Background: According to the World Bank, Malaysia reported an estimated 97 tuberculosis cases per 100,000 people in 2021. Chest x-ray (CXR) remains the best conventional method for the early detection of pulmonary tuberculosis (PTB) infection. The intervention of artificial intelligence (AI) in PTB diagnosis could efficiently aid human interpreters and reduce health professionals’ work burden. To date, no AI studies have been evaluated in Malaysia. Objective: This study aims to evaluate the performance of Putralytica and Qure.ai software for CXR screening and PTB diagnosis among the Malaysian population. Methods: We will conduct a retrospective case-control study at the Respiratory Medicine Institute, National Cancer Institute, and Sungai Buloh Health Clinic. A total of 1500 CXR images of patients who completed treatments or check-ups will be selected and categorized into three groups: (1) abnormal PTB cases, (2) abnormal non-PTB cases, and (3) normal cases. These CXR images, along with their clinical findings, will be the reference standard in this study. All patient data, including sociodemographic characteristics and clinical history, will be collected prior to screening via Putralytica and Qure.ai software and readers’ interpretation, which are the index tests for this study. Interpretation from all 3 index tests will be compared with the reference standard, and significant statistical analysis will be computed. Results: Data collection is expected to commence in August 2023. It is anticipated that 1 year will be needed to conduct the study. Conclusions: This study will measure the accuracy of Putralytica and Qure.ai software and whether their findings will concur with readers’ interpretation and the reference standard, thus providing evidence toward the effectiveness of implementing AI in the medical setting. International Registered Report Identifier (IRRID): PRR1-10.2196/36121

Background: From the beginning of the COVID‐19 pandemic until mid‐October 2020, Malaysia recorded ~15,000 confirmed cases. But there could be undiagnosed cases due mainly to asymptomatic infections. Seroprevalence studies can better quantify underlying infection from SARS‐CoV‐2 by identifying humoral antibodies against the virus. This study was the first to determine the prevalence of SARS‐CoV‐2 infection in  Malaysia's general population, as well as the proportion of asymptomatic and undiagnosed infections. Methods: This cross‐sectional seroprevalence study with a two‐stage stratified random cluster sampling design included 5,131 representative community dwellers in Malaysia aged ≥1 year. Data collection lasted from 7 August to 11 October 2020 involving venous blood sampling and interviews for history of COVID‐19 symptoms and diagnosis. Previous SARS‐CoV‐2 infection was defined as screened positive using the Wantai SARS‐CoV‐2 Total Antibody enzyme‐linked immunosorbent assay and confirmed positive using the GenScript SARS‐CoV‐2 surrogate Virus Neutralization Test. We performed a complex sampling design analysis, calculating sample weights considering probabilities of selection, non‐response rate and post‐stratification weight. Results: The overall weighted prevalence of SARS‐CoV‐2 infection was 0.49% (95%CI 0.28–0.85) (N = 150,857). Among the estimated population with past infection, around 84.1% (95%CI 58.84–95.12) (N = 126 826) were asymptomatic, and 90.1% (95%CI 67.06–97.58) (N = 135 866) were undiagnosed. Conclusions: Our study revealed a low pre‐variant and pre‐vaccination seroprevalence of SARS‐CoV‐2 infection in Malaysia up to mid‐October 2020, with a considerable proportion of asymptomatic and undiagnosed cases. This led to subsequent adoption of SARS‐CoV‐2 antigen rapid test kits to increase case detection rate and to reduce time to results and infection control measures.