Human-in-the-loop machine learning-based quantitative assessment of hemifacial spasm based on volumetric interpolated breath-hold examination MR

Abstract Objectives The purpose of this study was to assess the severity of hemifacial spasm (HFS) through quantitative measures that associated it with neurovascular contact (NVC). Methods We enrolled 108 HFS patients (63 severe and 45 mild cases) and implemented a human-in-the-loop approach to develop a quantitative NVC feature package. This process involved using interactive segmentation on 3D volumetric interpolated breath-hold examination (VIBE) MR images to delineate vascular and nerve structures. From these segmentations, we extracted quantitative NVC features, forming an NVC feature package, and applied a support vector machine model to assess HFS severity. Results Our interactive segmentation technique achieved high accuracy (Dice similarity coefficients of 0.905 ± 0.030 for vascular structures and 0.922 ± 0.086 for nerves). The NVC feature package, comprising distance between vascular structures and nerves, vascular diameter, their ratio, and clinical characteristics, enabled our model to assess HFS severity with an AUC of 0.823 (95% CI: 0.714-0.932, P < .001). Conclusions This study introduced a quantitative approach in understanding the relationship between HFS severity and NVC, using VIBE MR imaging. Our model offers a promising tool for enhancing clinical decision-making and offers deeper insights into the impact of NVCon HFS, aiming to improve patient outcomes. Advances in knowledge Microvascular decompression is well-established as a safe and effective treatment for HFS. However, there is a gap assessing the severity of HFS using quantitative measures that directly link it to NVC. Our method introduced a quantitative and objective alternative for assessing the severity of HFS to addressing this gap.