Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography.

Journal: Radiology. Artificial intelligence
PMID: 39503602

Abstract

Purpose To develop a deep learning model for the morphologic measurement of unruptured intracranial aneurysms (UIAs) based on CT angiography (CTA) data and validate its performance using a multicenter dataset. Materials and Methods In this retrospective study, patients with CTA examinations, including those with and without UIAs, in a tertiary referral hospital from February 2018 to February 2021 were included as the training dataset. Patients with UIAs who underwent CTA at multiple centers between April 2021 and December 2022 were included as the multicenter external testing set. An integrated deep learning (IDL) model was developed for UIA detection, segmentation, and morphologic measurement using an nnU-Net algorithm. Model performance was evaluated using the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC), with measurements by senior radiologists serving as the reference standard. The ability of the IDL model to improve performance of junior radiologists in measuring morphologic UIA features was assessed. Results The study included 1182 patients with UIAs and 578 controls without UIAs as the training dataset (median age, 55 years [IQR, 47-62 years], 1012 [57.5%] female) and 535 patients with UIAs as the multicenter external testing set (median age, 57 years [IQR, 50-63 years], 353 [66.0%] female). The IDL model achieved 97% accuracy in detecting UIAs and achieved a DSC of 0.90 (95% CI: 0.88, 0.92) for UIA segmentation. Model-based morphologic measurements showed good agreement with reference standard measurements (all ICCs > 0.85). Within the multicenter external testing set, the IDL model also showed agreement with reference standard measurements (all ICCs > 0.80). Junior radiologists assisted by the IDL model showed significantly improved performance in measuring UIA size (ICC improved from 0.88 [95% CI: 0.80, 0.92] to 0.96 [95% CI: 0.92, 0.97], < .001). Conclusion The developed integrated deep learning model using CTA data showed good performance in UIA detection, segmentation, and morphologic measurement and may be used to assist less experienced radiologists in morphologic analysis of UIAs. Segmentation, CT Angiography, Head/Neck, Aneurysms, Comparative Studies © RSNA, 2024 See also the commentary by Wang in this issue.

Authors

  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Zhenyao Chang
    From the Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China (Y.Y., Z.C., X.N., J.W., H.H., S.W., Q.L.); China National Clinical Research Center for Neurologic Diseases, Beijing, China (Y.Y., Z.C., X.N., J.W., S.W., Q.L.); Unimed Technology (Beijing), Tsinghua Tongfang Science and Technology Mansion, Beijing, China (J.C., W.L.); Beijing Neurosurgical Institution, Capital Medical University, Beijing, China (Z.C., H.H.); and Department of Radiology, University of Washington, Seattle, Wash (C.Z.).
  • Xin Nie
    School of Public Administration, Guangxi University, Nanning 530004, China.
  • Jun Wu
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Jingang Chen
    From the Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China (Y.Y., Z.C., X.N., J.W., H.H., S.W., Q.L.); China National Clinical Research Center for Neurologic Diseases, Beijing, China (Y.Y., Z.C., X.N., J.W., S.W., Q.L.); Unimed Technology (Beijing), Tsinghua Tongfang Science and Technology Mansion, Beijing, China (J.C., W.L.); Beijing Neurosurgical Institution, Capital Medical University, Beijing, China (Z.C., H.H.); and Department of Radiology, University of Washington, Seattle, Wash (C.Z.).
  • Weiqi Liu
    Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
  • Hongwei He
    Taishan Medical University, Tai'an, 271016.
  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Chengcheng Zhu
    Department of Radiology, University of Washington, Seattle, Washington, USA.
  • Qingyuan Liu
    College of Computer and Control Engineering, Northeast Forestry University, Harbin, Heilongjiang Province, China.

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