# Explainable Machine‐Learning Model to Classify Culprit Calcified Carotid Plaque in Embolic Stroke of Undetermined Source

## TL;DR

This study develops an explainable machine learning model to identify dangerous carotid plaques linked to strokes, using features like plaque thickness and fat volume.

## Contribution

The novel contribution is an interpretable ML model using SHAP to classify culprit carotid plaques in ESUS patients, outperforming traditional criteria.

## Key findings

- The ML model achieved ROC-AUC 0.79 and precision-recall-AUC 0.86, outperforming plaque thickness and IPH criteria.
- Plaque thickness >2.6 mm and PVAT volume ≥112 mm³ were identified as key thresholds by SHAP analysis.
- The model used five plaque- and calcification-level features for classification.

## Abstract

Embolic stroke of undetermined source (ESUS) may be associated with carotid artery plaques with <50% stenosis. Plaque vulnerability is multifactorial, possibly related to intraplaque hemorrhage (IPH), lipid‐rich necrotic core, perivascular adipose tissue (PVAT), and calcifications. Machine learning (ML)‐based plaque classification is increasingly popular but often limited in clinical interpretability by black‐box nature. We applied an explainable ML approach, using noncalcified plaque components and calcification features with the SHapley Additive exPlanations (SHAP) framework to classify plaques as culprit or nonculprit.

This was a retrospective, cross‐sectional study. Patients with unilateral anterior circulation ESUS with calcified carotid plaques in neck computed tomography (CT) angiography were analyzed. Calcification‐level features were derived from manual segmentations. Plaque‐level features were assessed by a neuroradiologist and by semi‐automated software. Plaques were classified as culprit if ipsilateral to stroke side. Eight classifiers were benchmarked, and a gradient‐boosted decision tree (CatBoost) was further tuned. SHAP explained model decisions.

Seventy patients yielded 116 calcified plaques (270 calcifications). Model based on five plaque‐ and calcification‐level features achieved ROC‐AUC (receiver operating characteristic area under the curve) 0.79 and precision‐recall‐AUC 0.86, outperforming classification based on plaque thickness ≥3 mm (ROC‐AUC 0.59, p = 0.04) and IPH presence (ROC‐AUC 0.51, p = 0.003). SHAP identified plaque thickness and PVAT volume as the most influential features with potential thresholds of >2.6 mm and ≥112 mm3, respectively.f

ML model trained with noncalcified plaque and calcification features can classify culprit calcified carotid plaque better than conventional criteria. Using clinically interpretable features with SHAP, the model explained its decisions and suggested hypothesis‐generating thresholds.