Service de chirurgie orthopédique, hôpital Trousseau, CHRU de Tours, faculté de médecine, université de Tours, Centre-Val-de-Loire, France.
LIFAT (EA6300), école polytechnique universitaire de Tours, 64, avenue Jean-Portalis, 37200 Tours, France.
Orthop Traumatol Surg Res. 2023 Dec;109(8S):103652. doi: 10.1016/j.otsr.2023.103652. Epub 2023 Jun 26.
The possible applications of artificial intelligence (AI) in orthopedic surgery are promising. Deep learning can be utilized in arthroscopic surgery due to the video signal used by computer vision. The intraoperative management of the long head of biceps (LHB) tendon is the subject of a long-standing controversy. The main objective of this study was to model a diagnostic AI capable of determining the healthy or pathological state of the LHB on arthroscopic images. The secondary objective was to create a second diagnostic AI model based on arthroscopic images and the medical, clinical and imaging data of each patient, to determine the healthy or pathological state of the LHB.
The hypothesis of this study was that it was possible to construct an AI model from operative arthroscopic images to aid in the diagnosis of the healthy or pathological state of the LHB, and its analysis would be superior to a human analysis.
Prospective clinical and imaging data from 199 patients were collected and associated with images from a validated protocoled arthroscopic video analysis, called "ground truth", made by the operating surgeon. A model based on a convolutional neural network (CNN) modeled via transfer learning on the Inception V3 model was built for the analysis of arthroscopic images. This model was then coupled to MultiLayer Perceptron (MLP), integrating clinical and imaging data. Each model was trained and tested using supervised learning.
The accuracy of the CNN in diagnosing the healthy or pathological state of the LHB was 93.7% in learning and 80.66% in generalization. Coupled with the clinical data of each patient, the accuracy of the model assembling the CNN and MLP were respectively 77% and 58% in learning and in generalization.
The AI model built from a CNN manages to determine the healthy or pathological state of the LHB with an accuracy rate of 80.66%. An increase in input data to limit overfitting, and the automation of the detection phase by a Mask-R-CNN are ways of improving the model. This study is the first to assess the ability of an AI to analyze arthroscopic images, and its results need to be confirmed by further studies on this subject.
III Diagnostic study.
人工智能（AI）在骨科手术中的应用具有广阔的前景。由于计算机视觉使用视频信号，因此可以将深度学习应用于关节镜手术中。肱二头肌长头腱（LHB）的术中管理一直存在争议。本研究的主要目的是构建一种诊断人工智能（AI）模型，该模型能够根据关节镜图像确定 LHB 的健康或病理状态。次要目标是创建第二个诊断 AI 模型，该模型基于关节镜图像以及每位患者的医学、临床和影像学数据，以确定 LHB 的健康或病理状态。
本研究的假设是，从手术关节镜图像构建 AI 模型来辅助诊断 LHB 的健康或病理状态是可行的，并且其分析结果优于人工分析。
收集了 199 例患者的前瞻性临床和影像学数据，并与手术医生进行的经过验证的关节镜视频分析（称为“真实数据”）的图像相关联。使用基于 Inception V3 模型的迁移学习构建了基于卷积神经网络（CNN）的模型，以分析关节镜图像。然后，该模型与多类感知器（MLP）结合，整合了临床和影像学数据。每个模型都使用监督学习进行训练和测试。
在学习阶段，CNN 诊断 LHB 健康或病理状态的准确率为 93.7%，在泛化阶段的准确率为 80.66%。将每个患者的临床数据与模型相结合后，CNN 和 MLP 组合模型在学习和泛化阶段的准确率分别为 77%和 58%。
从 CNN 构建的 AI 模型能够以 80.66%的准确率确定 LHB 的健康或病理状态。增加输入数据以限制过拟合，以及通过 Mask-R-CNN 实现检测阶段的自动化，都是改进模型的方法。本研究首次评估了 AI 分析关节镜图像的能力，其结果需要通过进一步研究来验证。
III 级诊断研究。
