通过神经符号建模在CT扫描中检测椎体压缩骨折的内在可解释方法
An Intrinsically Explainable Approach to Detecting Vertebral Compression Fractures in CT Scans via Neurosymbolic Modeling
- Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种结合深度学习椎体分割与基于形状算法的神经符号方法，用于在CT扫描中检测椎体压缩骨折，在保持内在可解释性的同时，性能匹配或超越黑盒模型。
AI中文摘要：
椎体压缩骨折（VCFs）是骨质疏松症常见且可能严重的后果。然而，它们往往未被诊断。机会性筛查，即对主要为其他目的获取的医学影像数据进行自动化分析，是识别未诊断VCFs的一种经济有效的方法。在机会性医疗诊断等高风险场景中，模型可解释性是采用AI建议的关键因素。基于规则的方法具有内在的可解释性，并与临床指南高度一致，但它们不能直接应用于CT扫描等高维数据。为了解决这一差距，我们引入了一种用于CT体积中VCF检测的神经符号方法。所提出的模型结合了用于椎体分割的深度学习（DL）和一个基于形状的算法（SBA），该算法分析显著解剖区域的椎体高度分布。这使得能够定义一个基于高度分布的规则集来检测VCFs。对VerSe19数据集的评估表明，我们的方法在VCF检测中达到了96%的准确率和91%的灵敏度。相比之下，一个黑盒模型DenseNet在同一数据集上达到了95%的准确率和91%的灵敏度。我们的结果表明，我们内在可解释的方法能够匹配或超越黑盒深度神经网络的性能，同时提供关于为何做出特定预测的额外见解。这种透明度可以增强临床医生的信任，从而支持在VCF诊断和治疗规划中做出更明智的决策。
英文摘要：
Vertebral compression fractures (VCFs) are a common and potentially serious consequence of osteoporosis. Yet, they often remain undiagnosed. Opportunistic screening, which involves automated analysis of medical imaging data acquired primarily for other purposes, is a cost-effective method to identify undiagnosed VCFs. In high-stakes scenarios like opportunistic medical diagnosis, model interpretability is a key factor for the adoption of AI recommendations. Rule-based methods are inherently explainable and closely align with clinical guidelines, but they are not immediately applicable to high-dimensional data such as CT scans. To address this gap, we introduce a neurosymbolic approach for VCF detection in CT volumes. The proposed model combines deep learning (DL) for vertebral segmentation with a shape-based algorithm (SBA) that analyzes vertebral height distributions in salient anatomical regions. This allows for the definition of a rule set over the height distributions to detect VCFs. Evaluation of VerSe19 dataset shows that our method achieves an accuracy of 96% and a sensitivity of 91% in VCF detection. In comparison, a black box model, DenseNet, achieved an accuracy of 95% and sensitivity of 91% in the same dataset. Our results demonstrate that our intrinsically explainable approach can match or surpass the performance of black box deep neural networks while providing additional insights into why a prediction was made. This transparency can enhance clinician's trust thus, supporting more informed decision-making in VCF diagnosis and treatment planning.
