Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.
Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60–69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS usi...
| Publicado en: | Neuroradiology Vol. 67; no. 3; pp. 729 - 743 |
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| Autores principales: | , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184520903&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184520903 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Mar2025 vid: 67 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184520903 179163165 184520903 184520903 10.1007/s00234-024-03449-1 184520903 ppf: 729 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis. aug: au: Łajczak, Paweł Matyja, Jakub Jóźwik, Kamil Nawrat, Zbigniew affil: https://ror.org/0104rcc94 Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, Jordana 18, Mekelweg 5, 40-043,, Zabrze, Poland sug: subj: Neuroma, Acoustic Diagnosis Deep Learning Prediction Models Predictive Value of Tests Magnetic Resonance Imaging Image Interpretation, Computer Assisted Algorithms Human Systematic Review Meta Analysis Confidence Intervals Descriptive Statistics Checklists ab: Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60–69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS using DL. To address this gap, this meta-analysis aims to provide insights into the current state of DL algorithms applied to MR images of VS. Methodology: Following 2020 PRISMA guidelines, a search across four databases was conducted. Inclusion criteria focused on articles using DL for VS MR image segmentation. The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD). Results: The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88–0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs. Discussion: This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed. Conclusion: In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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