Borjali Alireza, Ashkani-Esfahani Soheil, Bhimani Rohan, Guss Daniel, Muratoglu Orhun K, DiGiovanni Christopher W, Varadarajan Kartik Mangudi, Lubberts Bart
Harris Orthopaedics Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, 55 Fruit St. GRJ 1121B, Boston, MA, 02114, USA.
Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA.
Knee Surg Sports Traumatol Arthrosc. 2023 Dec;31(12):6039-6045. doi: 10.1007/s00167-023-07565-y. Epub 2023 Oct 12.
Delayed diagnosis of syndesmosis instability can lead to significant morbidity and accelerated arthritic change in the ankle joint. Weight-bearing computed tomography (WBCT) has shown promising potential for early and reliable detection of isolated syndesmotic instability using 3D volumetric measurements. While these measurements have been reported to be highly accurate, they are also experience-dependent, time-consuming, and need a particular 3D measurement software tool that leads the clinicians to still show more interest in the conventional diagnostic methods for syndesmotic instability. The purpose of this study was to increase accuracy, accelerate analysis time, and reduce interobserver bias by automating 3D volume assessment of syndesmosis anatomy using WBCT scans.
A retrospective study was conducted using previously collected WBCT scans of patients with unilateral syndesmotic instability. One-hundred and forty-four bilateral ankle WBCT scans were evaluated (48 unstable, 96 control). We developed three deep learning models for analyzing WBCT scans to recognize syndesmosis instability. These three models included two state-of-the-art models (Model 1-3D Convolutional Neural Network [CNN], and Model 2-CNN with long short-term memory [LSTM]), and a new model (Model 3-differential CNN LSTM) that we introduced in this study.
Model 1 failed to analyze the WBCT scans (F1 score = 0). Model 2 only misclassified two cases (F1 score = 0.80). Model 3 outperformed Model 2 and achieved a nearly perfect performance, misclassifying only one case (F1 score = 0.91) in the control group as unstable while being faster than Model 2.
In this study, a deep learning model for 3D WBCT syndesmosis assessment was developed that achieved very high accuracy and accelerated analytics. This deep learning model shows promise for use by clinicians to improve diagnostic accuracy, reduce measurement bias, and save both time and expenditure for the healthcare system.
II.
下胫腓联合不稳定的延迟诊断可导致严重的发病率及踝关节关节炎变化加速。负重计算机断层扫描（WBCT）已显示出利用三维容积测量早期且可靠地检测孤立性下胫腓联合不稳定的良好潜力。虽然据报道这些测量高度准确，但它们也依赖经验、耗时，且需要特定的三维测量软件工具，这使得临床医生仍对下胫腓联合不稳定的传统诊断方法更感兴趣。本研究的目的是通过使用WBCT扫描自动进行下胫腓联合解剖结构的三维容积评估来提高准确性、加快分析时间并减少观察者间偏差。
使用先前收集的单侧下胫腓联合不稳定患者的WBCT扫描进行回顾性研究。评估了144例双侧踝关节WBCT扫描（48例不稳定，96例对照）。我们开发了三种用于分析WBCT扫描以识别下胫腓联合不稳定的深度学习模型。这三种模型包括两种先进模型（模型1 - 三维卷积神经网络[CNN]，以及模型2 - 带有长短期记忆[LSTM]的CNN），以及我们在本研究中引入的一种新模型（模型3 - 差分CNN LSTM）。
模型1未能分析WBCT扫描（F1分数 = 0）。模型2仅将两例误分类（F1分数 = 0.80）。模型3优于模型2，实现了近乎完美的性能，仅将对照组中的一例误分类为不稳定（F1分数 = 0.91），同时比模型2更快。
在本研究中，开发了一种用于三维WBCT下胫腓联合评估的深度学习模型，其具有非常高的准确性并加快了分析速度。这种深度学习模型有望供临床医生使用，以提高诊断准确性、减少测量偏差，并为医疗系统节省时间和费用。
II级。
