使用深度学习的腰椎MRI自动椎体骨质量评分测量:人工智能算法的开发和验证。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY
Clinical Neurology and Neurosurgery Pub Date : 2025-10-01 Epub Date: 2025-08-05 DOI:10.1016/j.clineuro.2025.109094
Nishantha M Jayasuriya, Emily Feng, Karim Rizwan Nathani, Maliya Delawan, Konstantinos Katsos, Ojas Bhagra, Brett A Freedman, Mohamad Bydon
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引用次数: 0

摘要

背景和目的:骨健康是脊柱手术结果的关键决定因素,但由于当前骨质量评估方法的局限性,许多患者在手术前没有进行充分的术前评估。本研究旨在开发和验证一种基于人工智能的算法,该算法可以从常规MRI扫描中预测椎体骨质量(VBQ)评分,从而改善术前对手术预后不良风险患者的识别。方法:本研究利用来自SPIDER挑战数据集的257个腰椎t1加权MRI扫描。选取正中矢状面切片,测量L1-L4椎体信号强度,通过脑脊液信号强度归一化三步程序计算VBQ评分。开发了一个YOLOv8模型来自动定位感兴趣区域和计算VBQ分数。通过47例腰椎手术患者的手工注释对系统进行验证,并使用精度、召回率、平均平均精度、类内相关系数、Pearson相关系数、RMSE和平均误差来评估系统的性能。结果:YOLOv8模型具有较高的椎体检测准确率(precision: 0.9429, recall: 0.9076, mAP@0.5: 0.9403, mAP@[0.5:0.95]: 0.8288)。交互信度较强,ICC值分别为0.95 (human-human)、0.88和0.93 (human-AI)。人类和人工智能测量值之间的VBQ分数的Pearson相关性分别为0.86和0.9,RMSE值分别为0.58和0.42。结论:基于人工智能的算法能准确预测腰椎常规mri的VBQ评分。这种方法有可能加强对骨健康不良患者的早期识别和干预,从而改善手术结果。建议进一步进行外部验证,以确保通用性和临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated vertebral bone quality score measurement on lumbar MRI using deep learning: Development and validation of an AI algorithm.

Background and objectives: Bone health is a critical determinant of spine surgery outcomes, yet many patients undergo procedures without adequate preoperative assessment due to limitations in current bone quality assessment methods. This study aimed to develop and validate an artificial intelligence-based algorithm that predicts Vertebral Bone Quality (VBQ) scores from routine MRI scans, enabling improved preoperative identification of patients at risk for poor surgical outcomes.

Methods: This study utilized 257 lumbar spine T1-weighted MRI scans from the SPIDER challenge dataset. VBQ scores were calculated through a three-step process: selecting the mid-sagittal slice, measuring vertebral body signal intensity from L1-L4, and normalizing by cerebrospinal fluid signal intensity. A YOLOv8 model was developed to automate region of interest placement and VBQ score calculation. The system was validated against manual annotations from 47 lumbar spine surgery patients, with performance evaluated using precision, recall, mean average precision, intraclass correlation coefficient, Pearson correlation, RMSE, and mean error.

Results: The YOLOv8 model demonstrated high accuracy in vertebral body detection (precision: 0.9429, recall: 0.9076, mAP@0.5: 0.9403, mAP@[0.5:0.95]: 0.8288). Strong interrater reliability was observed with ICC values of 0.95 (human-human), 0.88 and 0.93 (human-AI). Pearson correlations for VBQ scores between human and AI measurements were 0.86 and 0.9, with RMSE values of 0.58 and 0.42 respectively.

Conclusion: The AI-based algorithm accurately predicts VBQ scores from routine lumbar MRIs. This approach has potential to enhance early identification and intervention for patients with poor bone health, leading to improved surgical outcomes. Further external validation is recommended to ensure generalizability and clinical applicability.

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来源期刊
Clinical Neurology and Neurosurgery
Clinical Neurology and Neurosurgery 医学-临床神经学
CiteScore
3.70
自引率
5.30%
发文量
358
审稿时长
46 days
期刊介绍: Clinical Neurology and Neurosurgery is devoted to publishing papers and reports on the clinical aspects of neurology and neurosurgery. It is an international forum for papers of high scientific standard that are of interest to Neurologists and Neurosurgeons world-wide.
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