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An Interpretable Machine Learning Model Based on Metabolomics for Predicting Plaque Burden in Cryptogenic Stroke.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Cryptogenic stroke represents 25%-40% of ischemic strokes, with many cases harboring unrecognized large artery atherosclerosis (LAA) requiring specific secondary prevention. In this multicenter pilot study, we developed a metabolomics-based machine l...

Artificial Intelligence and Multimodality Data Integration Decipher Tertiary Lymphoid Structure Maturity in Gastric Cancer.

Cancer research
UNLABELLED: Tertiary lymphoid structures (TLS) are critical components of the tumor microenvironment in gastric cancer, but clinical assessment of TLSs is challenging. The development of automated annotation tools for histopathologic slide analysis c...

Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.

Cancer research
UNLABELLED: Indeterminate pulmonary nodules (IPN) are increasingly detected due to increasing health awareness and widespread lung cancer screening, yet distinguishing benign from malignant nodules remains a critical challenge. Emerging evidence sugg...

Spatially Discontinuous Mutation Topographies in Ductal Carcinoma In Situ Reveal Noncompetitive Growth Dynamics.

Cancer research
UNLABELLED: Preinvasive breast cancer, or ductal carcinoma in situ (DCIS), shares many morphologic and genomic features with invasive breast cancer, yet most DCIS tumors remain indolent over decades. In this study, we performed spatial analyses of so...

Effectiveness of artificial intelligence-based diabetic retinopathy screening in primary care and endocrinology settings in Australia: a pragmatic trial.

The British journal of ophthalmology
PURPOSE: To investigate the diagnostic accuracy, feasibility and end-user experiences of an artificial intelligence (AI)-based, automated diabetic retinopathy (DR) screening model in real-world, Australian primary care and endocrinology clinics.

Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.

International journal of cardiology
BACKGROUND: Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This stud...

Automated coronary artery segmentation / tissue characterization and detection of lipid-rich plaque: An integrated backscatter intravascular ultrasound study.

International journal of cardiology
BACKGROUND: Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial...