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 tri...
| Published in: | Neuroradiology Vol. 64; no. 4; pp. 735 - 744 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155691305&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155691305 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Apr2022 vid: 64 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 155691305 152877107 155691305 155691305 10.1007/s00234-021-02826-4 155691305 ppf: 735 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans. aug: au: Dyer, Tom Chawda, Sanjiv Alkilani, Raed Morgan, Tom Naunton Hughes, Mike Rasalingham, Simon affil: Behold.ai, 180 Borough high St, SE1 1LB, London, UK sug: subj: Head Injuries Radiography Intracranial Hemorrhage Radiography Cerebral Infarction Radiography Artificial Intelligence Algorithms Evaluation Triage Tomography, X-Ray Computed Neuroradiography Human Validation Studies Retrospective Design Record Review Emergency Patients United Kingdom United States India Multicenter Studies Sensitivity and Specificity ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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