滑膜炎
色素沉着绒毛结节性滑膜炎
医学
分割
人工智能
试验装置
矢状面
放射科
类风湿性关节炎
核医学
模式识别(心理学)
计算机科学
内科学
作者
Qizheng Wang,Meiyi Yao,Xinhang Song,Yandong Liu,Xiaoying Xing,Yongye Chen,Fei Zhao,Ke Liu,Xiaoguang Cheng,Shuqiang Jiang,Ning Lang
标识
DOI:10.1016/j.acra.2023.10.036
摘要
To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis.This retrospective study included 376 consecutive patients with pathologically confirmed knee synovitis (rheumatoid arthritis, gouty arthritis, and pigmented villonodular synovitis) from two institutions. A semantic segmentation model was trained on manually annotated sagittal proton density-weighted images. The segmentation results of the regions of interest and patients' sex and age were used to classify knee synovitis after feature processing. Classification by the DL method was compared to the classification performed by radiologists.Data of the 376 patients (mean age, 42 ± 15 years; 216 men) were separated into a training set (n = 233), an internal test set (n = 93), and an external test set (n = 50). The automated segmentation model showed good performance (mean accuracy: 0.99 and 0.99 in the internal and external test sets). On the internal test set, the DL model performed better than the senior radiologist (accuracy: 0.86 vs. 0.79; area under the curve [AUC]: 0.83 vs. 0.79). On the external test set, the DL diagnostic model based on automatic segmentation performed as well or better than senior and junior radiologists (accuracy: 0.79 vs. 0.79 vs. 0.73; AUC: 0.76 vs. 0.77 vs. 0.70).DL models for segmentation of SC and IPFD can accurately classify knee synovitis and aid radiologic diagnosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI