摘要 7 - 基于加拿大脊柱关节炎研究协会(SPARCC)系统的骶髂关节磁共振成像中炎性病变的深度学习分辨方法

Ho Yin Chung, S. C. Chan, Yingying Lin, Peng Cao
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引用次数: 0

摘要

目的 根据磁共振成像(MRI)中的 SPARCC,开发一种用于骶髂关节炎分级的深度学习算法。方法 将 210 名磁共振成像骶髂关节炎患者的共 996 张有炎症病变的图像用于训练和验证。测试组包括 18 名患有磁共振成像骶髂关节炎的参与者和 19 名未患有磁共振成像骶髂关节炎的参与者。测试组中有 154 张图像的炎症病变是由我们之前研究[1]中预先训练好的算法识别的。基本真相由人工勾画的感兴趣区(ROI)定义,包括骶髂关节处的骨髓水肿(BME)。将深度学习管道预测 SPARCC 评分的性能与两位经验丰富的读者的人工判读进行了比较。结果 两名经验丰富的读者的 SPARCC 评分与深度学习管道之间的观察者内可靠性和皮尔逊系数分别为 0.83 和 0.86。识别所有炎症病变、深部病变和强烈病变的灵敏度分别为 0.83、0.79 和 0.81。骶骨和髂骨分割的 Dice 系数分别为 0.82 和 0.80。识别 SI 关节和参考血管的准确率分别为 0.90 和 0.88。结论 人工智能算法在 SPARCC 评分中的表现与经验丰富的读者进行人工评分的结果一致。所提出的深度学习管道首次展示了一种完整的、以 SPARCC 为依据的深度学习方法,可用于 SpA STIR 图像的评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Abstract 7 — Deep Learning Differentiation of Inflammatory Lesions in Sacroiliac Joint MRI Based on Spondyloarthritis Research Consortium of Canada (SPARCC) System
Objective To develop a deep learning algorithm for grading sacroiliitis based on SPARCC in magnetic resonance imaging (MRI). Method A total of 996 images with inflammatory lesions from 210 participants with MRI sacroiliitis were used for training and validation. The testing cohort consisted of 18 participants with and 19 without MRI sacroiliitis. One hundred and fifty four images from the testing cohort had inflammatory lesions identified by a pre-trained algorithm from our previous study[1]. The ground truth was defined by manually outlined regions of interests (ROIs) consisting of bone marrow edema (BME) at the sacroiliac joint. The performance of the deep learning pipeline in predicting the SPARCC score was compared to manual interpretation by two experienced readers. Result The intra-observer reliability and the Pearson coefficient between the SPARCC scores from two experienced readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivities in identifying all inflammatory lesions, deep lesions, and intense lesions were 0.83, 0.79 and 0.81, respectively. The Dice coefficients of the sacrum and ilium segmentation were 0.82 and 0.80, respectively. The accuracies of identifying the SI joint and reference vessel were 0.90 and 0.88, respectively. Conclusion The performance of AI algorithms in SPARCC scoring was compatible with manual scoring by experienced readers. This proposed deep learning pipeline could be the first demonstration of a complete and SPARCC-informed deep-learning approach in scoring STIR images in SpA.
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