학술논문
Artificial Intelligence Radiographic Analysis Tool for Total Knee Arthroplasty.
Document Type
Academic Journal
Author
Bonnin M; Centre Orthopédique Santy, Lyon, France.; Müller-Fouarge F; Deemea, Paris, France.; Estienne T; Deemea, Paris, France.; Bekadar S; Deemea, Paris, France.; Pouchy C; Deemea, Paris, France.; Ait Si Selmi T; Centre Orthopédique Santy, Lyon, France.
Source
Publisher: Taylor and Francis Country of Publication: United States NLM ID: 8703515 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-8406 (Electronic) Linking ISSN: 08835403 NLM ISO Abbreviation: J Arthroplasty Subsets: MEDLINE
Subject
Language
English
Abstract
Background: The postoperative follow-up of a patient after total knee arthroplasty (TKA) requires regular evaluation of the condition of the knee through interpretation of X-rays. This rigorous analysis requires expertize, time, and methodical standardization. Our work evaluated the use of an artificial intelligence tool, X-TKA, to assist surgeons in their interpretation.
Methods: A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons.
Results: The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71° (standard deviation, 1.53°), similar to the surgeon average difference of 1.69° (standard deviation, 1.52°). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1).
Conclusion: This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice.
(Copyright © 2023 Elsevier Inc. All rights reserved.)
Methods: A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons.
Results: The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71° (standard deviation, 1.53°), similar to the surgeon average difference of 1.69° (standard deviation, 1.52°). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1).
Conclusion: This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice.
(Copyright © 2023 Elsevier Inc. All rights reserved.)