Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty

Autor: Ki-Bong Park, Moo-Sub Kim, Do-Kun Yoon, Young Dae Jeon
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Orthopaedic Surgery and Research, Vol 19, Iss 1, Pp 1-8 (2024)
Druh dokumentu: article
ISSN: 1749-799X
DOI: 10.1186/s13018-024-05128-6
Popis: Abstract Background Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures. Methods Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases. Results The exact accuracies of the surgeon’s template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size. Conclusion The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.
Databáze: Directory of Open Access Journals
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