Prostate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging-Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values.

Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.

Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa.

Machine learning for early dynamic prediction of functional outcome after stroke.

Background: Prediction of outcome after stroke is critical for treatment planning and resource allocation but is complicated by fluctuations during the first days after onset. We propose a machine learning model that can provide hourly predictions based on the integration of continuous variables acquired within 72 h of hospital admission.

Methods: We analyzed 2492 admissions for ischemic stroke in the Geneva University Hospital from 01.

Raising awareness of potential biases in medical machine learning: Experience from a Datathon.

Objective: To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods: Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Dynamically Normalized Pupillometry for Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.

Objectives: Delayed cerebral ischemia (DCI) is a major driver of morbidity after aneurysmal subarachnoid hemorrhage (aSAH). Quantitative pupillometry has been shown to be of prognostic value after acute neurological injury. However, the evidence for the use of pupillometric features for the detection of DCI has been conflicting.