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

Although the Universal Salt Iodization (USI) program has been highly successful, it remains relevant due to the continued risk of Iodine Deficiency Disorders (IDDs) in vulnerable groups, such as children and pregnant women. This program empowers the relevant authority to continuously monitor iodine levels in iodized salt. Our study reports on the use of a Salt Iodate Micro-Method Reagent (SIMR) detection kit for this purpose. The kit was validated, with a linearity of 5.0–60.0 mg/Kg, at a detection limit of 6.8 mg/Kg, with excellent recovery ranging from 93.0 to 108.3%, whereas the repeatability, intermediate precision, and reproducibility achieved a mean coefficient of variation (CV) of 5.3%, 6.8%, and 5.9%, respectively. The stability of the reagents used in the kit was tested using freshly prepared iodine standard quality control (QC) samples of 20.0 mg/Kg and 40.0 mg/Kg, all of which were observed to be stable, within the range of the mean ± 2 × (standard deviation, SD), for 10 days. The suitability of the kit was proven when no difference was found in the mean results of 70 salt samples, using a paired t-test and the Bland–Altman plot, compared to the reference method, at a 95% confidence interval (CI). Thus, the SIMR detection kit is a highly feasible alternative method for iodine monitoring, with a fast analysis time, as well as being cost effective, and environmentally friendly.

Hepatitis B vaccination is a cornerstone of global disease prevention, yet susceptibility among vaccinated individuals remains a concern. This study aimed to determine the prevalence and factors associated with hepatitis B susceptibility in vaccinated adults in Malaysia. A cross-sectional analysis was conducted using data from the National Health and Morbidity Survey (NHMS) 2020, involving 4,083 respondents aged 15 years and older. Serological testing for hepatitis B virus (HBV) markers, HBsAg, anti-HBs, and anti-HBc assays was performed. Susceptibility was defined based on serological profiles, and complex sampling logistic regression was employed to identify associated risk factors. Among the participants, 64.9% (2,649/4,083) were susceptible to HBV infection, with a prevalence of 22.9% among vaccinated individuals. Susceptibility was notably higher in young adults aged 15–29 (19.0%), and Malay ethnic group (20.5%). Additionally, individuals with tertiary education (18.9%) and single marital status (20.1%) exhibited elevated susceptibility. Multivariate analysis revealed significant associations with ethnicity, where Chinese (aOR 7.10, 95% CI: 2.67, 18.91) and other ethnic groups (aOR 5.67, 95% CI: 2.03, 15.92) had higher odds of susceptibility compared to Malays. Education level was another strong predictor, with tertiary (aOR 9.82, 95% CI: 3.61, 26.73), secondary (aOR 8.88, 95% CI: 3.42, 23.11), and primary education (aOR 6.96, 95% CI: 2.77, 17.51) showing substantially increased odds relative to no formal education. This study highlights a substantial prevalence of hepatitis B susceptibility among vaccinated adults in Malaysia. Ethnicity and education level emerged as significant factors. These findings underscore the need for targeted interventions, such as booster vaccinations or enhanced screening, particularly for high-risk groups like the Chinese and other minority ethnicities, as well as those with higher education levels. Further research is warranted to explore these associations’ underlying reasons and optimize vaccination strategies in Malaysia.

Evidence on modern contraceptive use among Orang Asli women is limited. This study aimed to assess the prevalence and factors influencing modern contraceptive use among married or cohabiting Orang Asli women of reproductive age (15-49 years). Data from 3249 Orang Asli women in the 2022 Orang Asli Health Survey (OAHS), a cross-sectional study employing a two-stage stratified random sampling design, were analyzed. Complex sample analysis with logistic regression was applied. The study found that 53.3% of Orang Asli women used modern contraceptives. Key factors significantly associated with contraceptive use included residence in remote areas (aOR=1.88; 95% CI = 1.08, 3.27), age 20-34 years (aOR = 2.43; 95% CI = 1.91, 3.09), and having three or more children (aOR = 2.17; 95% CI = 1.75, 2.70). The prevalence of modern contraceptive use among Orang Asli women exceeded Malaysia’s national average and Indigenous populations globally. Improving accessibility and affordability remains critical, particularly for Orang Asli women in urban areas, to reduce disparities.

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 PRR1-10.2196/36121