Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans

Purpose 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 tria...

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Bibliographic Details
Published in:Neuroradiology 2022-04, Vol.64 (4), p.735-743
Main Authors: Dyer, Tom, Chawda, Sanjiv, Alkilani, Raed, Morgan, Tom Naunton, Hughes, Mike, Rasalingham, Simon
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computed tomography
Diagnostic Neuroradiology
Emergency medical care
Emergency medical services
Head
Head injuries
Hemorrhage
Humans
Imaging
Labels
Medical diagnosis
Medicine
Medicine & Public Health
Neurology
Neuroradiology
Neurosciences
Neurosurgery
Patients
Radiology
Tomography, X-Ray Computed
Triage
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Summary:Purpose 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. Methods 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. Results 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. Conclusion 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.
ISSN:0028-3940
1432-1920
DOI:10.1007/s00234-021-02826-4