Colorectal cancer recurrence remains a major challenge after curative resection, and accurate tools for early risk assessment are essential to stratify patients and guide personalized therapeutic planning. We developed MPMRecNet, a dual-stream deep learning…
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Missed periodontitis diagnoses are common, and AI dental radiography systems based on clinical standards can enhance reliable detection. We introduce and evaluate HC-Net+, a deep-learning model that mimics clinical pathways while integrating localized tooth…
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While chronological age is a universal risk predictor across most populations and diseases, distinguishing between biologically older from younger individuals may identify individuals with accelerated or delayed cardiovascular aging. This study presents a deep…
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Artificial intelligence and wearable technology are increasingly used in healthcare and hold significant potential for improving the management of diabetes. Wearable devices enable continuous monitoring and real-time data collection, supporting AI-driven personalized interventions. This…
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Stress, anxiety, and depression represent significant challenges to global public health. Exposure to virtual natural environments, as a convenient and scalable intervention, has shown uncertain effects on healthy adults. This systematic review and meta-analysis…
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Kidney transplantation offers life-extending treatment for patients with end-stage renal disease, yet long-term risks of graft loss and death persist. Traditional prediction models using only baseline data often fail to capture patients’ evolving health…
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Artificial Intelligence has revolutionized healthcare by offering smart services and reducing diagnostic burden, particularly facilitating the identification and segmentation of malignant tissues. However, current task-specific approaches require disease-specific models, while universal foundation models demand…
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Patients with clear cell renal cell carcinoma (ccRCC) face a high risk of recurrence after surgery, but existing clinical tools based on clinicopathological factors or costly molecular profiling often lack precision and clinical feasibility….
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This study presents an ensemble learning approach for automated screening and severity classification of chronic kidney disease (CKD) using polysomnographic (PSG) phenotypes. We analyzed PSG data from 358 subjects (179 CKD, 179 early-CKD) in…
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Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual…
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