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Machine learning-based method to detect capillary stalling using optical coherence tomography.

Joshua Assi¹, Sabina Stefan², Rockwell Tang³

¹Department of Neuroscience, Brown University, Providence, Rhode Island 02912, USA.

Biomedical Optics Express

|March 2, 2026

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This summary is machine-generated.

A new computational tool accurately quantifies capillary stalling in neurological disorders using optical coherence tomography angiography (OCTA) data. This open-source software improves efficiency and reveals novel morphological patterns of stalled capillaries.

Area of Science:

Neuroscience
Biomedical Engineering
Computational Biology

Background:

Capillary stalling is a key factor in neurological disease models.
Current quantification methods for capillary stalling are time-consuming and error-prone.
Existing methods may not fully utilize 3D OCTA data.

Purpose of the Study:

To develop an automated computational method for quantifying capillary stalling.
To improve the accuracy and efficiency of capillary stall analysis.
To investigate the morphological characteristics of stalled capillaries.

Main Methods:

Developed a support vector machine (SVM) classifier using engineered features from time-series OCTA data.
Validated the SVM classifier using 4-fold cross-validation.

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Compared the computational tool's performance against expert annotations.

Main Results:

The SVM classifier achieved high performance with an ROC AUC of 0.978 and PR AUC of 0.700.
Annotation time was reduced from 1 hour to 22 minutes per dataset.
The tool identified 26% more stalling segments than expert annotations, revealing smaller diameter, increased tortuosity, and greater length in stalled segments.

Conclusions:

The developed computational tool significantly enhances the efficiency and accuracy of capillary stall quantification.
The tool facilitates the discovery of morphological patterns associated with capillary stalling.
This open-source software supports further research into neurological disorders and capillary dynamics.