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Forecasting the Anterior Cruciate Ligament Rupture Patterns
Head: Fernando De la Torre Frade
Contact: Fernando De la Torre Frade
Mailing address:
Carnegie Mellon University
Robotics Institute
211 Smith Hall
Pittsburgh, PA 15213
Associated center(s) / consortia:
 Vision and Autonomous Systems Center (VASC)
Associated lab(s) / group(s):
 Component Analysis
Overview
Complex knee injuries are common, often resulting from multiple forces (e.g. rotational, varus-valgus loading, anterior/posterior displacement). Identification of the specific injury pattern of the Anterior Cruciate Ligament (ACL) and other knee structures using non-invasive methods may improve pre-operative planning and guide treatment, reduce costs and facilitate high-quality patient care. The main goal of this project is to present a classification system based on a set of non-invasive measures and state-of-the-art machine learning techniques to preempt the exact ACL rupture pattern.