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Vidal-Mondéjar J, Tejedor-Romero L, Catalá-López F. Methodological evaluation of systematic reviews based on the use of artificial intelligence systems in chest radiography. RADIOLOGIA 2024; 66:326-339. [PMID: 39089793 DOI: 10.1016/j.rxeng.2023.01.015] [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: 12/06/2022] [Accepted: 01/18/2023] [Indexed: 08/04/2024]
Abstract
INTRODUCTION In recent years, systems that use artificial intelligence (AI) in medical imaging have been developed, such as the interpretation of chest X-ray to rule out pathology. This has produced an increase in systematic reviews (SR) published on this topic. This article aims to evaluate the methodological quality of SRs that use AI for the diagnosis of thoracic pathology by simple chest X-ray. MATERIAL AND METHODS SRs evaluating the use of AI systems for the automatic reading of chest X-ray were selected. Searches were conducted (from inception to May 2022): PubMed, EMBASE, and the Cochrane Database of Systematic Reviews. Two investigators selected the reviews. From each SR, general, methodological and transparency characteristics were extracted. The PRISMA statement for diagnostic tests (PRISMA-DTA) and AMSTAR-2 were used. A narrative synthesis of the evidence was performed. Protocol registry: Open Science Framework: https://osf.io/4b6u2/. RESULTS After applying the inclusion and exclusion criteria, 7 SRs were selected (mean of 36 included studies per review). All the included SRs evaluated "deep learning" systems in which chest X-ray was used for the diagnosis of infectious diseases. Only 2 (29%) SRs indicated the existence of a review protocol. None of the SRs specified the design of the included studies or provided a list of excluded studies with their justification. Six (86%) SRs mentioned the use of PRISMA or one of its extensions. The risk of bias assessment was performed in 4 (57%) SRs. One (14%) SR included studies with some validation of AI techniques. Five (71%) SRs presented results in favour of the diagnostic capacity of the intervention. All SRs were rated "critically low" following AMSTAR-2 criteria. CONCLUSIONS The methodological quality of SRs that use AI systems in chest radiography can be improved. The lack of compliance in some items of the tools used means that the SRs published in this field must be interpreted with caution.
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Affiliation(s)
- J Vidal-Mondéjar
- Servicio de Medicina Preventiva, Hospital Universitario de La Princesa, Madrid, Spain.
| | - L Tejedor-Romero
- Servicio de Medicina Preventiva, Hospital Universitario de La Princesa, Madrid, Spain
| | - F Catalá-López
- Departamento de Planificación y Economía de la Salud, Escuela Nacional de Sanidad, Instituto de Salud Carlos III, Madrid, Spain; Departamento de Medicina, Universidad de Valencia/Instituto de Investigación Sanitaria INCLIVA y CIBERSAM, Valencia, Spain; Knowledge Synthesis Group, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
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Worodria W, Castro R, Kik SV, Dalay V, Derendinger B, Festo C, Nguyen TQ, Raberahona M, Sudarsan S, Andama A, Thangakunam B, Lyimo I, Nguyen VN, Rakotoarivelo R, Theron G, Yu C, Denkinger CM, Lapierre SG, Cattamanchi A, Christopher DJ, Jaganath D. An independent, multi-country head-to-head accuracy comparison of automated chest x-ray algorithms for the triage of pulmonary tuberculosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.19.24309061. [PMID: 38946949 PMCID: PMC11213091 DOI: 10.1101/2024.06.19.24309061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Background Computer-aided detection (CAD) algorithms for automated chest X-ray (CXR) reading have been endorsed by the World Health Organization for tuberculosis (TB) triage, but independent, multi-country assessment and comparison of current products are needed to guide implementation. Methods We conducted a head-to-head evaluation of five CAD algorithms for TB triage across seven countries. We included CXRs from adults who presented to outpatient facilities with at least two weeks of cough in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam. The participants completed a standard evaluation for pulmonary TB, including sputum collection for Xpert MTB/RIF Ultra and culture. Against a microbiological reference standard, we calculated and compared the accuracy overall, by country and key groups for five CAD algorithms: CAD4TB (Delft Imaging), INSIGHT CXR (Lunit), DrAid (Vinbrain), Genki (Deeptek), and qXR (qure.AI). We determined the area under the ROC curve (AUC) and if any CAD product could achieve the minimum target accuracy for a TB triage test (≥90% sensitivity and ≥70% specificity). We then applied country- and population-specific thresholds and recalculated accuracy to assess any improvement in performance. Results Of 3,927 individuals included, the median age was 41 years (IQR 29-54), 12.9% were people living with HIV (PLWH), 8.2% living with diabetes, and 21.2% had a prior history of TB. The overall AUC ranged from 0.774-0.819, and specificity ranged from 64.8-73.8% at 90% sensitivity. CAD4TB had the highest overall accuracy (73.8% specific, 95% CI 72.2-75.4, at 90% sensitivity), although qXR and INSIGHT CXR also achieved the target 70% specificity. There was heterogeneity in accuracy by country, and females and PLWH had lower sensitivity while males and people with a history of TB had lower specificity. The performance remained stable regardless of diabetes status. When country- and population-specific thresholds were applied, at least one CAD product could achieve or approach the target accuracy for each country and sub-group, except for PLWH and those with a history of TB. Conclusions Multiple CAD algorithms can achieve or exceed the minimum target accuracy for a TB triage test, with improvement when using setting- or population-specific thresholds. Further efforts are needed to integrate CAD into routine TB case detection programs in high-burden communities.
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Affiliation(s)
- William Worodria
- 1. World Alliance for Lung and Intensive Care in Uganda, Kampala, Uganda
| | - Robert Castro
- 2. Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, USA
- 3. Center for Tuberculosis, University of California, San Francisco, USA
| | | | - Victoria Dalay
- 5. De La Salle Medical and Health Sciences Institute, Dasmarinas Cavite, Philippines
| | - Brigitta Derendinger
- 6. DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Charles Festo
- 7. Ifakara Health Institute, Dar es Salaam, Tanzania
| | - Thanh Quoc Nguyen
- 8. Vietnam National Tuberculosis Programme, National Lung Hospital, Hanoi, Vietnam
- 9. VNU University of Medicine and Pharmacy, Hanoi, Vietnam
| | - Mihaja Raberahona
- 10. Department of Infectious Diseases, CHU Joseph Raseta Befelatanana, Antananarivo, Madagascar
- 11. Centre d’Infectiologie Charles Mérieux, Université d’Antananarivo, Antananarivo, Madagascar
| | - Swati Sudarsan
- 2. Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, USA
- 3. Center for Tuberculosis, University of California, San Francisco, USA
| | - Alfred Andama
- 1. World Alliance for Lung and Intensive Care in Uganda, Kampala, Uganda
| | - Balamugesh Thangakunam
- 12. Department of Pulmonary Medicine, Christian Medical College, Vellore, Tamil Nadu, India
| | - Issa Lyimo
- 7. Ifakara Health Institute, Dar es Salaam, Tanzania
| | - Viet Nhung Nguyen
- 8. Vietnam National Tuberculosis Programme, National Lung Hospital, Hanoi, Vietnam
- 9. VNU University of Medicine and Pharmacy, Hanoi, Vietnam
| | - Rivo Rakotoarivelo
- 11. Centre d’Infectiologie Charles Mérieux, Université d’Antananarivo, Antananarivo, Madagascar
- 13. Faculté de Médecine, Université de Fianarantsoa, Fianarantsoa, Madagascar
| | - Grant Theron
- 6. DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa
| | - Charles Yu
- 5. De La Salle Medical and Health Sciences Institute, Dasmarinas Cavite, Philippines
| | - Claudia M. Denkinger
- 14. Department of Infectious Disease and Tropical Medicine, Center for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
- 15. German Center for Infection Research, partner site, Heidelberg, Germany
| | - Simon Grandjean Lapierre
- 16. Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Immunopathology Axis, Montréal, Canada
- 17. Université de Montréal, Department of Microbiology, Infectious Diseases and Immunology, Montréal, Canada
| | - Adithya Cattamanchi
- 3. Center for Tuberculosis, University of California, San Francisco, USA
- 18. Division of Pulmonary Diseases and Critical Care Medicine, School of Medicine, University of California Irvine, Orange, USA
| | | | - Devan Jaganath
- 3. Center for Tuberculosis, University of California, San Francisco, USA
- 19. Division of Pediatric Infectious Diseases, University of California, San Francisco, San Francisco, USA
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Torres-Fernandez D, Dalsuco J, Bramugy J, Bassat Q, Varo R. Innovative strategies for the surveillance, prevention, and management of pediatric infections applied to low-income settings. Expert Rev Anti Infect Ther 2024; 22:413-422. [PMID: 38739471 DOI: 10.1080/14787210.2024.2354839] [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: 06/09/2023] [Accepted: 05/09/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION Infectious diseases still cause a significant burden of morbidity and mortality among children in low- and middle-income countries (LMICs). There are ample opportunities for innovation in surveillance, prevention, and management, with the ultimate goal of improving survival. AREAS COVERED This review discusses the current status in the use and development of innovative strategies for pediatric infectious diseases in LMICs by focusing on surveillance, diagnosis, prevention, and management. Topics covered are: Minimally Invasive Tissue Sampling as a technique to accurately ascertain the cause of death; Genetic Surveillance to trace the pathogen genomic diversity and emergence of resistance; Artificial Intelligence as a multidisciplinary tool; Portable noninvasive imaging methods; and Prognostic Biomarkers to triage and risk stratify pediatric patients. EXPERT OPINION To overcome the specific hurdles in child health for LMICs, some innovative strategies appear at the forefront of research. If the development of these next-generation tools remains focused on accessibility, sustainability and capacity building, reshaping epidemiological surveillance, diagnosis, and treatment in LMICs, can become a reality and result in a significant public health impact. Their integration with existing healthcare infrastructures may revolutionize disease detection and surveillance, and improve child health and survival.
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Affiliation(s)
- David Torres-Fernandez
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
| | - Jessica Dalsuco
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
| | - Justina Bramugy
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
| | - Quique Bassat
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
- ICREA, Pg. Lluís Companys, Barcelona, Spain
- Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Barcelona, Spain
- CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
| | - Rosauro Varo
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique
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Wang CH, Chang W, Lee MR, Tay J, Wu CY, Wu MC, Roth HR, Yang D, Zhao C, Wang W, Huang CH. Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:589-600. [PMID: 38343228 PMCID: PMC11031502 DOI: 10.1007/s10278-023-00952-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 04/20/2024]
Abstract
Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.
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Affiliation(s)
- Chih-Hung Wang
- Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Weishan Chang
- Department of Mathematics, National Taiwan University, Taipei, Taiwan
| | - Meng-Rui Lee
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Joyce Tay
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Cheng-Yi Wu
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | - Meng-Che Wu
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan
| | | | - Dong Yang
- NVIDIA Corporation, Bethesda, MD, USA
| | - Can Zhao
- NVIDIA Corporation, Bethesda, MD, USA
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan.
| | - Chien-Hua Huang
- Department of Emergency Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
- Department of Emergency Medicine, National Taiwan University Hospital, No. 7, Zhongshan S. Rd, Zhongzheng Dist., Taipei City, 100, Taiwan.
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Huddart S, Yadav V, Sieberts SK, Omberg L, Raberahona M, Rakotoarivelo R, Lyimo IN, Lweno O, Christopher DJ, Nhung NV, Theron G, Worodria W, Yu CY, Bachman CM, Burkot S, Dewan P, Kulhare S, Small PM, Cattamanchi A, Jaganath D, Lapierre SG. Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.27.24304980. [PMID: 38585855 PMCID: PMC10996751 DOI: 10.1101/2024.03.27.24304980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Cough is a common and commonly ignored symptom of lung disease. Cough is often perceived as difficult to quantify, frequently self-limiting, and non-specific. However, cough has a central role in the clinical detection of many lung diseases including tuberculosis (TB), which remains the leading infectious disease killer worldwide. TB screening currently relies on self-reported cough which fails to meet the World Health Organization (WHO) accuracy targets for a TB triage test. Artificial intelligence (AI) models based on cough sound have been developed for several respiratory conditions, with limited work being done in TB. To support the development of an accurate, point-of-care cough-based triage tool for TB, we have compiled a large multi-country database of cough sounds from individuals being evaluated for TB. The dataset includes more than 700,000 cough sounds from 2,143 individuals with detailed demographic, clinical and microbiologic diagnostic information. We aim to empower researchers in the development of cough sound analysis models to improve TB diagnosis, where innovative approaches are critically needed to end this long-standing pandemic.
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Affiliation(s)
- Sophie Huddart
- University of California San Francisco, School of Medicine, 533 Parnassus Ave, San Francisco, CA 94143 USA
| | | | | | - Larson Omberg
- Sage Bionetworks, Seattle, WA 98103 USA
- Curently at Koneksa Health, One World Trade Center 285 Fulton St. 77th Floor New York, NY, 10007
| | - Mihaja Raberahona
- CHU Joseph Rasera Befelatanana, Antananarivo, 101, Analamanga, Madagascar
- Centre d’Infectiologie Charles Mérieux, Antananarivo, 101, Analamanga, Madagascar
| | - Rivo Rakotoarivelo
- CHU Tambohobe Fianarantsoa, 301, Haute-Matsiatra, Madagascar
- Université de Fianarantsoa, Fianarantsoa, 301, Haute-Matsiatra, Madagascar
| | - Issa N. Lyimo
- Ifakara Health Institute, Environmental and Ecological Sciences & Interventions and Clinical Trials Departments, Kiko Avenue, Plot 463, Mikocheni, Dar es Salaam, Tanzania
| | - Omar Lweno
- Ifakara Health Institute, Environmental and Ecological Sciences & Interventions and Clinical Trials Departments, Kiko Avenue, Plot 463, Mikocheni, Dar es Salaam, Tanzania
| | | | - Nguyen Viet Nhung
- National Tuberculosis Programme, 463 Hoang Hoa Tham, Ba Dinh District, Hanoi, Vietnam
| | - Grant Theron
- Stellenbosch University, Division of Molecular Biology and Human Genetics, Matieland, 7602 South Africa
| | | | - Charles Y. Yu
- De La Salle Medical and Health Sciences Institute, Governor D. Mangubat Avenue, Dasmarinas Cavite, Philippines 4114
| | | | - Stephen Burkot
- Global Health Labs, 14360 SE Eastgate Way, Bellevue, WA 98007 USA
| | - Puneet Dewan
- Global Health Labs, 14360 SE Eastgate Way, Bellevue, WA 98007 USA
| | - Sourabh Kulhare
- Global Health Labs, 14360 SE Eastgate Way, Bellevue, WA 98007 USA
| | - Peter M Small
- Global Health Labs, 14360 SE Eastgate Way, Bellevue, WA 98007 USA
| | - Adithya Cattamanchi
- University of California Irvine, School of Medicine, 333 City Blvd. W Suite 400, Orange CA 92868 USA
| | - Devan Jaganath
- University of California Irvine, School of Medicine, 333 City Blvd. W Suite 400, Orange CA 92868 USA
| | - Simon Grandjean Lapierre
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Immunopathology Axis, 900 St-Denis, Montréal, Québec, H2X 0A9 Canada
- Université de Montréal, Department of Microbiology, Infectious Diseases and Immunology, 2900 Edouard-Montpetit, Montréal, Québec, H3T 1J4 Canada
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Hwang EJ, Jeong WG, David PM, Arentz M, Ruhwald M, Yoon SH. AI for Detection of Tuberculosis: Implications for Global Health. Radiol Artif Intell 2024; 6:e230327. [PMID: 38197795 PMCID: PMC10982823 DOI: 10.1148/ryai.230327] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/03/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Eui Jin Hwang
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Won Gi Jeong
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Pierre-Marie David
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Matthew Arentz
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Morten Ruhwald
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
| | - Soon Ho Yoon
- From the Department of Radiology, Seoul National University Hospital
and Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu,
Seoul 03080, Korea (E.J.H., S.H.Y.); Department of Radiology, Chonnam National
University Hwasun Hospital, Hwasun, Korea (W.G.J.); Faculty of Pharmacy,
University of Montréal, Montréal, Canada (P.M.D.);
OBVIA–Observatoire sur les Impacts Sociétaux de l'IA et du
Numérique, Québec, Canada (P.M.D.); and FIND–The Global
Alliance for Diagnostics, Geneva, Switzerland (M.A., M.R.)
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Scott AJ, Perumal T, Hohlfeld A, Oelofse S, Kühn L, Swanepoel J, Geric C, Ahmad Khan F, Esmail A, Ochodo E, Engel M, Dheda K. Diagnostic Accuracy of Computer-Aided Detection During Active Case Finding for Pulmonary Tuberculosis in Africa: A Systematic Review and Meta-analysis. Open Forum Infect Dis 2024; 11:ofae020. [PMID: 38328498 PMCID: PMC10849117 DOI: 10.1093/ofid/ofae020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024] Open
Abstract
Background Computer-aided detection (CAD) may be a useful screening tool for tuberculosis (TB). However, there are limited data about its utility in active case finding (ACF) in a community-based setting, and particularly in an HIV-endemic setting where performance may be compromised. Methods We performed a systematic review and evaluated articles published between January 2012 and February 2023 that included CAD as a screening tool to detect pulmonary TB against a microbiological reference standard (sputum culture and/or nucleic acid amplification test [NAAT]). We collected and summarized data on study characteristics and diagnostic accuracy measures. Two reviewers independently extracted data and assessed methodological quality against Quality Assessment of Diagnostic Accuracy Studies-2 criteria. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines were followed. Results Of 1748 articles reviewed, 5 met with the eligibility criteria and were included in this review. A meta-analysis revealed pooled sensitivity of 0.87 (95% CI, 0.78-0.96) and specificity of 0.74 (95% CI, 0.55-0.93), just below the World Health Organization (WHO)-recommended target product profile (TPP) for a screening test (sensitivity ≥0.90 and specificity ≥0.70). We found a high risk of bias and applicability concerns across all studies. Subgroup analyses, including the impact of HIV and previous TB, were not possible due to the nature of the reporting within the included studies. Conclusions This review provides evidence, specifically in the context of ACF, for CAD as a potentially useful and cost-effective screening tool for TB in a resource-poor HIV-endemic African setting. However, given methodological concerns, caution is required with regards to applicability and generalizability.
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Affiliation(s)
- Alex J Scott
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Tahlia Perumal
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Ameer Hohlfeld
- Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa
| | - Suzette Oelofse
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Louié Kühn
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Jeremi Swanepoel
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Coralie Geric
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Faiz Ahmad Khan
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada
| | - Aliasgar Esmail
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
| | - Eleanor Ochodo
- Kenya Medical Research Institute, Nairobi, Kenya
- Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa
| | - Mark Engel
- Department of Medicine, University of Cape Town, Cape Town, South Africa
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Keertan Dheda
- Division of Pulmonology, Department of Medicine, Centre for Lung Infection and Immunity, University of Cape Town Lung Institute, Cape Town, South Africa
- Centre for the Study of Antimicrobial Resistance, South African Medical Research Council and University of Cape Town, Cape Town, South Africa
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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8
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Eneogu RA, Mitchell EMH, Ogbudebe C, Aboki D, Anyebe V, Dimkpa CB, Egbule D, Nsa B, van der Grinten E, Soyinka FO, Abdur-Razzaq H, Useni S, Lawanson A, Onyemaechi S, Ubochioma E, Scholten J, Verhoef J, Nwadike P, Chukwueme N, Nongo D, Gidado M. Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0002018. [PMID: 38232129 DOI: 10.1371/journal.pgph.0002018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 11/29/2023] [Indexed: 01/19/2024]
Abstract
Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges. Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Persons with high CAD4TB score (≥80), who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months. An experimental calibration method achieved a viable CAD threshold for testing. High risk groups and key stakeholders were engaged. Operations evolved in real time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%) were recorded. Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores and initially spot -sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up. Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.
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Affiliation(s)
- Rupert A Eneogu
- United States Agency for International Development (USAID), Abuja, Nigeria
| | - Ellen M H Mitchell
- Mycobacterial Diseases and Neglected Tropical Diseases Unit, Department of Public Health, Institute of Tropical Medicine, Antwerp, Belgium
| | | | - Danjuma Aboki
- Nasarawa State TB and Leprosy Control Program, Nasarawa, Nigeria
| | | | | | - Daniel Egbule
- Nasarawa State TB and Leprosy Control Program, Nasarawa, Nigeria
| | | | | | | | | | | | - Adebola Lawanson
- National TB and Leprosy Program, Federal Ministry of Health Nigeria, Abuja, Nigeria
| | - Simeon Onyemaechi
- National TB and Leprosy Program, Federal Ministry of Health Nigeria, Abuja, Nigeria
| | - Emperor Ubochioma
- National TB and Leprosy Program, Federal Ministry of Health Nigeria, Abuja, Nigeria
| | | | | | | | | | - Debby Nongo
- United States Agency for International Development (USAID), Abuja, Nigeria
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9
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Segala FV, Nigussa W, Guido G, Kenate B, Facci E, Tsegaye A, Gulo B, Manenti F, Bobosha K, Cotugno S, Asmare AB, Cavallin F, Tilahun M, Miccio M, Abdissa A, Putoto G, Saracino A, Di Gennaro F. Active close contact investigation of tuberculosis through computer-aided detection and stool Xpert MTB/RIF among people living in Oromia Region, Ethiopia (CADOOL Study): protocol for a prospective, cross-sectional study. BMJ Open 2023; 13:e074968. [PMID: 38135314 DOI: 10.1136/bmjopen-2023-074968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2023] Open
Abstract
INTRODUCTION Pulmonary tuberculosis (TB) is an infectious disease with high incidence in low-income countries (LICs); it remains one of the infectious diseases with the highest mortality in the world, especially in LICs. It is crucial to recognise and diagnose TB as soon as possible, but microbiological tests on sputum are not always sensitive enough. New methods for an early diagnosis of TB are needed. In this study, we will investigate the role of two different tests to detect TB in Ethiopia (where the prevalence of TB is high): molecular search for TB in stool samples with Xpert assay and detection of pulmonary TB signs on chest X-rays with CAD4TB technology. METHODS AND ANALYSIS A prospective diagnostic test accuracy study during TB active contact investigation will be conducted. In the referral hospital in Southwest Shoa Zone, Oromia Region, Ethiopia, patients with pulmonary TB and a sputum sample positive for Mycobacterium tuberculosis and household contacts of at least 4 years of age will be enrolled, with a target sample size of 231 patients. Trained staff will label household contacts as 'possible TB' cases or not according to their symptoms; when TB is possible, a stool Xpert and computer-aided detection on chest X-ray will be performed, alongside standard diagnostic methods, assessing the diagnostic accuracy of CAD4TB compared with Xpert MTB/RIF during TB contact investigation and the accuracy of stool Xpert compared with sputum Xpert. ETHICS AND DISSEMINATION This study has been approved by the Oromia Health Bureau Research Ethics Committee (ref no BFO/MBTFH/1-16/100023). All information obtained will be kept confidential. Selected investigators will have access to data, while international partners will sign a dedicated data protection agreement. Eligible participants will receive brief information about the study before being asked to participate and they will provide written informed consent. Results will be disseminated through peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT05818059.
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Affiliation(s)
- Francesco Vladimiro Segala
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy
| | | | | | - Birhanu Kenate
- Health Research Team, Oromia Regional Health Bureau, Addis Ababa, Ethiopia
| | - Enzo Facci
- St Luke Catholic Hospital, Wolisso, Ethiopia
| | - Ademe Tsegaye
- Doctors with Africa CUAMM, Addis Ababa Coordination Office, Addis Ababa, Ethiopia
| | | | | | - Kidist Bobosha
- Mycobacterial Diseases Research, Armauer Hansen Research Institute, Addis Ababa, Ethiopia
| | - Sergio Cotugno
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy
| | | | | | - Melaku Tilahun
- Armauer Hansen Research Institute, Addis Ababa, Ethiopia
| | | | | | | | - Annalisa Saracino
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy
| | - Francesco Di Gennaro
- Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro, Bari, Italy
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10
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Biewer A, Tzelios C, Tintaya K, Roman B, Hurwitz S, Yuen CM, Mitnick CD, Nardell E, Lecca L, Tierney DB, Nathavitharana RR. Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.17.23290110. [PMID: 37292955 PMCID: PMC10246158 DOI: 10.1101/2023.05.17.23290110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Introduction Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. Methods We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. Results In the triage cohort (n=387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n=191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. Conclusions qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.
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Affiliation(s)
- Amanda Biewer
- Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | | | | | | | | | - Courtney M Yuen
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Carole D Mitnick
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Edward Nardell
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | | | - Dylan B Tierney
- Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Massachusetts Department of Public Health, Boston, MA
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11
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Choi SY, Choi A, Baek SE, Ahn JY, Roh YH, Kim JH. Effect of multimodal diagnostic approach using deep learning-based automated detection algorithm for active pulmonary tuberculosis. Sci Rep 2023; 13:19794. [PMID: 37957334 PMCID: PMC10643438 DOI: 10.1038/s41598-023-47146-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/09/2023] [Indexed: 11/15/2023] Open
Abstract
In this study, we developed a model to predict culture test results for pulmonary tuberculosis (PTB) with a customized multimodal approach and evaluated its performance in different clinical settings. Moreover, we investigated potential performance improvements by combining this approach with deep learning-based automated detection algorithms (DLADs). This retrospective observational study enrolled patients over 18 years of age who consecutively visited the level 1 emergency department and underwent chest radiograph and sputum testing. The primary endpoint was positive sputum culture for PTB. We compared the performance of the diagnostic models by replacing radiologists' interpretations of chest radiographs with screening scores calculated through DLAD. The optimal diagnostic model had an area under the receiver operating characteristic curve of 0.924 (95% CI 0.871-0.976) and an area under precision recall curve of 0.403 (95% CI 0.195-0.580) while maintaining a specificity of 81.4% when sensitivity was fixed at 90%. Multicomponent models showed improved performance for detecting PTB when chest radiography interpretation was replaced by DLAD. Multicomponent diagnostic models with DLAD customized for different clinical settings are more practical than traditional methods for detecting patients with PTB. This novel diagnostic approach may help prevent the spread of PTB and optimize healthcare resource utilization in resource-limited clinical settings.
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Affiliation(s)
- So Yeon Choi
- Department of Emergency Medicine, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, Republic of Korea
| | - Arom Choi
- Department of Emergency Medicine, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, Republic of Korea
- Institute for Innovation in Digital Healthcare, Yonsei University, Seodaemun-Gu, 50 Yonsei-Ro, Seoul, Republic of Korea
| | - Song-Ee Baek
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Severance Hospital, Seoul, Republic of Korea
| | - Jin Young Ahn
- Division of Infectious Disease, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yun Ho Roh
- Biostatistics Collaboration Unit, Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ji Hoon Kim
- Department of Emergency Medicine, Yonsei University College of Medicine, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, Republic of Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, Seodaemun-Gu, 50 Yonsei-Ro, Seoul, Republic of Korea.
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12
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Blake SR, Das N, Tadepalli M, Reddy B, Singh A, Agrawal R, Chattoraj S, Shah D, Putha P. Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust. Diagnostics (Basel) 2023; 13:3408. [PMID: 37998543 PMCID: PMC10670411 DOI: 10.3390/diagnostics13223408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/16/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as 'abnormal' or 'normal'. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.
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Affiliation(s)
- Sarah R. Blake
- East Kent Hospitals University NHS Foundation Trust, Ashford TN24 OLZ, UK;
| | - Neelanjan Das
- East Kent Hospitals University NHS Foundation Trust, Ashford TN24 OLZ, UK;
| | - Manoj Tadepalli
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Bhargava Reddy
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Anshul Singh
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Rohitashva Agrawal
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Subhankar Chattoraj
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Dhruv Shah
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
| | - Preetham Putha
- Qure.ai, Mumbai 400063, Maharashtra, India; (M.T.); (B.R.); (A.S.); (R.A.); (D.S.); (P.P.)
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13
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Innes AL, Martinez A, Gao X, Dinh N, Hoang GL, Nguyen TBP, Vu VH, Luu THT, Le TTT, Lebrun V, Trieu VC, Tran NDB, Qin ZZ, Pham HM, Dinh VL, Nguyen BH, Truong TTH, Nguyen VC, Nguyen VN, Mai TH. Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam's District Health Facilities: An Implementation Study. Trop Med Infect Dis 2023; 8:488. [PMID: 37999607 PMCID: PMC10675130 DOI: 10.3390/tropicalmed8110488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/20/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing.
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Affiliation(s)
- Anh L. Innes
- FHI 360 Asia Pacific Regional Office, Bangkok 10330, Thailand
| | | | - Xiaoming Gao
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Nhi Dinh
- FHI 360, Durham, NC 27701, USA; (A.M.); (X.G.); (N.D.)
| | - Gia Linh Hoang
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Bich Phuong Nguyen
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Viet Hien Vu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Tuan Ho Thanh Luu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Thi Thu Trang Le
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Victoria Lebrun
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Van Chinh Trieu
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Nghi Do Bao Tran
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
| | - Zhi Zhen Qin
- Stop TB Partnership, Grand-Saconnex, 1218 Geneva, Switzerland;
| | - Huy Minh Pham
- United States Agency for International Development/Vietnam, Hanoi 10000, Vietnam;
| | - Van Luong Dinh
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Binh Hoa Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Thi Thanh Huyen Truong
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Van Cu Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
| | - Viet Nhung Nguyen
- Vietnam National Lung Hospital, Hanoi 10000, Vietnam; (V.L.D.); (B.H.N.); (T.T.H.T.); (V.C.N.); (V.N.N.)
- Pulmonology Department, University of Medicine and Pharmacy, Vietnam National University, Hanoi 10000, Vietnam
| | - Thu Hien Mai
- FHI 360 Vietnam, Hanoi 10000, Vietnam; (G.L.H.); (T.B.P.N.); (V.H.V.); (T.H.T.L.); (T.T.T.L.); (V.L.); (V.C.T.); (N.D.B.T.); (T.H.M.)
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14
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Yang Y, Xia L, Liu P, Yang F, Wu Y, Pan H, Hou D, Liu N, Lu S. A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm. Front Med (Lausanne) 2023; 10:1195451. [PMID: 37649977 PMCID: PMC10463041 DOI: 10.3389/fmed.2023.1195451] [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: 03/28/2023] [Accepted: 07/24/2023] [Indexed: 09/01/2023] Open
Abstract
Background Chest radiography (chest X-ray or CXR) plays an important role in the early detection of active pulmonary tuberculosis (TB). In areas with a high TB burden that require urgent screening, there is often a shortage of radiologists available to interpret the X-ray results. Computer-aided detection (CAD) software employed with artificial intelligence (AI) systems may have the potential to solve this problem. Objective We validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm. Methods We conducted prospective multicenter clinical research to validate the performance of pulmonary tuberculosis imaging screening software (JF CXR-1). Volunteers under the age of 15 years, both with or without suspicion of pulmonary tuberculosis, were recruited for CXR photography. The software reported a probability score of TB for each participant. The results were compared with those reported by radiologists. We measured sensitivity, specificity, consistency rate, and the area under the receiver operating characteristic curves (AUC) for the diagnosis of tuberculosis. Besides, adverse events (AE) and severe adverse events (SAE) were also evaluated. Results The clinical research was conducted in six general infectious disease hospitals across China. A total of 1,165 participants were enrolled, and 1,161 were enrolled in the full analysis set (FAS). Men accounted for 60.0% (697/1,161). Compared to the results from radiologists on the board, the software showed a sensitivity of 94.2% (95% CI: 92.0-95.8%) and a specificity of 91.2% (95% CI: 88.5-93.2%). The consistency rate was 92.7% (91.1-94.1%), with a Kappa value of 0.854 (P = 0.000). The AUC was 0.98. In the safety set (SS), which consisted of 1,161 participants, 0.3% (3/1,161) had AEs that were not related to the software, and no severe AEs were observed. Conclusion The software for tuberculosis screening based on a convolutional neural network algorithm is effective and safe. It is a potential candidate for solving tuberculosis screening problems in areas lacking radiologists with a high TB burden.
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Affiliation(s)
- Yang Yang
- Department of Tuberculosis, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, China
| | - Lu Xia
- Department of Pulmonary Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital/The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Ping Liu
- Department of Tuberculosis, Shanghai Public Health Clinical Center Affiliated to Fudan University, Shanghai, China
| | - Fuping Yang
- Department of Tuberculosis, Chongqing Public Health Medical Center, Southwest University, Chongqing, China
| | - Yuqing Wu
- Department of Tuberculosis, Jiangxi Chest Hospital, Nanchang, Jiangxi, China
| | - Hongqiu Pan
- Department of Tuberculosis, The Third Hospital of Zhenjiang, Zhenjiang, Jiangsu, China
| | - Dailun Hou
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Ning Liu
- Department of Tuberculosis, Hebei Chest Hospital, Shijiangzhuang, Hebei, China
| | - Shuihua Lu
- Department of Pulmonary Medicine, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital/The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China
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15
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Naidoo J, Shelmerdine SC, -Charcape CFU, Sodhi AS. Artificial Intelligence in Paediatric Tuberculosis. Pediatr Radiol 2023; 53:1733-1745. [PMID: 36707428 PMCID: PMC9883137 DOI: 10.1007/s00247-023-05606-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/07/2022] [Accepted: 01/13/2023] [Indexed: 01/29/2023]
Abstract
Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.
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Affiliation(s)
- Jaishree Naidoo
- Envisionit Deep AI LTD, Coveham House, Downside Bridge Road, Cobham, KT11 3 EP, UK.
| | - Susan Cheng Shelmerdine
- Department of Clinical Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- Great Ormond Street Hospital for Children, UCL Great Ormond Street Institute of Child Health, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK
| | - Carlos F Ugas -Charcape
- Department of Diagnostic Imaging, Instituto Nacional de Salud del Niño San Borja, Lima, Peru
| | - Arhanjit Singh Sodhi
- Department of Computer Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
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16
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Graciaa DS, Schechter MC, Fetalvero KB, Cranmer LM, Kempker RR, Castro KG. Updated considerations in the diagnosis and management of tuberculosis infection and disease: integrating the latest evidence-based strategies. Expert Rev Anti Infect Ther 2023; 21:595-616. [PMID: 37128947 PMCID: PMC10227769 DOI: 10.1080/14787210.2023.2207820] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/24/2023] [Indexed: 05/03/2023]
Abstract
INTRODUCTION Tuberculosis (TB) is a leading infectious cause of global morbidity and mortality, affecting nearly a quarter of the human population and accounting for over 10 million deaths each year. Over the past several decades, TB incidence and mortality have gradually declined, but 2021 marked a threatening reversal of this trend highlighting the importance of accurate diagnosis and effective treatment of all forms of TB. AREAS COVERED This review summarizes advances in TB diagnostics, addresses the treatment of people with TB infection and TB disease including recent evidence for treatment regimens for drug-susceptible and drug-resistant TB, and draws attention to special considerations in children and during pregnancy. EXPERT OPINION Improvements in diagnosis and management of TB have expanded the available options for TB control. Molecular testing has enhanced the detection of TB disease, but better diagnostics are still needed, particularly for certain populations such as children. Novel treatment regimens have shortened treatment and improved outcomes for people with TB. However, important questions remain regarding the optimal management of TB. Work must continue to ensure the potential of the latest developments is realized for all people affected by TB.
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Affiliation(s)
- Daniel S. Graciaa
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Marcos Coutinho Schechter
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Krystle B. Fetalvero
- Angelo King Medical Research Center-De La Salle Medical and Health Science Institute, Cavite, Philippines
- Department of Family and Community Medicine, Calamba Medical Center, Laguna, Philippines
| | - Lisa Marie Cranmer
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Children’s Healthcare of Atlanta, Atlanta, Georgia, USA
| | - Russell R. Kempker
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kenneth G. Castro
- Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
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17
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Mukherjee S, Perveen S, Negi A, Sharma R. Evolution of tuberculosis diagnostics: From molecular strategies to nanodiagnostics. Tuberculosis (Edinb) 2023; 140:102340. [PMID: 37031646 PMCID: PMC10072981 DOI: 10.1016/j.tube.2023.102340] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/12/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023]
Abstract
Tuberculosis has remained a global concern for public health affecting the lives of people for ages. Approximately 10 million people are affected by the disease and 1.5 million succumb to the disease worldwide annually. The COVID-19 pandemic has highlighted the role of early diagnosis to win the battle against such infectious diseases. Thus, advancement in the diagnostic approaches to provide early detection forms the foundation to eradicate and manage contagious diseases like tuberculosis. The conventional diagnostic strategies include microscopic examination, chest X-ray and tuberculin skin test. The limitations associated with sensitivity and specificity of these tests demands for exploring new techniques like probe-based assays, CRISPR-Cas and microRNA detection. The aim of the current review is to envisage the correlation between both the conventional and the newer approaches to enhance the specificity and sensitivity. A significant emphasis has been placed upon nanodiagnostic approaches manipulating quantum dots, magnetic nanoparticles, and biosensors for accurate diagnosis of latent, active and drug-resistant TB. Additionally, we would like to ponder upon a reliable method that is cost-effective, reproducible, require minimal infrastructure and provide point-of-care to the patients.
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Affiliation(s)
| | - Summaya Perveen
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Anjali Negi
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
| | - Rashmi Sharma
- Infectious Diseases Division, CSIR- Indian Institute of Integrative Medicine, Jammu, 180001, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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18
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Geric C, Majidulla A, Tavaziva G, Nazish A, Saeed S, Benedetti A, Khan AJ, Khan FA. Artificial Intelligence-reported chest X-ray findings of culture-confirmed pulmonary tuberculosis in people with and without diabetes. J Clin Tuberc Other Mycobact Dis 2023; 31:100365. [PMID: 37095759 PMCID: PMC10121442 DOI: 10.1016/j.jctube.2023.100365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Objectives We applied computer-aided detection (CAD) software for chest X-ray (CXR) analysis to determine if diabetes affects the radiographic presentation of tuberculosis. Methods From March 2017-July 2018, we consecutively enrolled adults being evaluated for pulmonary tuberculosis in Karachi, Pakistan. Participants had same-day CXR, two sputum mycobacterial cultures, and random blood glucose measurement. We identified diabetes through self-report or glucose >11.1mMol/L. We included participants with culture-confirmed tuberculosis for this analysis. We used linear regression to estimate associations between CAD-reported tuberculosis abnormality score (range 0.00 to 1.00) and diabetes, adjusting for age, body mass index, sputum smear-status, and prior tuberculosis. We also compared radiographic abnormalities between participants with and without diabetes. Results 63/272 (23%) of included participants had diabetes. After adjustment, diabetes was associated with higher CAD tuberculosis abnormality scores (p < 0.001). Diabetes was not associated with frequency of CAD-reported radiographic abnormalities apart from cavitary disease; participants with diabetes were more likely to have cavitary disease (74.6% vs 61.2% p = 0.07), particularly non-upper zone cavitary disease (17% vs 7.8%, p = 0.09). Conclusions CAD analysis of CXR suggests diabetes is associated with more extensive radiographic abnormalities and with greater likelihood of cavities outside upper lung zones.
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19
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Fehr J, Gunda R, Siedner MJ, Hanekom W, Ndung’u T, Grant A, Lippert C, Wong EB. CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities. Int J Tuberc Lung Dis 2023; 27:157-160. [PMID: 36853104 PMCID: PMC9904401 DOI: 10.5588/ijtld.22.0437] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Affiliation(s)
- J. Fehr
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
,Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - R. Gunda
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,Division of Infection and Immunity, University College London, London, UK
| | - M. J. Siedner
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,Harvard Medical School, Boston, MA, USA
,Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA
| | - W. Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
| | - T. Ndung’u
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Division of Infection and Immunity, University College London, London, UK
,HIV Pathogenesis Programme, The Doris Duke Medical Research Institute, University of KwaZulu-Natal, Durban, South Africa
,Ragon Institute of MGH, MIT and Harvard University, Cambridge, MA, USA
| | - A. Grant
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,School of Nursing and Public Health, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
,London School of Hygiene & Tropical Medicine, London, UK
,School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
| | - C. Lippert
- Digital Health & Machine Learning, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
,Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - E. B. Wong
- Africa Health Research Institute, KwaZulu-Natal, South Africa
,Division of Infectious Diseases, University of Alabama at Birmingham, AL, USA
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20
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Evaluación metodológica de las revisiones sistemáticas basadas en la utilización de sistemas de inteligencia artificial en radiografía de tórax. RADIOLOGIA 2023. [DOI: 10.1016/j.rx.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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21
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Soares TR, Oliveira RDD, Liu YE, Santos ADS, Santos PCPD, Monte LRS, Oliveira LMD, Park CM, Hwang EJ, Andrews JR, Croda J. Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: a cross-sectional study. LANCET REGIONAL HEALTH. AMERICAS 2023; 17:100388. [PMID: 36776567 PMCID: PMC9904090 DOI: 10.1016/j.lana.2022.100388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/28/2022] [Accepted: 10/18/2022] [Indexed: 06/18/2023]
Abstract
Background The World Health Organization (WHO) recommends systematic tuberculosis (TB) screening in prisons. Evidence is lacking for accurate and scalable screening approaches in this setting. We aimed to assess the accuracy of artificial intelligence-based chest x-ray interpretation algorithms for TB screening in prisons. Methods We performed prospective TB screening in three male prisons in Brazil from October 2017 to December 2019. We administered a standardized questionnaire, performed a chest x-ray in a mobile unit, and collected sputum for confirmatory testing using Xpert MTB/RIF and culture. We evaluated x-ray images using three algorithms (CAD4TB version 6, Lunit version 3.1.0.0 and qXR version 3) and compared their accuracy. We utilized multivariable logistic regression to assess the effect of demographic and clinical characteristics on algorithm accuracy. Finally, we investigated the relationship between abnormality scores and Xpert semi-quantitative results. Findings Among 2075 incarcerated individuals, 259 (12.5%) had confirmed TB. All three algorithms performed similarly overall with area under the receiver operating characteristic curve (AUC) of 0.88-0.91. At 90% sensitivity, only LunitTB and qXR met the WHO Target Product Profile requirements for a triage test, with specificity of 84% and 74%, respectively. All algorithms had variable performance by age, prior TB, smoking, and presence of TB symptoms. LunitTB was the most robust to this heterogeneity but nonetheless failed to meet the TPP for individuals with previous TB. Abnormality scores of all three algorithms were significantly correlated with sputum bacillary load. Interpretation Automated x-ray interpretation algorithms can be an effective triage tool for TB screening in prisons. However, their specificity is insufficient in individuals with previous TB. Funding This study was supported by the US National Institutes of Health (grant numbers R01 AI130058 and R01 AI149620) and the State Secretary of Health of Mato Grosso do Sul.
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Affiliation(s)
- Thiego Ramon Soares
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
| | - Roberto Dias de Oliveira
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
- Nursing School, State University of Mato Grosso do Sul, Dourados, MS, Brazil
| | - Yiran E. Liu
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Andrea da Silva Santos
- Faculty of Health Sciences of Federal University of Grande Dourados, Dourados, MS, Brazil
| | | | | | | | - Chang Min Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Eui Jin Hwang
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Jason R. Andrews
- Division of Infectious Diseases and Geographic Medicine, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Julio Croda
- Oswaldo Cruz Foundation, Campo Grande, MS, Brazil
- Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, United States of America
- School of Medicine, Federal University of Mato Grosso do Sul, Campo Grande, MS, Brazil
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22
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David PM, Onno J, Keshavjee S, Ahmad Khan F. Conditions required for the artificial-intelligence-based computer-aided detection of tuberculosis to attain its global health potential. Lancet Digit Health 2022; 4:e702-e704. [PMID: 36150780 DOI: 10.1016/s2589-7500(22)00172-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 08/05/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Affiliation(s)
- Pierre-Marie David
- Faculty of Pharmacy, University of Montréal, Montréal, QC, Canada; Observatoire sur les impacts sociétaux de l'IA et du numérique, Quebec City, QC, Canada.
| | - Julien Onno
- Faculty of Pharmacy, University of Montréal, Montréal, QC, Canada; Observatoire sur les impacts sociétaux de l'IA et du numérique, Quebec City, QC, Canada
| | - Salmaan Keshavjee
- Department of Global Health and Social Medicine and Centre for Global Health Delivery, Harvard Medical School, Harvard University, Boston, MA, USA; Division of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA
| | - Faiz Ahmad Khan
- Observatoire sur les impacts sociétaux de l'IA et du numérique, Quebec City, QC, Canada; McGill International TB Centre, Respiratory Epidemiology and Clinical Research Unit, Montréal, QC, Canada; Centre for Outcomes Research and Clinical Evaluation, Research Institute of the McGill University Health Centre, Montréal, QC, Canada
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23
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Kagujje M, Kerkhoff AD, Nteeni M, Dunn I, Mateyo K, Muyoyeta M. The Performance of Computer-Aided Detection Digital Chest X-ray Reading Technologies for Triage of Active Tuberculosis Among Persons With a History of Previous Tuberculosis. Clin Infect Dis 2022; 76:e894-e901. [PMID: 36004409 PMCID: PMC9907528 DOI: 10.1093/cid/ciac679] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/04/2022] [Accepted: 08/19/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Digital chest X-ray (dCXR) computer-aided detection (CAD) technology uses lung shape and texture analysis to determine the probability of tuberculosis (TB). However, many patients with previously treated TB have sequelae, which also distort lung shape and texture. We evaluated the diagnostic performance of 2 CAD systems for triage of active TB in patients with previously treated TB. METHODS We conducted a retrospective analysis of data from a cross-sectional active TB case finding study. Participants ≥15 years, with ≥1 current TB symptom and complete data on history of previous TB, dCXR, and TB microbiological reference (Xpert MTB/RIF) were included. dCXRs were evaluated using CAD4TB (v.7.0) and qXR (v.3.0). We determined the diagnostic accuracy of both systems, overall and stratified by history of TB, using a single threshold for each system that achieved 90% sensitivity and maximized specificity in the overall population. RESULTS Of 1884 participants, 452 (24.0%) had a history of previous TB. Prevalence of microbiologically confirmed TB among those with and without history of previous TB was 12.4% and 16.9%, respectively. Using CAD4TB, sensitivity and specificity were 89.3% (95% CI: 78.1-96.0%) and 24.0% (19.9-28.5%) and 90.5% (86.1-93.3%) and 60.3% (57.4-63.0%) among those with and without previous TB, respectively. Using qXR, sensitivity and specificity were 94.6% (95% CI: 85.1-98.9%) and 22.2% (18.2-26.6%) and 89.7% (85.1-93.2%) and 61.8% (58.9-64.5%) among those with and without previous TB, respectively. CONCLUSIONS The performance of CAD systems as a TB triage tool is decreased among persons previously treated for TB.
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Affiliation(s)
- Mary Kagujje
- Correspondence: M. Kagujje, Centre for Infectious Disease Research in Zambia, Plot 34620 Off Alick Nkhata Road between ERB and FAZ, Mass Media, PO Box 34681, Lusaka, Zambia ()
| | - Andrew D Kerkhoff
- Division of HIV, Infectious Diseases and Global Medicine Zuckerberg, San Francisco General Hospital and Trauma Center, University of California, San Francisco, San Francisco, California, USA
| | - Mutinta Nteeni
- Department of Radiology, Levy Mwanawasa University Teaching Hospital, Lusaka, Zambia
| | - Ian Dunn
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Kondwelani Mateyo
- Department of Internal Medicine, University Teaching Hospital, Lusaka, Zambia
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24
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Lee SY, Ha S, Jeon MG, Li H, Choi H, Kim HP, Choi YR, I H, Jeong YJ, Park YH, Ahn H, Hong SH, Koo HJ, Lee CW, Kim MJ, Kim YJ, Kim KW, Choi JM. Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation. NPJ Digit Med 2022; 5:107. [PMID: 35908091 PMCID: PMC9339006 DOI: 10.1038/s41746-022-00658-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 07/11/2022] [Indexed: 11/24/2022] Open
Abstract
While many deep-learning-based computer-aided detection systems (CAD) have been developed and commercialized for abnormality detection in chest radiographs (CXR), their ability to localize a target abnormality is rarely reported. Localization accuracy is important in terms of model interpretability, which is crucial in clinical settings. Moreover, diagnostic performances are likely to vary depending on thresholds which define an accurate localization. In a multi-center, stand-alone clinical trial using temporal and external validation datasets of 1,050 CXRs, we evaluated localization accuracy, localization-adjusted discrimination, and calibration of a commercially available deep-learning-based CAD for detecting consolidation and pneumothorax. The CAD achieved image-level AUROC (95% CI) of 0.960 (0.945, 0.975), sensitivity of 0.933 (0.899, 0.959), specificity of 0.948 (0.930, 0.963), dice of 0.691 (0.664, 0.718), moderate calibration for consolidation, and image-level AUROC of 0.978 (0.965, 0.991), sensitivity of 0.956 (0.923, 0.978), specificity of 0.996 (0.989, 0.999), dice of 0.798 (0.770, 0.826), moderate calibration for pneumothorax. Diagnostic performances varied substantially when localization accuracy was accounted for but remained high at the minimum threshold of clinical relevance. In a separate trial for diagnostic impact using 461 CXRs, the causal effect of the CAD assistance on clinicians’ diagnostic performances was estimated. After adjusting for age, sex, dataset, and abnormality type, the CAD improved clinicians’ diagnostic performances on average (OR [95% CI] = 1.73 [1.30, 2.32]; p < 0.001), although the effects varied substantially by clinical backgrounds. The CAD was found to have high stand-alone diagnostic performances and may beneficially impact clinicians’ diagnostic performances when used in clinical settings.
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Affiliation(s)
- Sun Yeop Lee
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Sangwoo Ha
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Min Gyeong Jeon
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Hao Li
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Hyunju Choi
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Hwa Pyung Kim
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea
| | - Ye Ra Choi
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hoseok I
- Department of Thoracic and Cardiovascular Surgery, Pusan National University School of Medicine, Busan, Republic of Korea.,Convergence Medical Institute of Technology, Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Yeon Joo Jeong
- Department of Radiology and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Yoon Ha Park
- Department of Internal Medicine, Jawol Health Center, Incheon, Republic of Korea
| | - Hyemin Ahn
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Hyup Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyun Jung Koo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Choong Wook Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Min Jae Kim
- Department of Infectious Disease, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yeon Joo Kim
- Department of Respiratory Allergy Medicine, Nowon Eulji Medical Center, Seoul, Republic of Korea
| | - Kyung Won Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jong Mun Choi
- Department of Medical Artificial Intelligence, Deepnoid, Inc., Seoul, Republic of Korea.
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25
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Garcia-Basteiro AL, White RG, Tait D, Schmidt AC, Rangaka MX, Quaife M, Nemes E, Mogg R, Hill PC, Harris RC, Hanekom WA, Frick M, Fiore-Gartland A, Evans T, Dagnew AF, Churchyard G, Cobelens F, Behr MA, Hatherill M. End-point definition and trial design to advance tuberculosis vaccine development. Eur Respir Rev 2022; 31:220044. [PMID: 35675923 PMCID: PMC9488660 DOI: 10.1183/16000617.0044-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/04/2022] [Indexed: 11/05/2022] Open
Abstract
Tuberculosis (TB) remains a leading infectious cause of death worldwide and the coronavirus disease 2019 pandemic has negatively impacted the global TB burden of disease indicators. If the targets of TB mortality and incidence reduction set by the international community are to be met, new more effective adult and adolescent TB vaccines are urgently needed. There are several new vaccine candidates at different stages of clinical development. Given the limited funding for vaccine development, it is crucial that trial designs are as efficient as possible. Prevention of infection (POI) approaches offer an attractive opportunity to accelerate new candidate vaccines to advance into large and expensive prevention of disease (POD) efficacy trials. However, POI approaches are limited by imperfect current tools to measure Mycobacterium tuberculosis infection end-points. POD trials need to carefully consider the type and number of microbiological tests that define TB disease and, if efficacy against subclinical (asymptomatic) TB disease is to be tested, POD trials need to explore how best to define and measure this form of TB. Prevention of recurrence trials are an alternative approach to generate proof of concept for efficacy, but optimal timing of vaccination relative to treatment must still be explored. Novel and efficient approaches to efficacy trial design, in addition to an increasing number of candidates entering phase 2-3 trials, would accelerate the long-standing quest for a new TB vaccine.
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Affiliation(s)
- Alberto L Garcia-Basteiro
- Centro de Investigação em Sade de Manhiça (CISM), Maputo, Mozambique
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Infecciosas (CIBERINFECT), Barcelona, Spain
| | | | - Dereck Tait
- International AIDS Vaccine Initiative (IAVI) NPC, Cape Town, South Africa
| | | | - Molebogeng X Rangaka
- Institute for Global Health and MRC Clinical Trials Unit at University College London, London, UK
- CIDRI-AFRICA, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Matthew Quaife
- London School of Hygiene and Tropical Medicine, London, UK
| | - Elisa Nemes
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Dept of Pathology, University of Cape Town, Cape Town, South Africa
| | - Robin Mogg
- Takeda Pharmaceutical Company Limited, Cambridge, MA, USA
| | - Philip C Hill
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Rebecca C Harris
- London School of Hygiene and Tropical Medicine, London, UK
- Sanofi Pasteur, Singapore
| | - Willem A Hanekom
- Africa Health Research Institute, KwaZulu-Natal, South Africa
- Division of Infection and Immunity, University College London, London, UK
| | - Mike Frick
- Treatment Action Group, New York, NY, USA
| | - Andrew Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Alemnew F Dagnew
- Bill and Melinda Gates Medical Research Institute, Cambridge, MA, USA
| | - Gavin Churchyard
- The Aurum Institute, Parktown, South Africa
- Vanderbilt University, Nashville, TN, USA
- University of the Witwatersrand, Johannesburg, South Africa
| | - Frank Cobelens
- Dept of Global Health and Amsterdam Institute for Global health and development, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Marcel A Behr
- Dept of Medicine, McGill University; McGill International TB Centre, Montreal, QC, Canada
| | - Mark Hatherill
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine and Division of Immunology, Dept of Pathology, University of Cape Town, Cape Town, South Africa
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Nadkarni P, Merchant SA. Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters: Future insights. Artif Intell Med Imaging 2022; 3:55-69. [DOI: 10.35711/aimi.v3.i3.55] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 04/12/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
Much of the published literature in Radiology-related Artificial Intelligence (AI) focuses on single tasks, such as identifying the presence or absence or severity of specific lesions. Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image. Also, since a diagnosis is rarely achieved through an image alone, radiology AI must be able to employ diverse strategies that consider all available evidence, not just imaging information. Using key imaging and clinical signs will help improve their accuracy and utility tremendously. Employing strategies that consider all available evidence will be a formidable task; we believe that the combination of human and computer intelligence will be superior to either one alone. Further, unless an AI application is explainable, radiologists will not trust it to be either reliable or bias-free; we discuss some approaches aimed at providing better explanations, as well as regulatory concerns regarding explainability (“transparency”). Finally, we look at federated learning, which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models, and quantum computing, still prototypical but potentially revolutionary in its computing impact.
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Affiliation(s)
- Prakash Nadkarni
- College of Nursing, University of Iowa, Iowa City, IA 52242, United States
| | - Suleman Adam Merchant
- Department of Radiology, LTM Medical College & LTM General Hospital, Mumbai 400022, Maharashtra, India
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Tavaziva G, Majidulla A, Nazish A, Saeed S, Benedetti A, Khan AJ, Ahmad Khan F. Diagnostic accuracy of a commercially available, deep learning-based chest X-ray interpretation software for detecting culture-confirmed pulmonary tuberculosis. Int J Infect Dis 2022; 122:15-20. [PMID: 35597555 DOI: 10.1016/j.ijid.2022.05.037] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/12/2022] [Accepted: 05/13/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Few evaluations of computer-aided detection (CAD) software for analyzing chest radiographs for tuberculosis have used mycobacterial culture as the reference standard. METHODS Using data from a prospective study of symptomatic adults and household contacts of persons with tuberculosis who were seeking care in Karachi, we evaluated the accuracy of LUNIT INSIGHT version 3.1.0.0 (LUNIT, South Korea) for detecting pulmonary tuberculosis in the triage use case. The reference standard was liquid culture. We estimated the diagnostic accuracy using three developer-recommended threshold scores for tuberculosis: 15, 30, and 45. RESULTS A total 269 of 2190 (12%) participants had culture-confirmed pulmonary tuberculosis. LUNIT-reported abnormalities of nodule, consolidation, fibrosis, and pleural effusion were more common with culture-confirmed tuberculosis. At the tuberculosis threshold score of 30, sensitivity and specificity were, respectively, 87.7% [95% CI: 83.2-91.4%] and 64.3% [62.1-66.4%]. Sensitivity was similar at scores of 15, 88.1% [95% CI: 83.6-91.7%] and 45, 86.6% [82.0 - 90.5%]; and specificity was 57.9% [55.7-60.2%] and 69.9% [67.8-71.9%], respectively. Sensitivity was lower for smear-negative disease, and specificity was lower with increasing age, previous tuberculosis, and decreasing body mass index. Diabetes and tobacco smoking did not modify accuracy. CONCLUSION In a population where most tuberculosis was smear-positive, LUNIT-reported radiographic abnormalities were associated with culture-confirmed disease. Manufacturer-recommended threshold scores had limited sensitivity.
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Affiliation(s)
- Gamuchirai Tavaziva
- McGill International TB Centre, Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
| | - Arman Majidulla
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Ahsana Nazish
- Indus Hospital and Health Network, Karachi, Pakistan.
| | - Saima Saeed
- Indus Hospital and Health Network, Karachi, Pakistan.
| | - Andrea Benedetti
- McGill International TB Centre, Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada; Departments of Medicine & Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.
| | | | - Faiz Ahmad Khan
- McGill International TB Centre, Centre for Outcomes Research & Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada; Departments of Medicine & Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.
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28
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Nathavitharana RR, Garcia-Basteiro AL, Ruhwald M, Cobelens F, Theron G. Reimagining the status quo: How close are we to rapid sputum-free tuberculosis diagnostics for all? EBioMedicine 2022; 78:103939. [PMID: 35339423 PMCID: PMC9043971 DOI: 10.1016/j.ebiom.2022.103939] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/14/2022] [Accepted: 02/28/2022] [Indexed: 01/26/2023] Open
Abstract
Rapid, accurate, sputum-free tests for tuberculosis (TB) triage and confirmation are urgently needed to close the widening diagnostic gap. We summarise key technologies and review programmatic, systems, and resource issues that could affect the impact of diagnostics. Mid-to-early-stage technologies like artificial intelligence-based automated digital chest X-radiography and capillary blood point-of-care assays are particularly promising. Pitfalls in the diagnostic pipeline, included a lack of community-based tools. We outline how these technologies may complement one another within the context of the TB care cascade, help overturn current paradigms (eg, reducing syndromic triage reliance, permitting subclinical TB to be diagnosed), and expand options for extra-pulmonary TB. We review challenges such as the difficulty of detecting paucibacillary TB and the limitations of current reference standards, and discuss how researchers and developers can better design and evaluate assays to optimise programmatic uptake. Finally, we outline how leveraging the urgency and innovation applied to COVID-19 is critical to improving TB patients' diagnostic quality-of-care.
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Affiliation(s)
- Ruvandhi R. Nathavitharana
- Division of Infectious Diseases, Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, USA
| | - Alberto L. Garcia-Basteiro
- ISGlobal, Hospital Clínic - Universitat de Barcelona, Barcelona, Spain,Centro de Investigação em Saude de Manhiça, Maputo, Mozambique
| | - Morten Ruhwald
- FIND, the global alliance for diagnostics, Geneva, Switzerland
| | - Frank Cobelens
- Department of Global Health and Amsterdam Institute for Global Health and Development, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Grant Theron
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, South Africa,Corresponding author.
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Affiliation(s)
- Madhukar Pai
- From the McGill International TB Centre, McGill School of Population and Global Health, McGill University, Montreal (M.P.); and the Department of Paediatrics and Child Health, South African Medical Research Council Unit on Child and Adolescent Health, University of Cape Town, Cape Town, South Africa (H.J.Z.)
| | - Heather J Zar
- From the McGill International TB Centre, McGill School of Population and Global Health, McGill University, Montreal (M.P.); and the Department of Paediatrics and Child Health, South African Medical Research Council Unit on Child and Adolescent Health, University of Cape Town, Cape Town, South Africa (H.J.Z.)
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Child Contact Case Management-A Major Policy-Practice Gap in High-Burden Countries. Pathogens 2021; 11:pathogens11010001. [PMID: 35055949 PMCID: PMC8780142 DOI: 10.3390/pathogens11010001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/10/2021] [Accepted: 12/15/2021] [Indexed: 01/18/2023] Open
Abstract
The 2021 Global Tuberculosis (TB) report shows slow progress towards closing the pediatric TB detection gap and improving the TB preventive treatment (TPT) coverage among child and adolescent contacts. This review presents the current knowledge around contact case management (CCM) in low-resource settings, with a focus on child contacts, which represents a key priority population for CCM and TPT. Compelling evidence demonstrates that CCM interventions are a key gateway for both TB case finding and identification of those in need of TPT, and their yield and effectiveness should provide a strong rationale for prioritization by national TB programs. A growing body of evidence is now showing that innovative models of care focused on community-based and patient-centered approaches to household contact investigation can help narrow down the CCM implementation gaps that we are currently facing. The availability of shorter and child-friendly TPT regimens for child contacts provide an additional important opportunity to improve TPT acceptability and adherence. Prioritization of TB CCM implementation and adequate resource mobilization by ministries of health, donors and implementing agencies is needed to timely close the gap.
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