Advancements in opportunistic intracranial aneurysm screening: The impact of a deep learning algorithm on radiologists' analysis of T2-weighted cranial MRI

• Published in: Journal of stroke and cerebrovascular diseases Vol. 33; no. 12; p. 108014
• Main Authors: Teodorescu, Bianca, Gilberg, Leonard, Koç, Ali Murat, Goncharov, Andrei, Berclaz, Luc M, Wiedemeyer, Christian, Guzel, Hamza Eren, Ataide, Elmer Jeto Gomes
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.12.2024
• Subjects: Adult; Aged; AI Detection; Decision Support; Deep Learning; Female; Humans; Image Interpretation, Computer-Assisted; Intracranial Aneurysm; Intracranial Aneurysm - diagnostic imaging; Magnetic Resonance Angiography; Magnetic Resonance Imaging; Male; Middle Aged; Observer Variation; Opportunistic Screening; Predictive Value of Tests; Radiologists; Reproducibility of Results; Intracranial Aneurysm; Magnetic Resonance Imaging; Opportunistic Screening; Decision Support; Deep Learning; AI Detection
• ISSN: 1052-3057; 1532-8511; 1532-8511
• DOI: 10.1016/j.jstrokecerebrovasdis.2024.108014

•Unruptured Intracranial Aneurysms (UIAs) are common in humans and pose an increasing risk of rupture with age, potentially resulting in fatality.•UIAs are often underreported in routine T2-weighted imaging (T2WI).•We developed a Deep Learning Algorithm (DLA) that enhanced radiologists' performance in detecting UIAs during routine T2WI reporting.•Artificial Intelligence (AI) can be utilized for opportunistic screening of UIAs with high sensitivity. (1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in vascular anatomy. However, T2-Weighted Magnetic Resonance Imaging (T2WI) is a standard sequence utilized for the majority of outpatient head scans. By employing a sequence such as T2WI, there is a possible shift towards early detection of UIAs through opportunistic screening. Here, we assess a Deep Learning Algorithm (DLA) developed to assist radiologists in identifying and reporting UIAs on T2WI in a routine clinical setting. (2) Methods: A DLA was trained on a set of 110 patients undergoing an MR head scan with the gold standard set by two radiology experts. Eight radiologists were given a cohort of 50 cranial T2WI studies and asked for a routine report. After a 10-day washout period, they reviewed the same cases randomized and supported by the DLA predictions. We assessed changes in sensitivity, specificity, accuracy, and false positives. (3) Results: During routine reporting, the models’ assistance improved the sensitivity of the eight participants by an average of 36.19 and the accuracy by 10.00 percentage points. (4) Conclusion: Our results indicate the potential benefit of deep learning to improve radiologists' detection of UIAs during routine reporting. From this, we can infer that the combination of T2WI with our DLA supports opportunistic screening, suggesting potential approaches for future research and application.