Publications by authors named "G D'Assignies"
Objectives: To develop a deep-learning algorithm for anterior cruciate ligament (ACL) tear detection and to compare its accuracy using two external datasets.
Methods: A database of 19,765 knee MRI scans (17,738 patients) issued from different manufacturers and magnetic fields was used to build a deep learning-based ACL tear detector. Fifteen percent showed partial or complete ACL rupture.
Background: Artificial Intelligence (AI)/Machine Learning (ML) applications have been proven efficient to improve diagnosis, to stratify risk, and to predict outcomes in many respective medical specialties, including in orthopaedics.
Challenges And Discussion: Regarding hip and knee reconstruction surgery, AI/ML have not made it yet to clinical practice. In this review, we present sound AI/ML applications in the field of hip and knee degenerative disease and reconstruction.
Article Synopsis
- The study aimed to train a machine-learning model to identify the transition zone of adhesion-related small bowel obstruction (SBO) in CT scans using a dataset of 562 scans from 404 patients.
- Experienced radiologists annotated the transition zones, and a neural network was trained to classify segments of the scans as either containing or not containing a transition zone, achieving an impressive AUROC score of 0.93.
- The results indicated that the model had a high probability of accurately detecting transition zones, especially in the hypogastric region, suggesting potential for automatic detection in clinical practice.