Purpose

Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm.

We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage.

Methods

Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed. Anomaly detection was performed using a weakly supervised ML classifier. Findings were displayed on a voxel-level (heatmap) and pooled to an anomaly score. Thresholds for this score classified patients into i) normal, ii) inconclusive, iii) pathological. Expert-validated radiological reports were considered as ground truth. Test assessment was performed with ROC analysis; inconclusive results were pooled to pathological predictions for accuracy measurements.

Results

During the investigation period 208 patients were referred for head CT of which 111 could be included. Definite ratings into normal/pathological were feasible in 77 (69.4%) patients. Based on anomaly scores, the AUC to differentiate normal from pathological scans was 0.98 (95% CI 0.97–1.00). The sensitivity, specificity, positive and negative predictive values were 100%, 40.6%, 80.6% and 100%, respectively.

Conclusion

Our study demonstrates the potential of a weakly supervised anomaly-detection tool to detect stroke findings in head CT. Definite classification into normal/pathological was made with high accuracy in > 2/3 of patients. Anomaly heatmaps further provide guidance towards pathologies, also in cases with inconclusive ratings.

中文翻译：

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在常规获得的头部 CT 中自动检测缺血性中风和随后的患者分类

目的

先进的机器学习 (ML) 技术可能会通过偏离学习的规范来检测整个病理学范围。

我们研究了弱监督 ML 工具在检测与头部 CT 缺血性卒中相关的特征性发现并提供后续患者分流的效用。

方法

回顾性分析了 2020 年 4 月在三级医院接受非增强头部 CT 且无异常、亚急性或慢性缺血、深部白质腔隙性梗死或高密度血管体征的患者。使用弱监督 ML 分类器进行异常检测。结果显示在体素级别（热图）上，并汇总为异常分数。该分数的阈值将患者分为 i) 正常、ii) 不确定、iii) 病态。经专家验证的放射学报告被视为基本事实。使用 ROC 分析进行测试评估；将不确定的结果汇总到病理预测中以进行准确度测量。

结果

在调查期间，208 名患者被转诊进行头部 CT，其中 111 名可以纳入。对 77 名 (69.4%) 患者进行明确的正常/病理分级是可行的。根据异常评分，区分正常与病理扫描的 AUC 为 0.98（95% CI 0.97–1.00）。敏感性、特异性、阳性和阴性预测值分别为100%、40.6%、80.6%和100%。

结论

我们的研究证明了弱监督异常检测工具在检测头部 CT 中风发现方面的潜力。在 > 2/3 的患者中以高精度进行了明确的正常/病理分类。异常热图进一步提供了对病理的指导，即使在评级不确定的情况下也是如此。