BACKGROUND: Combination immunotherapies, such as pembrolizumab plus lenvatinib (PL), are commonly used in treatment for unresectable hepatocellular carcinoma (uHCC). However, it remains challenging to predict which patients will benefit from this the...
BACKGROUND: The worldwide rise in dementia creates an urgent need for screening methods that are both sensitive and easy to administer. Dual-task walking-requiring people to walk while performing a second cognitive or motor task-meets these criteria ...
Cancer is a life-threatening disease that affects several human lives all over the world. The classification of cancer severities utilizing histopathological images is vital for effective and timely diagnosis. This always creates a demandable require...
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depre...
The prediction of Cervical Cancer (CC) remains a tough task due to diverse clinical variations and unbalanced data distribution, while good-quality data remains limited. Early CC signs tend to lack distinct characteristics, which makes their precise ...
Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machin...
The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized en...
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...
The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variabilit...
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