AI Medical Compendium Journal:
BMJ open

Showing 1 to 10 of 212 articles

Development and validation of a 3-D deep learning system for diabetic macular oedema classification on optical coherence tomography images.

BMJ open
OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.

Early efficacy observation of suspended lower-limb rehabilitation robot-assisted therapy in patients with intensive care unit-acquired weakness: a study protocol for a self-controlled randomised controlled trial.

BMJ open
INTRODUCTION: Intensive care unit-acquired weakness (ICUAW) is a common and severe complication in critically ill patients, associated with high morbidity and poor prognosis. Despite increasing focus on ICUAW, definitive diagnostic and therapeutic st...

Deep learning-based automatic image quality assessment in ultra-widefield fundus photographs.

BMJ open
OBJECTIVE: With a growing need for ultra-widefield fundus (UWF) fundus photographs in clinics and AI development, image quality assessment (IQA) of UWF fundus photographs is an important preceding step for accurate diagnosis and clinical interpretati...

AI-DBS study: protocol for a longitudinal prospective observational cohort study of patients with Parkinson's disease for the development of neuronal fingerprints using artificial intelligence.

BMJ open
INTRODUCTION: Deep brain stimulation (DBS) is a proven effective treatment for Parkinson's disease (PD). However, titrating DBS stimulation parameters is a labourious process and requires frequent hospital visits. Additionally, its current applicatio...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...

Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.

BMJ open
BACKGROUND: Neonatal intestinal diseases often have an insidious onset and can lead to poor outcomes if not identified early. Early assessment of abnormal bowel function is critical for timely intervention and improving prognosis, underscoring the cl...