Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study
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Authors
Wijethilake, Navodini
Connor, Steve
Oviedova, Anna
Ivory, Marina
Burger, Rebecca
De Leon De Sagun, Jeromel
Hitchings, Amanda
Abougamil, Ahmed
Giannis, Theofanis
Syrris, Christoforos
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Issue Date
2026
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Article
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Abstract
OBJECTIVES: Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate a novel computer-assisted reporting tool for clinical decision support during multidisciplinary team meetings (MDTMs) for VS patients. MATERIALS AND METHODS: Our approach exploits deep learning for tumor segmentation, automating tumor volume, and standard linear measurement extraction. We conducted 2 simulated MDTMs with the same 50 patients evaluated in both arms to compare our proposed approach against the standard process, focusing on its impact on preparation time and decision-making. RESULTS: Automated reports provided acceptable information for an expert neuroradiologist in 72% of cases, while the remaining 28% required some revision with manual feature extraction. The segmentation models used in this report generation task achieved Dice scores of 0.9392 ( DISCUSSION: An initial learning curve in interpreting new data measurements is quickly mastered and the enhanced communication of growth patterns and more comprehensive assessments ultimately provides clinicians with the tools to offer patients more personalized care. CONCLUSION: This pilot clinical implementation study highlights the potential benefits of integrating automated measurements into clinical decision-making for VS management.
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JAMIA Open
Volume
9
Issue
1