20.500.12556/RUL-160094 Assessing accuracy and consistency in intracranial aneurysm sizing human expertise vs. artifcial intelligence Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth of more than 1 mm is considered a surrogate of rupture risk, therefore, this study presents a comprehensive analysis of intracranial aneurysm measurements utilizing a dataset comprising 358 IA from 248 computed tomography angiography (CTA) scans measured by four junior raters and one senior rater. The study explores the variability in sizing assessments by employing both human raters and an Artificial Intelligence (AI) system. Our findings reveal substantial inter- and intra-rater variability among junior raters, contrasting with the lower intra-rater variability observed in the senior rater. Standard deviations of all raters were above the threshold for IA growth (1 mm). Additionally, the study identifies a systemic bias, indicating a tendency for human experts to measure aneurysms smaller than the AI system. Our findings emphasize the challenges in human assessment while also showcasing the capacity of AI technology to improve the precision and reliability of intracranial aneurysm assessments, especially beneficial for junior raters. The potential of AI was particularly evident in the task of monitoring IA at various intervals, where the AI-based approach surpassed junior raters and achieved performance comparable to senior raters. intrarater variability interrater variability deep learning intracranial aneurysms variabilnost ocenjevalcev globoko učenje intrakranialne anevrizme true false true Springer Nature Angleški jezik Slovenski jezik Članek v reviji 2024-08-20 08:23:34 2024-08-20 08:23:41 2025-06-30 11:51:07 0000-00-00 00:00:00 2024 0 0 11 str. 14, art. 16080 2024 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004.8:616-007.64 2045-2322 10.1038/s41598-024-65825-4 204579587 s41598-024-65825-4.pdf s41598-024-65825-4.pdf 1 86071F2CA6E8A6E718825F34521BD901 5b6e3cc914a7410f0b8f6b7a2b7d1c906035329120ade82c89de3bb848a3138a e0d9e085-5ebc-11ef-8f74-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=188557 https://www.nature.com/articles/s41598-024-65825-4 1 8c4eedd8-5ebc-11ef-8f74-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=188556 Fakulteta za elektrotehniko Medicinska fakulteta 0 0 0