Artificial Intelligence Medical Compendium

Artificial intelligence (AI) is rapidly transforming the delivery of kidney care through predictive analytics, machine learning, deep learning, and generative AI technologies. To meet this challenge, the American Society of Nephrology convened an AI ... read more 

Bioprocesses for stem cell-based therapeutics are resource- and time-intensive, hindering the generation of sufficient data for machine learning-informed development. We describe a framework combining data augmentation, multivariate regression, and f... read more 

Kawasaki disease (KD) is an acute multisystem vasculitis that represents the leading cause of acquired pediatric heart disease in children aged 1-5 years in developed nations. The diagnosis of KD remains clinically challenging due to its diverse clin... read more 

OBJECTIVES: 1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (Dmax) and volumes of aortic dissection components: true-lumen (TL), circulating false-lumen (CFL), and thrombus (Th) on CT angiography ... read more 

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OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overcome this, the authors have developed a unique sensor-driven, secure, cloud-based scalable data framew... read more 

Di(2-ethylhexyl) phthalate (DEHP), a widely used plasticizer, impairs cardiac development and increases the risk of cardiovascular disease. Astaxanthin (ASX), a natural carotenoid, has recognized cardiovascular benefits, but its protective effects ag... read more 

The detection of soluble arsenic in realgar and its preparations is crucial for toxicity evaluation. Therefore, surface-enhanced Raman spectroscopy (SERS) combined with machine learning was applied for the rapid detection of soluble As3+ in realgar a... read more 

AIM: To develop and validate models that use electronic health record (EHR) data to predict diabetic ketoacidosis (DKA)-related hospitalizations over 90 and 180 days among adults with type 1 diabetes (T1D). METHODS: We used EHR data from adults with ... read more 

Digital twin technology, which enables the creation of patient-specific virtual models, is increasingly applied in interventional cardiology to support personalized procedural planning and risk assessment. This review examines current applications of... read more