Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds

Background: Detection and localization of cerebral microbleeds (CMBs) is crucial for disease diagnosis and treatment planning. However, CMB detection is labor-intensive, time-consuming, and challenging owing to its visual similarity to mimics. This study aimed to validate the performance of a three-dimensional (3D) deep learning model that not only detects CMBs but also identifies their anatomic location in real-world settings. Methods: A total of 21 patients with 116 CMBs and 12 without CMBs were visited in the neurosurgery outpatient department between January 2023 and October 2023. Three readers, including a board-certified neuroradiologist (reader 1), a resident in radiology (reader 2), and a neurosurgeon (reader 3) independently reviewed SWIs of 33 patients to detect CMBs and categorized their locations into lobar, deep, and infratentorial regions without any AI assistance. After a one-month washout period, the same datasets were redistributed randomly, and readers reviewed them again with the assistance of the 3D deep learning model. A comparison of the diagnostic performance between readers with and without AI assistance was performed. Results: All readers with an AI assistant (reader 1:0.991 [0.930–0.999], reader 2:0.922 [0.881–0.905], and reader 3:0.966 [0.928–0.984]) tended to have higher sensitivity per lesion than readers only (reader 1:0.905 [0.849–0.942], reader 2:0.621 [0.541–0.694], and reader 3:0.871 [0.759–0.935], p = 0.132, 0.017, and 0.227, respectively). In particular, radiology residents (reader 2) showed a statistically significant increase in sensitivity per lesion when using AI. There was no statistically significant difference in the number of FPs per patient for all readers with AI assistant (reader 1: 0.394 [0.152–1.021], reader 2: 0.727 [0.334–1.582], reader 3: 0.182 [0.077–0.429]) and reader only (reader 1: 0.364 [0.159–0.831], reader 2: 0.576 [0.240–1.382], reader 3: 0.121 [0.038–0.383], p = 0.853, 0.251, and 0.157, respectively). Our model accurately categorized the anatomical location of all CMBs. Conclusions: Our model demonstrated promising potential for the detection and anatomical localization of CMBs, although further research with a larger and more diverse population is necessary to establish clinical utility in real-world settings.