A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning

IF 3.4 3区 医学 Q1 ORTHOPEDICS JOR Spine Pub Date : 2024-05-30 DOI:10.1002/jsp2.1342
Di Zhang, Jiawei Du, Jiaxiao Shi, Yundong Zhang, Siyue Jia, Xingyu Liu, Yu Wu, Yicheng An, Shibo Zhu, Dayu Pan, Wei Zhang, Yiling Zhang, Shiqing Feng
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Abstract

Background

Normalized decision support system for lumbar disc herniation (LDH) will improve reproducibility compared with subjective clinical diagnosis and treatment. Magnetic resonance imaging (MRI) plays an essential role in the evaluation of LDH. This study aimed to develop an MRI-based decision support system for LDH, which evaluates lumbar discs in a reproducible, consistent, and reliable manner.

Methods

The research team proposed a system based on machine learning that was trained and tested by a large, manually labeled data set comprising 217 patients' MRI scans (3255 lumbar discs). The system analyzes the radiological features of identified discs to diagnose herniation and classifies discs by Pfirrmann grade and MSU classification. Based on the assessment, the system provides clinical advice.

Results

Eventually, the accuracy of the diagnosis process reached 95.83%. An 83.5% agreement was observed between the system's prediction and the ground-truth in the Pfirrmann grade. In the case of MSU classification, 95.0% precision was achieved. With the assistance of this system, the accuracy, interpretation efficiency and interrater agreement among surgeons were improved substantially.

Conclusion

This system showed considerable accuracy and efficiency, and therefore could serve as an objective reference for the diagnosis and treatment procedure in clinical practice.

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利用机器学习的全自动腰椎间盘突出症 MRI 指导决策支持系统。
背景:与主观临床诊断和治疗相比,腰椎间盘突出症(LDH)的规范化决策支持系统将提高可重复性。磁共振成像(MRI)在腰椎间盘突出症的评估中起着至关重要的作用。本研究旨在开发一种基于磁共振成像的 LDH 决策支持系统,该系统能以可重复、一致和可靠的方式评估腰椎间盘:研究小组提出了一种基于机器学习的系统,该系统由一个人工标注的大型数据集进行训练和测试,该数据集由 217 名患者的 MRI 扫描(3255 个腰椎间盘)组成。该系统通过分析已识别椎间盘的放射学特征来诊断椎间盘突出症,并根据 Pfirrmann 等级和 MSU 分类对椎间盘进行分类。根据评估结果,系统提供临床建议:结果:诊断过程的准确率最终达到 95.83%。在 Pfirrmann 等级方面,系统预测与地面实况的一致性达到 83.5%。在 MSU 分级方面,精确度达到了 95.0%。在该系统的帮助下,外科医生的准确性、解释效率和医生间的一致性都得到了大幅提高:结论:该系统显示出相当高的准确性和效率,因此可作为临床实践中诊断和治疗程序的客观参考。
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来源期刊
JOR Spine
JOR Spine ORTHOPEDICS-
CiteScore
6.40
自引率
18.90%
发文量
42
审稿时长
10 weeks
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