Evidence map›Paper›PMID 39503602›Full record

ArticleRadiology. Artificial intelligence2025

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

Yi Yang, Zhenyao Chang, Xin Nie, Jun Wu, Jingang Chen, Weiqi Liu, Hongwei He, Shuo Wang, Chengcheng Zhu, Qingyuan Liu

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Yi YangFrom 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.).
Zhenyao ChangFrom 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.).ORCID 0009-0001-6154-4760
Xin NieFrom 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.).ORCID 0009-0007-1548-414X
Jun WuFrom 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.).
Jingang ChenFrom 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 LiuFrom 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.).ORCID 0009-0002-6221-7136
Hongwei HeFrom 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.).ORCID 0000-0001-6042-7629
Shuo WangFrom 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.).ORCID 0000-0003-4919-5390
Chengcheng Zhu *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.).ORCID 0000-0001-6898-549X
Qingyuan Liu *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.).ORCID 0000-0002-4277-5037

Funding

Investigation of the quantitative intracranial aneurysm wall enhancement and geometric features associated with aneurysm volume growthR01HL162743 · NHLBI · UNIVERSITY OF WASHINGTON · PI Chengcheng Zhu · 2022 to 2026
$2.4M
NHLBI NIH HHS R01 HL162743
6 · The paper itself

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],

Indexed as

Cerebral AngiographyComputed Tomography AngiographyDeep LearningIntracranial AneurysmRadiographic Image Interpretation, Computer-AssistedFemaleHumansMaleMiddle AgedRetrospective StudiesAneurysmsComparative StudiesCT AngiographyHead/NeckSegmentation

Identifiers

PMID39503602
PMCPMC12256305

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.