基于YOLOv11深度学习模型的X线可疑脊柱结核计算机辅助检测

摘要(中文译文)

脊柱结核（STB），又称波特病或结核性脊柱炎，在一些偏远和资源有限地区仍构成相当大的临床负担，在这些地区，X线成像是初始评估时通常可用的主要手段。然而，早期和中期STB在X线图像上常表现为细微、模棱两可或不典型的结构改变，这可能导致基层医疗机构延迟识别和转诊。对临床怀疑STB但X线表现不明确的病例进行可疑区域定位研究，可为基层医疗机构进一步影像学评估和转诊决策提供支持性信息。本回顾性研究纳入来自三家三级医院的307例患者，所有患者X线表现均不明确且经CT确诊为STB。根据CT确认的受累脊柱节段及其在X线片上的解剖对应关系，在X线图像上建立CT参考的可疑区域标注。基于YOLOv11目标检测框架开发单类别可疑区域定位模型，数据集按患者层面分为训练集和测试集。采用mAP@0.5、mAP@0.5:0.95、精确率、召回率、F1-置信度曲线、精确率-召回率曲线及置信度相关曲线评估模型性能。进一步引入患者层面检出率和漏诊率以评估病例层面的警示性能。为评估CT参考标注与X线图像上可见可疑区域之间的空间对应关系，额外进行了仅基于X线的盲法标注与CT参考标注之间的一致性分析。在测试集上，YOLOv11模型的mAP@0.5为0.7664，mAP@0.5:0.95为0.4520，精确率为0.8358，召回率为0.6215。阈值相关曲线显示不同置信度阈值下精确率与召回率之间存在权衡。在0.25的暂定工作阈值下，患者层面检出率为0.8073，漏诊率为0.1927，表明该模型可在大多数CT确诊阳性测试病例中提供至少一个可疑区域提示。一致性分析显示，随机选取的40例测试病例均可仅基于X线图像标注出至少一个可疑区域。仅基于X线的盲法标注与CT参考标注之间的平均最大IoU为0.7542；34/40例达到最大IoU≥0.5，38/40例达到最大IoU≥0.3。本研究表明，仅以X线图像作为输入，深度学习模型可为X线表现不明确的CT确诊STB病例提供可疑区域定位提示。所提出的方法为资源有限环境下STB的早期风险提示和转诊支持提供了可行的概念验证框架。仍需纳入正常病例和非结核性脊柱疾病对照，以及外部验证和前瞻性评估的进一步研究，以评估其在真实基层医疗环境中的适用性。

Abstract

Spinal tuberculosis (STB), also known as Pott's disease or tuberculous spondylitis, remains a substantial clinical burden in some remote and resource-limited regions, where X-ray imaging is often the primary modality available for initial assessment. However, early- and mid-stage STB frequently presents with subtle, equivocal, or atypical structural changes on X-ray images, which may lead to delayed recognition and referral in primary-care settings. Investigating suspicious-region localization in cases with clinical suspicion of STB but equivocal X-ray findings may provide supportive information for further imaging evaluation and referral decisions in primary-care settings. This retrospective study included 307 patients from three tertiary hospitals, all of whom had equivocal X-ray findings and CT-confirmed STB. CT-referenced suspicious-region annotations were established on X-ray images according to the CT-confirmed involved spinal levels and their anatomical correspondence on radiographs. A single-class suspicious-region localization model was developed based on the YOLOv11 object detection framework, and the dataset was split at the patient level into training and test sets. Model performance was evaluated using mAP@0.5, mAP@0.5:0.95, precision, recall, F1-confidence curves, precision-recall curves, and confidence-related curves. Patient-level detection rate and missed-case rate were further introduced to evaluate case-level alerting performance. To assess the spatial correspondence between CT-referenced annotations and suspicious regions visible on X-ray images, an agreement analysis between X-ray-only blinded annotations and CT-referenced annotations was additionally performed. On the test set, the YOLOv11 model achieved an mAP@0.5 of 0.7664, an mAP@0.5:0.95 of 0.4520, a precision of 0.8358, and a recall of 0.6215. Threshold-related curves showed a trade-off between precision and recall across different confidence thresholds. At the provisional working threshold of 0.25, the patient-level detection rate was 0.8073, with a missed-case rate of 0.1927, indicating that the model could provide at least one suspicious-region prompt in most CT-confirmed positive test cases. The agreement analysis showed that all 40 randomly selected test cases could be annotated with at least one suspicious region based on X-ray images alone. The mean maximum IoU between X-ray-only blinded annotations and CT-referenced annotations was 0.7542; 34/40 cases reached a maxIoU ≥0.5, and 38/40 cases reached a maxIoU ≥0.3. This study shows that, using X-ray images alone as input, a deep learning model can provide suspicious-region localization prompts for CT-confirmed STB cases with equivocal X-ray findings. The proposed approach provides a feasible proof-of-concept framework for early risk prompting and referral support for STB in resource-limited settings. Further studies incorporating normal cases and non-tuberculous spinal disease controls, as well as external validation and prospective evaluation, are required to assess its applicability in real-world primary-care settings.

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