ArticleNeuroradiology2025
Towards clinical implementation of automated segmentation of vestibular schwannomas: a reliability study comparing AI and human performance.
Article in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Models for Radiomics-Based Segmentation of Vestibular Schwannoma on Magnetic Resonance Imaging: A Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled it
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- [Skull base surgery-where do we stand?]HNO · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
purposeTo evaluate the clinimetric reliability of automated vestibular schwannoma (VS) segmentations by a comparison with human inter-observer variability on T1-weighted contrast-enhanced MRI scans.
methodsThis retrospective study employed MR images, including follow-up, from 1,015 patients (median age: 59, 511 men), resulting in 1,856 unique scans. Two nnU-Net models were trained using fivefold cross-validation to create a single-center segmentation model, along with a multi-center model using additional publicly available data. Geometric-based segmentation metrics (e.g. the Dice score) were used to evaluate model performance. To quantitatively assess the clinimetric reliability of the models, automated tumor volumes from a separate test set were compared to human inter-observer variability using the limits of agreement with the mean (LOAM) procedure. Additionally, new agreement limits that include automated annotations are calculated.
resultsBoth models performed comparable to current state-of-the-art VS segmentation models, with median Dice scores of 91.6% and 91.9% for the single and multi-center models, respectively. There is a stark difference in clinimetric performance between both models: automated tumor volumes of the multi-center model fell within human agreement limits in 73% of the cases, compared to 44% for the single-center model. Newly calculated agreement limits including the single-center model, resulted in very high and wide limits. For the multi-center model, the new agreement limits were comparable to human inter-observer variability.
conclusionModels with excellent geometric-based metrics do not necessarily imply high clinimetric reliability, demonstrating the need to clinimetrically evaluate models as part of the clinical implementation process. The multi-center model displayed high reliability, warranting its possible future use in clinical practice. However, caution should be exercised when employing the model for small tumors, as the reliability was found to be volume-dependent.
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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.