Abstract Cerebral aneurysm is a life-threatening condition characterized by the formation of a saccular bulge in brain blood vessels, which can rupture and lead to severe complications. One treatment involves inserting a soft, flexible wire (coil) into the aneurysm to promote clotting and sealing. Mediators are often used to simulate tissue ingrowth within the sac to stabilize healing and prevent recurrence. However, quantitative assessment of tissue ingrowth in preclinical models remains labor-intensive, subjective, and poorly standardized, limiting the ability to compare therapeutic strategies and healing mechanisms. We developed a robust machine learning (ML) pipeline based on a Unet + + convolutional neural network (CNN), optimized for segmenting and quantifying tissue ingrowth in a preclinical carotid aneurysm mouse model. The model was trained and validated on 64 high-resolution histological images using 10-fold cross-validation. Image preprocessing included resizing, normalization, and augmentation, while post-processing applied thresholding techniques to CNN-generated heatmaps. Our method achieved Dice coefficients of 94. 58% for sac segmentation and 95. 23% for tissue ingrowth detection, with AUCs of 99. 24% and 96. 78%, respectively. The model’s predictions showed strong agreement with ground truth (R^2=0. 94), supporting its potential for assessing biological stability and informing clinical decisions. In a blinded evaluation against expert annotations, our AI model achieved the highest agreement (Cohen’s κ) among all raters, demonstrating its potential to provide consistent and expert-level tissue ingrowth assessments. A user-friendly graphical interface was developed, to enable non-technical users to perform segmentation and quantify tissue ingrowth. By providing objective, reproducible metrics of intra-aneurysmal healing, this approach supports mechanistic studies of therapeutic efficacy in preclinical aneurysm models and establishes a foundation for standardized evaluation of pro-healing interventions.
Afsari et al. (Fri,) studied this question.