Behold.ai, 180 Borough high St, London, SE1 1LB, UK.
Department of Radiology, Barking, Havering and Redbridge University Hospitals NHS Trust, Romford, RM7 0AG, UK.
Neuroradiology. 2022 Apr;64(4):735-743. doi: 10.1007/s00234-021-02826-4. Epub 2021 Oct 8.
Non-contrast CT head scans provide rapid and accurate diagnosis of acute head injury; however, increased utilisation of CT head scans makes it difficult to prioritise acutely unwell patients and places pressure on busy emergency departments (EDs). This study validates an AI algorithm to triage patients presenting with Intracranial Haemorrhage (ICH) or Acute Infarct whilst also identifying a subset of patients as Normal, with the potential to function as a rule-out test.
In total, 390 CT head scans were collected from 3 institutions in the UK, US and India. Ground-truth labels were assigned by 3 FRCR consultant radiologists. AI performance, as well as the performance of 3 independent radiologists, was measured against ground-truth labels.
The algorithm showed AUC values of 0.988 (0.978-0.994), 0.933 (0.901-0.961) and 0.939 (0.919-0.958) for ICH, Acute Infarct and Normal, respectively. Sensitivity/specificity for ICH and Acute Infarct were 0.988/0.925 and 0.833/0.927, respectively, compared to 0.907/0.991 and 0.618/0.977 for radiologists. AI rule-out of Normal scans achieved 0.93% negative predictive value (NPV) for the removal of 54.3% of Normal cases, compared to 86.8% NPV for radiologists.
We show our algorithm can provide effective triage of ICH and Acute Infarct to prioritise acutely unwell patients. AI can also benefit clinical accuracy, with the algorithm identifying 91.3% of radiologist false negatives for ICH and 69.1% for Acute Infarct. Rule-out of Normal scans has huge potential for workload management in busy EDs, in this case removing 27.4% of all scans with no acute findings missed.
非对比 CT 头部扫描可快速准确诊断急性头部损伤；然而，由于头部 CT 扫描的使用增加，使得对病情严重的患者进行优先排序变得困难，并给繁忙的急诊科（ED）带来压力。本研究验证了一种人工智能算法，用于对颅内出血（ICH）或急性梗塞患者进行分诊，同时也确定了一部分患者为正常，有可能作为排除测试。
共从英国、美国和印度的 3 家机构收集了 390 例头部 CT 扫描。由 3 位 FRCR 顾问放射科医生对地面真实标签进行分配。根据地面真实标签测量 AI 性能以及 3 位独立放射科医生的性能。
该算法对 ICH、急性梗塞和正常的 AUC 值分别为 0.988（0.978-0.994）、0.933（0.901-0.961）和 0.939（0.919-0.958）。ICH 和急性梗塞的敏感性/特异性分别为 0.988/0.925 和 0.833/0.927，而放射科医生的敏感性/特异性分别为 0.907/0.991 和 0.618/0.977。AI 对正常扫描的排除可实现 0.93%的阴性预测值（NPV），可排除 54.3%的正常病例，而放射科医生的 NPV 为 86.8%。
我们表明，我们的算法可以有效地对 ICH 和急性梗塞进行分诊，以优先处理病情严重的患者。人工智能还可以提高临床准确性，该算法识别出 ICH 放射科医生 91.3%的假阴性和急性梗塞 69.1%的假阴性。正常扫描的排除对繁忙急诊科的工作量管理具有巨大潜力，在这种情况下，可排除 27.4%的所有无急性发现的扫描。
