The study's central objective is to harness the power of generative Artificial Intelligence (AI), in particular based on Large Language Models, as a valuable resource for delving deeper into the insights offered by patients with breast cancer (BC) wh...
Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this st...
BACKGROUND: Recently, robotic arms have been incorporated into the implantation of electrodes for deep brain stimulation (DBS).This study aimed to determine the accuracy of brain electrode placement, initial clinical efficacy, and safety profile of t...
BACKGROUND: We aimed to quantify hepatic vessel volumes across chronic liver disease stages and healthy controls using deep learning-based magnetic resonance imaging (MRI) analysis, and assess correlations with biomarkers for liver (dys)function and ...
Interstitial lung disease (ILD) is a common and serious organ manifestation in patients with connective tissue disease (CTD), but it is uncertain whether there is a difference in ILD between symptomatic and asymptomatic patients. Therefore, we conduc...
BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predictin...
BACKGROUND: Identifying bradykinesia is crucial for diagnosing Parkinson's disease (PD). Traditionally, the finger-tapping test has been used, relying on subjective assessments by physicians. Computer vision offers a non-contact and cost-effective al...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 11, 2025
Prostate cancer (PCa) detection and accurate grading remain critical challenges in medical diagnostics. While deep learning has shown promise in medical image analysis, existing computer-aided diagnosis approaches primarily focus on image recognition...
BMC medical informatics and decision making
Aug 11, 2025
BACKGROUND: Understanding early predictors of treatment outcomes allows better outcome prediction and resource allocation for efficient tuberculosis (TB) management.
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.
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