Machine Learning for Vestibular Schwannoma Diagnosis Using Audiometrie Data Alone.

Objective: The aim of this study is to compare machine learning algorithms and established rule-based evaluations in screening audiograms for the purpose of diagnosing vestibular schwannomas. A secondary aim is to assess the performance of rule-based evaluations for predicting vestibular schwannomas using the largest dataset in the literature.

Study Design: Retrospective case-control study.

On the heterogeneity of the femoral enthesis of the human ACL: microscopic anatomy and clinical implications.

Background: Most ruptures of the native anterior cruciate ligament (ACL) and ACL graft occur at, or near, the femoral enthesis, with the posterolateral fibers of the native ligament being especially vulnerable during pivot landings. Characterizing the anatomy of the ACL femoral enthesis may help us explain injury patterns which, in turn, could help guide injury prevention efforts. It may also lead to improved anatomic reconstruction techniques given that the goal of such techniques is to replicate the knee's normal anatomy.

Quantitative comparison of the microscopic anatomy of the human ACL femoral and tibial entheses.

The femoral enthesis of the human anterior cruciate ligament (ACL) is known to be more susceptible to injury than the tibial enthesis. To determine whether anatomic differences might help explain this difference, we quantified the microscopic appearance of both entheses in 15 unembalmed knee specimens using light microscopy, toluidine blue stain and image analysis. The amount of calcified fibrocartilage and uncalcified fibrocartilage, and the ligament entheseal attachment angle were then compared between the femoral and tibial entheses via linear mixed-effects models.