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Tan Q, Huang CC, Becerra MC, Calderon R, Contreras C, Lecca L, Jimenez J, Yataco R, Galea JT, Feng JY, Pan SW, Tseng YH, Huang JR, Zhang Z, Murray MB. Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru. Emerg Infect Dis 2024; 30:1115-1124. [PMID: 38781680 PMCID: PMC11138965 DOI: 10.3201/eid3006.231699] [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] [Indexed: 05/25/2024] Open
Abstract
The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.
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Hua D, Nguyen K, Petrina N, Young N, Cho JG, Yap A, Poon SK. Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis. Int J Med Inform 2023; 177:105159. [PMID: 37549498 DOI: 10.1016/j.ijmedinf.2023.105159] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/16/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The global market for AI systems used in lung tuberculosis (TB) detection has expanded significantly in recent years. Verifying their performance across diverse settings is crucial before medical organisations can invest in them and pursue safe, wide-scale deployment. The goal of this research was to synthesise the clinical evidence for the diagnostic accuracy of certified AI products designed for screening TB in chest X-rays (CXRs) compared to a microbiological reference standard. METHODS Four databases were searched between June to September 2022. Data concerning study methodology, system characteristics, and diagnostic accuracy metrics was extracted and summarised. Study bias was evaluated using QUADAS-2 and by examining sources of funding. Forest plots for diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC) curves were constructed for the AI products individually and collectively. RESULTS 10 out of 3642 studies satisfied the review criteria however only 8 were subject to meta-analysis following bias assessment. Three AI products were evaluated with a 95 % confidence interval producing the following pooled estimates for accuracy rankings: qXR v2 (sensitivity of 0.944 [0.887-0.973], specificity of 0.692 [0.549-0.805], DOR of 3.63 [3.17-4.09], Lunit INSIGHT CXR v3.1 (sensitivity of 0.853 [0.787-0.901], specificity of 0.646 [0.627-0.665], DOR of 2.37 [1.96-2.78]), and CAD4TB v3.07 (sensitivity of 0.917 [0.848-0.956], specificity of 0.371 [0.336-0.408], DOR of 1.91 [1.4-2.47]). Overall, the products had a sensitivity of 0.903 (0.859-0.934), specificity of 0.526 (0.409-0.641), and DOR of 2.31 (1.78-2.84). CONCLUSION Current publicly available evidence indicates considerable variability in the diagnostic accuracy of available AI products although overall they have high sensitivity and modest specificity which is improving with time. These preliminary results are limited by the small number of studies and poor coverage for low TB burden settings. More research is needed to expand the clinical evidence base for the performance of AI products.
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Affiliation(s)
- David Hua
- School of Computer Science, The University of Sydney, Australia; Sydney Law School, The University of Sydney, Australia
| | - Khang Nguyen
- School of Computer Science, The University of Sydney, Australia
| | - Neysa Petrina
- School of Computer Science, The University of Sydney, Australia
| | - Noel Young
- Lumus Imaging, Australia; Western Sydney Local Health District, Australia
| | - Jin-Gun Cho
- Sydney Medical School, The University of Sydney, Australia; Lumus Imaging, Australia; Western Sydney Local Health District, Australia
| | - Adeline Yap
- School of Computer Science, The University of Sydney, Australia
| | - Simon K Poon
- School of Computer Science, The University of Sydney, Australia; Western Sydney Local Health District, Australia.
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Glaser N, Bosman S, Madonsela T, van Heerden A, Mashaete K, Katende B, Ayakaka I, Murphy K, Signorell A, Lynen L, Bremerich J, Reither K. Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series. J Med Case Rep 2023; 17:365. [PMID: 37620921 PMCID: PMC10464059 DOI: 10.1186/s13256-023-04097-4] [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: 05/02/2023] [Accepted: 07/21/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Chest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non--tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature. CASE PRESENTATION In this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma. CONCLUSIONS Solely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.
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Affiliation(s)
- Naomi Glaser
- Faculty of Medicine, University of Zürich, Zurich, Switzerland.
- Department of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland.
| | - Shannon Bosman
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Thandanani Madonsela
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | - Alastair van Heerden
- Center for Community Based Research, Human Sciences Research Council, Pietermaritzburg, South Africa
| | | | | | - Irene Ayakaka
- SolidarMed, Partnerships for Health, Maseru, Lesotho
| | - Keelin Murphy
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Aita Signorell
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Lutgarde Lynen
- Institute of Tropical Medicine Antwerp, Antwerp, Belgium
| | - Jens Bremerich
- Department of Radiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Klaus Reither
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
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Ngosa D, Moonga G, Shanaube K, Jacobs C, Ruperez M, Kasese N, Klinkenberg E, Schaap A, Mureithi L, Floyd S, Fidler S, Sichizya V, Maleya A, Ayles H. Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores from a tuberculosis prevalence survey in Zambia and South Africa. BMC Infect Dis 2023; 23:518. [PMID: 37553658 PMCID: PMC10408069 DOI: 10.1186/s12879-023-08460-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/14/2023] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Chest X-rays (CXRs) have traditionally been used to aid the diagnosis of TB-suggestive abnormalities. Using Computer-Aided Detection (CAD) algorithms, TB risk is quantified to assist with diagnostics. However, CXRs capture all other structural abnormalities. Identification of non-TB abnormalities in individuals with CXRs that have high CAD scores but don't have bacteriologically confirmed TB is unknown. This presents a missed opportunity of extending novel CAD systems' potential to simultaneously provide information on other non-TB abnormalities alongside TB. This study aimed to characterize and estimate the prevalence of non-TB abnormalities on digital CXRs with high CAD4TB scores from a TB prevalence survey in Zambia and South Africa. METHODOLOGY This was a cross-sectional analysis of clinical data of participants from the TREATS TB prevalence survey conducted in 21 communities in Zambia and South Africa. The study included individuals aged ≥ 15 years who had high CAD4TB scores (score ≥ 70), but had no bacteriologically confirmed TB in any of the samples submitted, were not on TB treatment, and had no history of TB. Two consultant radiologists reviewed the images for non-TB abnormalities. RESULTS Of the 525 CXRs reviewed, 46.7% (245/525) images were reported to have non-TB abnormalities. About 11.43% (28/245) images had multiple non-TB abnormalities, while 88.67% (217/245) had a single non-TB abnormality. The readers had a fair inter-rater agreement (r = 0.40). Based on anatomical location, non-TB abnormalities in the lung parenchyma (19%) were the most prevalent, followed by Pleura (15.4%), then heart & great vessels (6.1%) abnormalities. Pleural effusion/thickening/calcification (8.8%) and cardiomegaly (5%) were the most prevalent non-TB abnormalities. Prevalence of (2.7%) for pneumonia not typical of pulmonary TB and (2.1%) mass/nodules (benign/ malignant) were also reported. CONCLUSION A wide range of non-TB abnormalities can be identified on digital CXRs among individuals with high CAD4TB scores but don't have bacteriologically confirmed TB. Adaptation of AI systems like CAD4TB as a tool to simultaneously identify other causes of abnormal CXRs alongside TB can be interesting and useful in non-faculty-based screening programs to better link cases to appropriate care.
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Affiliation(s)
- Dennis Ngosa
- Department of Epidemiology and Biostatistics, School of Public Health, The University of Zambia, Lusaka, Zambia.
| | - Given Moonga
- Department of Epidemiology and Biostatistics, School of Public Health, The University of Zambia, Lusaka, Zambia
| | - Kwame Shanaube
- Zambia Aids Related Tuberculosis (ZAMBART), Lusaka, Zambia
| | - Choolwe Jacobs
- Department of Epidemiology and Biostatistics, School of Public Health, The University of Zambia, Lusaka, Zambia
| | - Maria Ruperez
- London School of Hygiene and Tropical Medicine, London, UK
| | - Nkatya Kasese
- Zambia Aids Related Tuberculosis (ZAMBART), Lusaka, Zambia
| | - Eveline Klinkenberg
- London School of Hygiene and Tropical Medicine, London, UK
- Department of Global Health, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Ab Schaap
- Zambia Aids Related Tuberculosis (ZAMBART), Lusaka, Zambia
| | | | - Sian Floyd
- London School of Hygiene and Tropical Medicine, London, UK
| | - Sarah Fidler
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | | | | | - Helen Ayles
- Zambia Aids Related Tuberculosis (ZAMBART), Lusaka, Zambia
- London School of Hygiene and Tropical Medicine, London, UK
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