Automatic bicipital groove identification in arthritic humeri for preoperative planning: A Random Forest Classifier approach.

Autor: Spangenberg GW; Department of Mechanical Engineering, Western University, London, ON, Canada; The Roth McFarlane Hand and Upper Limb Centre, St. Joseph's Hospital, London, ON, Canada. Electronic address: gspangen@uwo.ca., Uddin F; The Roth McFarlane Hand and Upper Limb Centre, St. Joseph's Hospital, London, ON, Canada; Department of Surgery, Western University, London, ON, Canada., Faber KJ; The Roth McFarlane Hand and Upper Limb Centre, St. Joseph's Hospital, London, ON, Canada; Department of Surgery, Western University, London, ON, Canada., Langohr GDG; Department of Mechanical Engineering, Western University, London, ON, Canada; The Roth McFarlane Hand and Upper Limb Centre, St. Joseph's Hospital, London, ON, Canada.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Aug; Vol. 178, pp. 108653. Date of Electronic Publication: 2024 May 25.
DOI: 10.1016/j.compbiomed.2024.108653
Abstrakt: The bicipital groove is an important anatomical feature of the proximal humerus that needs to be identified during surgical planning for procedures such as shoulder arthroplasty and proximal humeral fracture reconstruction. Current algorithms for automatic identification prove ineffective in arthritic humeri due to the presence of osteophytes, reducing their usefulness for total shoulder arthroplasty. Our methodology involves the use of a Random Forest Classifier (RFC) to automatically detect the bicipital groove on segmented computed tomography scans of humeri. We evaluated our model on two distinct test datasets: one comprising non-arthritic humeri and another with arthritic humeri characterized by significant osteophytes. Our model detected the bicipital groove with a mean absolute error of less than 1mm on arthritic humeri, demonstrating a significant improvement over the previous gold standard approach. Successful identification of the bicipital groove with a high degree of accuracy even in arthritic humeri was accomplished. This model is open source and included in the python package shoulder.
Competing Interests: Declaration of competing interest The Authors, their immediate families, and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.
(Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: