March 3, 2026Open Access

Deep learning-based classification of superficial femoral arterial lesions: a pilot study

Puntos clave

Classification of superficial femoral arterial lesions was supported by object detection algorithms, indicating potential real-world use.
Analyzed lesions can enhance clinical decision-making, with improvements needed for accuracy in classifying stenosis and occlusion.
Application of advanced algorithms aims to better identify and support treatment for peripheral arterial disease.
Future refinements may require more data and detailed lesion annotation to realize full clinical utility.

Resumen

This pilot study supports the feasibility of applying object detection algorithms as a preliminary step toward developing clinical decision support tools for peripheral arterial disease. Further refinements, including additional training data and more granular lesion annotation, are essential for improved classification of stenosis and occlusion.

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Cite This Study

Lee et al. (Thu,) studied this question.

synapsesocial.com/papers/69a767a1badf0bb9e87e1bb4 https://doi.org/https://doi.org/10.4174/astr.2026.110.2.92

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