Scholarlink

Purpose <p>Timely and accurate diagnosis of anterior cruciate ligament (ACL) tears has important clinical significance. In this study we tried to establish a segmentation and diagnosis model for ACL tear using deep learning and radiomics based on knee CT.</p> Materials and methods <p>Totally 469 patients were collected for ACL segmentation model construction. Among them, 328 patients underwent MRI examination within one week of CT scanning and were used to construct diagnosis model. The segmentation model was trained using deep learning of 3D nnU-Net. After segmentation, a total of 2,264 quantitative radiomics features were extracted from each ACL. The support vector machine (SVM), random forest (RF) and stochastic gradient descent (SGD) were used to construct classification model.</p> Results <p>The 3D nnU-Net segmentation model we constructed achieved high performance in the ACL segmentation with Dice Similarity Coefficient (DSC) of 0.79 in the external validation. In terms of ACL tear diagnosis, the SVM, RF, and SGD models all demonstrated excellent performance. In the external validation, the Area Under the Curve (AUC) were 0.85, 0.86, and 0.81.</p> Conclusions <p>We developed a CT based artificial intelligence system that could perform ACL segmentation and tears diagnosis. It had high accuracy and convenience, and was of great significance in clinical practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Segmentation and diagnosis of anterior cruciate ligament tear using deep learning and radiomics based on knee CT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

查看全文
  • Xiaoli Yu,
  • Qingning Yang,
  • Xingyan Le,
  • Qingbiao Zhang,
  • Yuyin Wang,
  • Junbang Feng,
  • Chuanming Li

摘要

Purpose

Timely and accurate diagnosis of anterior cruciate ligament (ACL) tears has important clinical significance. In this study we tried to establish a segmentation and diagnosis model for ACL tear using deep learning and radiomics based on knee CT.

Materials and methods

Totally 469 patients were collected for ACL segmentation model construction. Among them, 328 patients underwent MRI examination within one week of CT scanning and were used to construct diagnosis model. The segmentation model was trained using deep learning of 3D nnU-Net. After segmentation, a total of 2,264 quantitative radiomics features were extracted from each ACL. The support vector machine (SVM), random forest (RF) and stochastic gradient descent (SGD) were used to construct classification model.

Results

The 3D nnU-Net segmentation model we constructed achieved high performance in the ACL segmentation with Dice Similarity Coefficient (DSC) of 0.79 in the external validation. In terms of ACL tear diagnosis, the SVM, RF, and SGD models all demonstrated excellent performance. In the external validation, the Area Under the Curve (AUC) were 0.85, 0.86, and 0.81.

Conclusions

We developed a CT based artificial intelligence system that could perform ACL segmentation and tears diagnosis. It had high accuracy and convenience, and was of great significance in clinical practice.