Validation of an Interpretable AI System for Localizing the SubThalamic Nucleus During DBS Neurosurgery

Accurate localization of the SubThalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery is critical for therapeutic efficacy and is commonly supported by intraoperative MicroElectrode Recordings (MERs). While deep learning approaches have shown promising performance in automatic STN identification, their limited transparency hinders their clinical adoption. In this work, we present an interpretable deep learning pipeline for MERs classification coupled with a structured validation framework aimed at assessing the clinical relevance of the model reasoning. To this aim, the classification output from a patch-based convolutional neural network with self-attention is combined with Grad-CAM relevance maps. Alignment between relevance maps and manual annotations from 3 expert neurologists with 27, 20, and 11 years of experience was quantified using overlap-based metrics, while perceived transparency, usefulness, and trustworthiness were assessed through Likert-scale questionnaires. Results show competitive classification performance (0.92 ± 0.07 AUC) and consistent agreement between automatic explanations and expert reasoning, with a maximum Dice Score of 0.77 ± 0.11, alongside high clinician acceptance and perceived interpretability. These findings suggest that structured, expert-centred validation of XAI can provide a meaningful contribution toward trustworthy AI-assisted decision support systems in intraoperative neurosurgery.