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. 2024 Apr:173:111375.
doi: 10.1016/j.ejrad.2024.111375. Epub 2024 Feb 16.

Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography

Huibert C Ruitenbeek  1 , Edwin H G Oei  1 , Bart L Schmahl  1 , Eelke M Bos  2 , Rob J C G Verdonschot  3 , Jacob J Visser  4
Affiliations
Free article

Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography

Huibert C Ruitenbeek et al. Eur J Radiol. 2024 Apr.
Free article

Abstract

Background: Artificial intelligence (AI) applications can facilitate detection of cervical spine fractures on CT and reduce time to diagnosis by prioritizing suspected cases.

Purpose: To assess the effect on time to diagnose cervical spine fractures on CT and diagnostic accuracy of a commercially available AI application.

Materials and methods: In this study (June 2020 - March 2022) with historic controls and prospective evaluation, we evaluated regulatory-cleared AI-software to prioritize cervical spine fractures on CT. All patients underwent non-contrast CT of the cervical spine. The time between CT acquisition and the moment the scan was first opened (DNT) was compared between the retrospective and prospective cohorts. The reference standard for determining diagnostic accuracy was the radiology report created in routine clinical workflow and adjusted by a senior radiologist. Discrepant cases were reviewed and clinical relevance of missed fractures was determined.

Results: 2973 (mean age, 55.4 ± 19.7 [standard deviation]; 1857 men) patients were analyzed by AI, including 2036 retrospective and 938 prospective cases. Overall prevalence of cervical spine fractures was 7.6 %. The DNT was 18 % (5 min) shorter in the prospective cohort. In scans positive for cervical spine fracture according to the reference standard, DNT was 46 % (16 min) shorter in the prospective cohort. Overall sensitivity of the AI application was 89.8 % (95 % CI: 84.2-94.0 %), specificity was 95.3 % (95 % CI: 94.2-96.2 %), and diagnostic accuracy was 94.8 % (95 % CI: 93.8-95.8 %). Negative predictive value was 99.1 % (95 % CI: 98.5-99.4 %) and positive predictive value was 63.0 % (95 % CI: 58.0-67.8 %). 22 fractures were missed by AI of which 5 required stabilizing therapy.

Conclusion: A time gain of 16 min to diagnosis for fractured cases was observed after introducing AI. Although AI-assisted workflow prioritization of cervical spine fractures on CT shows high diagnostic accuracy, clinically relevant cases were missed.

Keywords: Artificial intelligence; Cervical spine; Spinal fractures; Tomography spiral computed.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Authors HR, BS, EMB, RV and EO hereby declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Jacob J. Visser conflicts of interest are the following: Medical advisor AstraZeneca (PINPOINT), Medical advisor NLC Ventures, Medical advisor Noaber Ventures, Medical advisor Contextflow, Medical advisor Quibim, Medical advisor Tegus..

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