Posterior Communicating Artery Aneurysm Microsurgery: PComA-CORE, an Anatomy-Informed Explainable AI Framework for...

The posterior communicating artery (PComA) aneurysm is a challenging microsurgical problem due to multiple factors. These include the technical complexity of the surgery itself, potential injury to blood vessels during surgery, recovery of cranial nerve function, and overall neurological outcome after the operation. These are all related to the area where the PComA aneurysm is located, but they have fundamentally different biological determinants. Prior methods of describing aneurysms do not adequately describe how the relationships of the internal carotid artery (ICA) and PComA/P1 configuration influence the proximity of the aneurysm to other important structures such as the perforating arteries, the anterior choroidal artery (AChA), cranial nerve III (CN III), and the surgical corridor. We created PComA-CORE, an artificial intelligence-based framework designed to evaluate whether the elements of experienced microsurgeons' thought processes can be measured individually while still maintaining temporally valid predictions, human interpretability, and explicit estimates of predictive uncertainty. Methods: Using a highly detailed database of clinical, radiographic, anatomical, intraoperative, and longitudinal data from 687 adult patients who underwent microsurgical clipping of PComA aneurysms over the period 1997-2026, we applied PComA-CORE to predict separately: Microsurgical Complexity (C); Oculomotor Recovery (O); Neurovascular Preservation Risk (R); and Expected 90-Day Functional Outcome (E). The models used cases from 1997-2020 (n = 564) for development and cases from 2021-2026 (n = 123) for temporal evaluation. Several architectures, including penalized regression, machine-learning techniques, interpretable machine learning, and ensembles, were compared using nested cross-validation, discrimination metrics, calibration metrics, decision-curve analysis, explainability measures, uncertainty-aware prediction, inter-observer reproducibility, and model-to-score distillation. Results: Four discrete predictive architectures were identified by PComA-CORE. PComA-C was found to be highly dependent upon anatomy because the specific geometric characteristics of individual vascular segments and the presence or incorporation of branches around the aneurysm strongly influenced temporal predictions. PComA-R was found to behave as a distributed susceptibility phenotype based on neurovascular attributes rather than a deterministic injury model and achieved a temporal AUC of 0.703. Among patients with preoperative CN III palsy, PComA-O identified that recovery primarily depended upon the time course of neurological dysfunction and structural deformation of the affected nerve. Temporal validation was not feasible given the small number of recent non-recovery events. Conversely, PComA-E showed that global functional outcome continued to depend predominantly upon clinical neurological severity, with a temporally evaluated penalized model achieving an AUC of 0.878. Uncertainty-aware predictions indicated that some cases would benefit from greater caution in interpretation. High-resolution anatomical phenotypes demonstrated good inter-observer reproducibility. Score distillation demonstrated that simplification preserved predictive information, but did so differently depending on the endpoint. Conclusions: The problem of predicting the consequences of clipping a PComA aneurysm is multidimensional and does not exist as a single "risk" prediction problem. Technical complexity, neurovascular vulnerability, neural recovery, and global disability each exist within distinct predictive spaces and require different levels of anatomical detail and/or computational complexity. PComA-CORE establishes a human-supervised framework to transform expert microsurgical thought processes into explicit, reproducible, uncertainty-aware, and clinically interpretable representations. While prospective multicenter validation will be needed prior to clinical use, it has the potential to establish a basis for explainable AI, precision cerebrovascular neurosurgery, anatomy-informed risk stratification, and clinically interpretable decision-support systems in complex aneurysm surgery.