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

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 (standard deviation 0.21). After the final testing stage, the model improved to a final mean DICE score of 0.88 (range 0.74-0.93, standard deviation 0.04). Our study has demonstrated an efficient, accurate AI for 3D volumetric analysis of vestibular schwannomas. The use of this AI will enable faster 3D volumetric analysis compared to manual segmentation. Additionally, the overlay function would allow visualization of growth patterns. The tool will be a method of assessing tumor growth and allow clinicians to make more informed decisions.