![]() ![]() ![]() Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. Only 2 included studies had built applications based on the proposed solutions.Ĭonclusions: Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC n=34), sensitivity (n=27), and specificity (n=23). Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Most studies used human radiologist’s report as the reference standard. DL (n=34) was more commonly used than ML (n=7) in the included studies. Results: Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. Methods: In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. ![]() Objective: This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray ). Substantial efforts have been made in utilizing various artificial intelligence–based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. South West Sydney (SWS), School of Clinical MedicineĮmail: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. Asian/Pacific Island Nursing Journal 11 articles.JMIR Bioinformatics and Biotechnology 35 articles.JMIR Biomedical Engineering 69 articles.Journal of Participatory Medicine 80 articles.JMIR Perioperative Medicine 91 articles.JMIR Rehabilitation and Assistive Technologies 206 articles.JMIR Pediatrics and Parenting 287 articles.Interactive Journal of Medical Research 315 articles.JMIR Public Health and Surveillance 1176 articles.Journal of Medical Internet Research 7628 articles. ![]()
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