Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800.

The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800.

Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer.

Introduction: Selecting a patient-specific sequencing strategy to maximize survival outcomes is a clinically unmet need for patients with castration-resistant prostate cancer (CRPC). We developed and validated an artificial intelligence-based decision support system (DSS) to guide optimal sequencing strategy selection.

Patients And Methods: Clinicopathological data of 46 covariates were retrospectively collected from 801 patients diagnosed with CRPC at 2 high-volume institutions between February 2004 and March 2021.