The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas.

ABSTRACT: Linear and volumetric analysis are the typical methods to measure tumor size. 3D volumetric analysis has risen in popularity; however, this is very time and labor intensive limiting its implementation in clinical practice. This study aims to show that an AI-led approach can shorten the length of time required to conduct 3D volumetric analysis of VS tumors and improve image processing accuracy. From Yale New Haven Hospital and public patient recruitment, 143 MRIs were included in the ground truth dataset. To create the tumor models for the ground truth dataset, an image processing software (Simpleware ScanIP, Synopsys) was used. The helper (DPP V1.0) was trained using proprietary AI- and ML-based algorithms and information. A proof-of-concept AI model achieved a mean DICE score of 0.76 (sta

SUBMITTER: Jester N 

PROVIDER: S-EPMC11836447 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature