Retrospective detection of missed intra-cranial aneurysms on computed tomography angiography using a commercial deep learning algorithm.

Background: The early identification of intracranial aneurysms (IAs) enables risk stratification and the timely initiation of optimal management. This study aimed to identify patients with missed aneurysms for follow-up and possible treatment, and to evaluate the effectiveness of a commercial deep learning algorithm in retrospectively detecting missed IAs on CTA.

Methods: All consecutive head CTA studies of adult patients performed at a single referral center between February 18, 2020, and July 31, 2022, were retrospectively collected. A machine learning algorithm using natural language processing (NLP) classified radiology reports as positive or negative for aneurysms, and a convolutional neural network (CNN) algorithm analyzed the imaging data. Concordant results with the original reports were accepted as ground truth, while discordant cases were reviewed by three neuroradiologists, with majority voting determining the reference standard.

Results: A total of 2,615 head CTA studies were analyzed. the algorithm flagged 34 suspected missed aneurysms, with 67% (23/34) confirmed as true positives by at least two neuroradiologists. This improved detection by 20.9% (23/110) or 0.88% of all studies. Most missed aneurysms were small (≤ 3 mm). There were 4 false negatives, resulting in a sensitivity of 96.36%, specificity of 99.56%, positive predictive value of 90.6%, and negative predictive value of 99.84%.

Conclusion: This study highlights the potential of deep learning systems to detect missed intracranial aneurysms. Although the missed aneurysms in this cohort were predominantly small, follow-up or diagnostic digital subtraction angiography may still be warranted, depending on clinical characteristics and risk factors for aneurysm rupture.