/s2589-7500(20)30221-1] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/20/2020] [Accepted: 08/27/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND Deep learning-based radiological image analysis could facilitate use of chest x-rays as triage tests for pulmonary tuberculosis in resource-limited settings. We sought to determine whether commercially available chest x-ray analysis software meet WHO recommendations for minimal sensitivity and specificity as pulmonary tuberculosis triage tests. METHODS We recruited symptomatic adults at the Indus Hospital, Karachi, Pakistan. We compared two software, qXR version 2.0 (qXRv2) and CAD4TB version 6.0 (CAD4TBv6), with a reference of mycobacterial culture of two sputa. We assessed qXRv2 using its manufacturer prespecified threshold score for chest x-ray classification as tuberculosis present versus not present. For CAD4TBv6, we used a data-derived threshold, because it does not have a prespecified one. We tested for non-inferiority to preset WHO recommendations (0·90 for sensitivity, 0·70 for specificity) using a non-inferiority limit of 0·05. We identified factors associated with accuracy by stratification and logistic regression. FINDINGS We included 2198 (92·7%) of 2370 enrolled participants. 2187 (99·5%) of 2198 were HIV-negative, and 272 (12·4%) had culture-confirmed pulmonary tuberculosis. For both software, accuracy was non-inferior to WHO-recommended minimum values (qXRv2 sensitivity 0·93 [95% CI 0·89-0·95], non-inferiority p=0·0002; CAD4TBv6 sensitivity 0·93 [0·90-0·96], p<0·0001; qXRv2 specificity 0·75 [0·73-0·77], p<0·0001; CAD4TBv6 specificity 0·69 [0·67-0·71], p=0·0003). Sensitivity was lower in smear-negative pulmonary tuberculosis for both software, and in women for CAD4TBv6. Specificity was lower in men and in those with previous tuberculosis, and reduced with increasing age and decreasing body mass index. Smoking and diabetes did not affect accuracy. INTERPRETATION In an HIV-negative population, these software met WHO-recommended minimal accuracy for pulmonary tuberculosis triage tests. Sensitivity will be lower when smear-negative pulmonary tuberculosis is more prevalent. FUNDING Canadian Institutes of Health Research.
Collapse
Affiliation(s)
- Faiz Ahmad Khan
- McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada.
| | - Arman Majidulla
- Interactive Research and Development Pakistan, Karachi, Pakistan
| | - Gamuchirai Tavaziva
- McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | | | - Syed Kumail Abidi
- McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada
| | - Andrea Benedetti
- Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada
| | - Dick Menzies
- McGill International TB Centre, Research Institute of the McGill University Health Centre and McGill University, Montreal, QC, Canada; Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; Department of Medicine and Department of Epidemiology, McGill University, Montreal, Canada
| | - James C Johnston
- Ghori TB Clinic, University of British Columbia, Vancouver, BC, Canada
| | | | - Saima Saeed
- Global Health Directorate, Indus Health Network, Karachi, Pakistan
| |
Collapse
|
60
|
Lange C, Aarnoutse R, Chesov D, van Crevel R, Gillespie SH, Grobbel HP, Kalsdorf B, Kontsevaya I, van Laarhoven A, Nishiguchi T, Mandalakas A, Merker M, Niemann S, Köhler N, Heyckendorf J, Reimann M, Ruhwald M, Sanchez-Carballo P, Schwudke D, Waldow F, DiNardo AR. Perspective for Precision Medicine for Tuberculosis. Front Immunol 2020; 11:566608. [PMID: 33117351 PMCID: PMC7578248 DOI: 10.3389/fimmu.2020.566608] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/02/2020] [Indexed: 12/28/2022] Open
Abstract
Tuberculosis is a bacterial infectious disease that is mainly transmitted from human to human via infectious aerosols. Currently, tuberculosis is the leading cause of death by an infectious disease world-wide. In the past decade, the number of patients affected by tuberculosis has increased by ~20 percent and the emergence of drug-resistant strains of Mycobacterium tuberculosis challenges the goal of elimination of tuberculosis in the near future. For the last 50 years, management of patients with tuberculosis has followed a standardized management approach. This standardization neglects the variation in human susceptibility to infection, immune response, the pharmacokinetics of drugs, and the individual duration of treatment needed to achieve relapse-free cure. Here we propose a package of precision medicine-guided therapies that has the prospect to drive clinical management decisions, based on both host immunity and M. tuberculosis strains genetics. Recently, important scientific discoveries and technological advances have been achieved that provide a perspective for individualized rather than standardized management of patients with tuberculosis. For the individual selection of best medicines and host-directed therapies, personalized drug dosing, and treatment durations, physicians treating patients with tuberculosis will be able to rely on these advances in systems biology and to apply them at the bedside.
Collapse
Affiliation(s)
- Christoph Lange
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
- Cluster of Excellence Precision Medicine in Chronic Inflammation, Kiel, Germany
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Rob Aarnoutse
- Department of Internal Medicine, Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Dumitru Chesov
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
- Department of Pulmonology and Allergology, Nicolae Testemitanu University of Medicine and Pharmacy, Chisinau, Moldova
| | - Reinout van Crevel
- Department of Internal Medicine, Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Hans-Peter Grobbel
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Barbara Kalsdorf
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
- Cluster of Excellence Precision Medicine in Chronic Inflammation, Kiel, Germany
| | - Irina Kontsevaya
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Arjan van Laarhoven
- Department of Internal Medicine, Radboud Center of Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, Netherlands
| | - Tomoki Nishiguchi
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Anna Mandalakas
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Matthias Merker
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Cluster of Excellence Precision Medicine in Chronic Inflammation, Kiel, Germany
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
| | - Stefan Niemann
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Cluster of Excellence Precision Medicine in Chronic Inflammation, Kiel, Germany
- Molecular and Experimental Mycobacteriology, Research Center Borstel, Borstel, Germany
| | - Niklas Köhler
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Jan Heyckendorf
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Maja Reimann
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Morten Ruhwald
- Foundation of Innovative New Diagnostics (FIND), Geneva, Switzerland
| | - Patricia Sanchez-Carballo
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Respiratory Medicine and International Health, University of Lübeck, Lübeck, Germany
| | - Dominik Schwudke
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Bioanalytical Chemistry, Priority Area Infection, Research Center Borstel, Leibniz Lung Center, Borstel, Germany
- Airway Research Center North, German Center for Lung Research (DZL), Borstel, Germany
| | - Franziska Waldow
- German Center for Infection Research (DZIF) Partner Site Borstel-Hamburg-Lübeck-Riems, Borstel, Germany
- Bioanalytical Chemistry, Priority Area Infection, Research Center Borstel, Leibniz Lung Center, Borstel, Germany
| | - Andrew R. DiNardo
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| |
Collapse
|
61
|
Murphy K, Smits H, Knoops AJG, Korst MBJM, Samson T, Scholten ET, Schalekamp S, Schaefer-Prokop CM, Philipsen RHHM, Meijers A, Melendez J, van Ginneken B, Rutten M. COVID-19 on Chest Radiographs: A Multireader Evaluation of an Artificial Intelligence System. Radiology 2020; 296:E166-E172. [PMID: 32384019 PMCID: PMC7437494 DOI: 10.1148/radiol.2020201874] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/03/2020] [Accepted: 05/08/2020] [Indexed: 12/21/2022]
Abstract
Background Chest radiography may play an important role in triage for coronavirus disease 2019 (COVID-19), particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Materials and Methods An AI system (CAD4COVID-XRay) was trained on 24 678 chest radiographs, including 1540 used only for validation while training. The test set consisted of a set of continuously acquired chest radiographs (n = 454) obtained in patients suspected of having COVID-19 pneumonia between March 4 and April 6, 2020, at one center (223 patients with positive reverse transcription polymerase chain reaction [RT-PCR] results, 231 with negative RT-PCR results). Radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was analyzed with the receiver operating characteristic curve. Results For the test set, the mean age of patients was 67 years ± 14.4 (standard deviation) (56% male). With RT-PCR test results as the reference standard, the AI system correctly classified chest radiographs as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81. The system significantly outperformed each reader (P < .001 using the McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader significantly outperformed the AI system (P = .04). Conclusion The performance of an artificial intelligence system in the detection of coronavirus disease 2019 on chest radiographs was comparable with that of six independent readers. © RSNA, 2020.
Collapse
Affiliation(s)
- Keelin Murphy
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Henk Smits
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Arnoud J. G. Knoops
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Michael B. J. M. Korst
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Tijs Samson
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Ernst T. Scholten
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Steven Schalekamp
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Cornelia M. Schaefer-Prokop
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Rick H. H. M. Philipsen
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Annet Meijers
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Jaime Melendez
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Bram van Ginneken
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| | - Matthieu Rutten
- From the Diagnostic Image Analysis Group, Radboud University Medical Center, Geert Groteplein 10, Nijmegen 6500 HB, the Netherlands (K.M., E.T.S., S.S., C.M.S., B.v.G.); Department of Radiology, Bernhoven Hospital, Uden, the Netherlands (H.S.); Department of Radiology, Jeroen Bosch Hospital, ‘s-Hertogenbosch, the Netherlands (A.J.G.K., M.B.J.M.K., T.S., M.R.); Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands (C.M.S.); and Thirona, Nijmegen, the Netherlands (R.H.H.M.P., A.M., J.M.)
| |
Collapse
|
62
|
Nash M, Kadavigere R, Andrade J, Sukumar CA, Chawla K, Shenoy VP, Pande T, Huddart S, Pai M, Saravu K. Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India. Sci Rep 2020; 10:210. [PMID: 31937802 PMCID: PMC6959311 DOI: 10.1038/s41598-019-56589-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 12/10/2019] [Indexed: 12/01/2022] Open
Abstract
In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of professionals available to interpret CXR in low-resource, high PTB burden settings. Recently, several computer-aided detection (CAD) programs have been developed to facilitate automated CXR interpretation. We conducted a retrospective case-control study to assess the diagnostic accuracy of a CAD software (qXR, Qure.ai, Mumbai, India) using microbiologically-confirmed PTB as the reference standard. To assess overall accuracy of qXR, receiver operating characteristic (ROC) analysis was used to determine the area under the curve (AUC), along with 95% confidence intervals (CI). Kappa coefficients, and associated 95% CI, were used to investigate inter-rater reliability of the radiologists for detection of specific chest abnormalities. In total, 317 cases and 612 controls were included in the analysis. The AUC for qXR for the detection of microbiologically-confirmed PTB was 0.81 (95% CI: 0.78, 0.84). Using the threshold that maximized sensitivity and specificity of qXR simultaneously, the software achieved a sensitivity and specificity of 71% (95% CI: 66%, 76%) and 80% (95% CI: 77%, 83%), respectively. The sensitivity and specificity of radiologists for the detection of microbiologically-confirmed PTB was 56% (95% CI: 50%, 62%) and 80% (95% CI: 77%, 83%), respectively. For detection of key PTB-related abnormalities 'pleural effusion' and 'cavity', qXR achieved an AUC of 0.94 (95% CI: 0.92, 0.96) and 0.84 (95% CI: 0.82, 0.87), respectively. For the other abnormalities, the AUC ranged from 0.75 (95% CI: 0.70, 0.80) to 0.94 (95% CI: 0.91, 0.96). The controls had a high prevalence of other lung diseases which can cause radiological manifestations similar to PTB (e.g., 26% had pneumonia, 15% had lung malignancy, etc.). In a tertiary hospital in India, qXR demonstrated moderate sensitivity and specificity for the detection of PTB. There is likely a larger role for CAD software as a triage test for PTB at the primary care level in settings where access to radiologists in limited. Larger prospective studies that can better assess heterogeneity in important subgroups are needed.
Collapse
Affiliation(s)
- Madlen Nash
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- McGill International TB Centre, McGill University, Montreal, Canada
| | - Rajagopal Kadavigere
- Department of Radiodiagnosis, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Jasbon Andrade
- Department of Radiodiagnosis, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Cynthia Amrutha Sukumar
- Department of Medicine, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Kiran Chawla
- Department of Microbiology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Vishnu Prasad Shenoy
- Department of Microbiology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India
| | - Tripti Pande
- McGill International TB Centre, McGill University, Montreal, Canada
| | - Sophie Huddart
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- McGill International TB Centre, McGill University, Montreal, Canada
| | - Madhukar Pai
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- McGill International TB Centre, McGill University, Montreal, Canada
- Manipal McGill Program for Infectious Diseases, Manipal Centre for Infectious Diseases, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India
| | - Kavitha Saravu
- Department of Infectious Diseases, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India.
- Manipal McGill Program for Infectious Diseases, Manipal Centre for Infectious Diseases, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India.
| |
Collapse
|
63
|
Kulkarni S, Jha S. Artificial Intelligence, Radiology, and Tuberculosis: A Review. Acad Radiol 2020; 27:71-75. [PMID: 31759796 DOI: 10.1016/j.acra.2019.10.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 09/22/2019] [Accepted: 10/05/2019] [Indexed: 12/13/2022]
Abstract
Tuberculosis is a leading cause of death from infectious disease worldwide, and is an epidemic in many developing nations. Countries where the disease is common also tend to have poor access to medical care, including diagnostic tests. Recent advancements in artificial intelligence may help to bridge this gap. In this article, we review the applications of artificial intelligence in the diagnosis of tuberculosis using chest radiography, covering simple computer-aided diagnosis systems to more advanced deep learning algorithms. In so doing, we will demonstrate an area where artificial intelligence could make a substantial contribution to global health through improved diagnosis in the future.
Collapse
Affiliation(s)
- Sagar Kulkarni
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA.
| | - Saurabh Jha
- Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA
| |
Collapse
|
64
|
Ma J, Song Y, Tian X, Hua Y, Zhang R, Wu J. Survey on deep learning for pulmonary medical imaging. Front Med 2019; 14:450-469. [PMID: 31840200 DOI: 10.1007/s11684-019-0726-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 10/12/2019] [Indexed: 12/27/2022]
Abstract
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.
Collapse
Affiliation(s)
| | - Yang Song
- Dalian Municipal Central Hospital Affiliated to Dalian Medical University, Dalian, 116033, China
| | - Xi Tian
- InferVision, Beijing, 100020, China
| | | | | | - Jianlin Wu
- Affiliated Zhongshan Hospital of Dalian University, Dalian, 116001, China.
| |
Collapse
|