Understanding the role of Social Determinants of Health (SDoH) in pregnancy outcomes is critical for improving maternal and infant health yet extracting SDoH from unstructured electronic health records remains challenging. We trained and evaluated na...
This study uses artificial intelligence (AI) for differentiation between salivary gland tumours (SGT) using digitised Haematoxylin and Eosin stained whole-slide images (WSI). Machine learning (ML) classifiers were developed and tested using 320 scann...
Accurate forecasting of diabetes burden is essential for healthcare planning, resource allocation, and policy-making. While deep learning models have demonstrated superior predictive capabilities, their real-world applicability is constrained by comp...
Pain-related discussions on social media provide valuable insights into how people naturally express and communicate their pain experiences. However, the network structure of these discussions remains poorly understood. This study analyzed 57,000 Red...
In this study, we propose a new method for oral cancer detection using a modified Vanilla Convolutional Neural Network (CNN) architecture with incorporated batch normalization, dropout regularization, and a customized design structure for the convolu...
Dermatofibrosarcoma protuberans (DFSP) is a rare, locally aggressive cutaneous sarcoma in which achieving histologically negative margins is paramount to minimizing recurrence. Mohs micrographic surgery (MMS) has transformed margin control in DFSP by...
While machine learning (ML) approaches are commonly utilized in medical diagnostics, the accuracy of these methods in identifying psoriatic arthritis (PsA) remains uncertain. To evaluate the accuracy of ML approaches in the medical diagnosis of PsA. ...
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...
Point-of-care (POC) devices have grown in popularity due to their ease of use, low cost, and speedy on-site diagnostic capabilities. This study focuses on ketamine detection by colorimetric and lateral flow assays (LFA), with aptamer-based LFA emergi...
BACKGROUND: Abnormal hedgehog (Hh) signaling is linked to preeclampsia (PE). This study aimed to identify Hh-related diagnostic biomarkers for PE and assess the role of immune infiltration.
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