深度学习算法在髋关节术后植入物切口分类和定位中的应用。

IF 1.9 3区 医学 Q2 ORTHOPEDICS Skeletal Radiology Pub Date : 2026-01-01 Epub Date: 2024-05-21 DOI:10.1007/s00256-024-04692-6
Jin Rong Tan, Yan Gao, Raghavan Raghuraman, Daniel Ting, Kang Min Wong, Lionel Tim-Ee Cheng, Hong Choon Oh, Siang Hiong Goh, Yet Yen Yan
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引用次数: 0

摘要

研究目的本研究旨在探索利用卷积神经网络检测和定位骨盆前路X光片上植入物切口的可行性:研究涉及两个深度学习模型的开发。最初,利用从一家机构获得的 40191 张骨盆 X 光片创建了一个模型,用于对植入物切口进行图像级分类。射线照片以 6/2/2 的比例分为训练数据集、验证数据集和保持测试数据集。使用测试数据集计算性能指标,包括接收者操作者特征曲线下面积(AUROC)、灵敏度和特异性。此外,还训练了第二个对象检测模型,以定位同一数据集中的植入物切口。在测试数据集中,对分类模型预测为切口阳性的图像生成边界框可视化,作为评估算法有效性的辅助工具:分类模型的准确率为 99.7%,灵敏度为 84.6%,特异性为 99.8%,AUROC 为 0.998(95% CI:0.996, 0.999),AUPRC 为 0.774(95% CI:0.646, 0.880)。在被预测为切口阳性的骨盆 X 光片中,物体检测模型对真阳性图像的定位准确率可达 95.5%,但在 15 个假阳性预测中,错误地生成了 14 个结果:结论:分类模型在检测种植体切迹方面表现出了相当高的准确性,而物体检测模型则有效地定位了切迹。这证明了使用基于深度学习的方法对骨盆X光片上的植入体切口进行分类和定位的概念。
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Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip.

Objective: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs.

Materials and methods: The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity.

Results: The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions.

Conclusion: The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.

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来源期刊
Skeletal Radiology
Skeletal Radiology 医学-核医学
CiteScore
4.40
自引率
9.50%
发文量
253
审稿时长
3-8 weeks
期刊介绍: Skeletal Radiology provides a forum for the dissemination of current knowledge and information dealing with disorders of the musculoskeletal system including the spine. While emphasizing the radiological aspects of the many varied skeletal abnormalities, the journal also adopts an interdisciplinary approach, reflecting the membership of the International Skeletal Society. Thus, the anatomical, pathological, physiological, clinical, metabolic and epidemiological aspects of the many entities affecting the skeleton receive appropriate consideration. This is the Journal of the International Skeletal Society and the Official Journal of the Society of Skeletal Radiology and the Australasian Musculoskelelal Imaging Group.
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