Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up).

Enhancing Data Completeness in Early Detection Pathway of Prostate Cancer: Integration of a Dashboard-Driven Feedback Tool to Improve Quality of Care.

: Quality assurance in data collection is essential as data quality directly impacts the accuracy and reliability of outcomes. In the context of early detection of prostate cancer, improving data completeness is a key focus for enhancing patient care. This study aimed to evaluate the effectiveness of a data-driven feedback tool, visualized through a dashboard, in improving the completeness of data collection by healthcare professionals.

Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

Objectives: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.

Materials And Methods: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans.

Capturing Usability Problems for People Living With Dementia by Applying the DEMIGNED Principles in Usability Evaluation Methods: Mixed Methods Study.

Background: Dementia-related impairments can cause complex barriers to access, use, and adopt digital health technologies (DHTs). These barriers can contribute to digital health inequities. Therefore, literature-based design principles called DEMIGNED have been developed to support the design and evaluation of DHTs for this rapidly increasing population.