Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason scoring system. The inherently subjective nature of the grading creates variability between pathologists, potentially resulting in suboptimal patient management decisions. These reproducibility challenges extend beyond Gleason scoring to encompass other critical diagnostic and prognostic markers, including cancer volume quantification and detection of cribriform morphology patterns and perineural invasion. Artificial intelligence (AI) applications in digital pathology have emerged as promising solutions for enhancing diagnostic consistency and accuracy, with recent research demonstrating that automated systems can match expert-level performance in prostate biopsy evaluation. Nevertheless, comprehensive validation studies have revealed concerning limitations in model generalisability when deployed across different clinical environments and patient populations. Recent systematic reviews revealed widespread risk-of-bias limitations and insufficient external validation in AI diagnostic studies, highlighting critical needs for accumulated evidence supporting generalisability before clinical implementation. Rigorous external validation with preregistered protocols using independent datasets from diverse clinical settings remains essential to establish the reliability and safety of AI-assisted prostate pathology systems.

This study protocol establishes a framework for the retrospective external validation of an AI system developed for prostate biopsy assessment, to be conducted on the case-control samples of the National Prostate Cancer Register of Sweden, ProMort study (1998-2015). The primary aim is to evaluate the AI model’s diagnostic accuracy and Gleason grading performance using completely independent datasets separate from any model development or previously used validation cohorts. The diversity of the validation samples, spanning multiple geographic regions, temporal collection periods and reference standards, allows evaluation of model robustness across varied clinical contexts. Secondary aims encompass evaluating AI performance in cancer length estimation and detection of cribriform patterns and perineural invasion. This protocol delineates procedures for data collection, reference standard clarification and prespecified statistical analyses, ensuring comprehensive validation and reliable performance assessment. The study design conforms to established reporting guidelines Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Standards for Reporting Diagnostic Accuracy Studies using Artificial Intelligence (STARD-AI), and recognised best practices for AI validation in medical imaging.

Data collection and usage were approved by the Swedish Regional Ethics Review Board and the Swedish Ethical Review Authority (permits 2012/1586-31/1, 2016/613-31/2, 2019-01395, 2019-05220). The study adheres to the Declaration of Helsinki principles, and findings will be made available in open access peer-reviewed publications.

Frailty is a key predictor of adverse surgical outcomes in older adults, contributing to increased postoperative complications, prolonged hospitalisation and delayed recovery. Prehabilitation—targeting improvements in physical function before surgery—can mitigate these risks. However, traditional programmes often face low adherence due to logistical barriers. Integrating smart wearable devices into tele-supervised, home-based prehabilitation may enhance adherence, engagement and clinical outcomes.

This trial protocol describes the PREhabilitation of frail elderly PAtients undergoing majoR surgEry at HOME study with the objective to evaluate the effectiveness of a wearable-enhanced, tele-supervised prehabilitation programme (swSEP) versus standard care (unsupervised prehabilitation, uSEP) on improving preoperative functional capacity and postoperative outcomes in frail older adults undergoing major elective surgery.

This single-centre, prospective, randomised controlled trial will enrol 190 patients aged ≥65 years scheduled for major elective, non-cardiac surgery at Singapore General Hospital. Participants with frailty (Edmonton Frail Scale ≥6) will be randomised 1:1 to either the swSEP group (tele-supervised exercise with Fitbit Inspire 3 monitoring) or the uSEP group (standard physiotherapy education, exercise booklet and inspiratory muscle training if maximal inspiratory pressure 2O). The primary outcome is change in 6 min walk test distance from baseline to 1–3 days presurgery. Secondary outcomes include 30 s sit-to-stand test, handgrip strength, postoperative complications (per American College of Surgeons National Surgical Quality Improvement Program), hospital length of stay, readmissions, five-level version of the EuroQol five-dimensional questionnaire (EQ-5D-5L) and adherence. Data will be analysed using t-tests, analysis of covariance, logistic regression and Cox models, with stratification by baseline nutritional status.

Approved by the SingHealth Institutional Review Board (CIRB Ref: 2024/2242). Trial registered on ClinicalTrials.gov (NCT06633614). Results will be disseminated via peer-reviewed publications and academic conferences. Contact: irb@singhealth.com.sg

ClinicalTrials.gov Identifier: NCT06633614