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

Capillary stalling has emerged as an important mechanistic and potential therapeutic target in mouse models of several neurological disorders. Time-series optical coherence tomography angiography (OCTA) has been used to rapidly detect capillary stalling over hundreds of capillaries in 3D and can be used to study this phenomenon in a research setting. However, existing methods for quantifying capillary stalls are labor-intensive, prone to errors, and may be limited by their reliance on 2D representations of inherently 3D data. To address these limitations, we developed a computational approach based on a support vector machine (SVM) trained on engineered features pertaining to OCTA time-series data. When evaluated with 4-fold cross-validation, the final classifier achieved a receiver operating characteristic (ROC) area under the curve (AUC) of .978 (baseline: 0.5) and a precision-recall (PR) AUC of .700 (baseline: 0.013). It also reduced the amount of time required to annotate from 1 hour to 22 minutes per dataset and detected an average of 8.1 stalling segments in each dataset that were missed by expert annotations, which amounted to 26% of all stalling segments. To demonstrate the utility of our tool, we measured the morphological properties of capillaries and found that stalling segments are significantly smaller in diameter, more tortuous, and longer than non-stalling segments. These findings highlight the algorithm's potential to uncover morphological patterns associated with stalling and facilitate comparative studies across experimental conditions. To support further research, the tool is freely available as open-source software for use by the scientific community.