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<p>The ability to perform accurate point-of-care assessment of limb perfusion is critical for safe clinical decision-making. A formal ankle-brachial index (ABI) is typically required prior to supervised exercise therapy (SET) for peripheral arterial disease (PAD) or compression therapy for venous stasis ulcers. However, ABI measurements cannot be reported in patients with calcified, non-compressible tibial vessels. In this study, we introduce AutoABI, a deep learning algorithm that classifies ABI categories directly from circulatory Doppler sounds to improve the accessibility of point-of-care ABI assessment. AutoABI was trained and tested on a limited size dataset of 791 recordings from 198 patients and predicts ABI categories of &lt;0.5, 0.5–0.7, 0.7–0.9, and &gt;0.9. The algorithm achieved strong discriminatory performance with average areas under the receiver operating characteristic curve (AUCs) of 0.95, 0.96, 0.94, and 0.97 for the respective ABI ranges. Additional testing demonstrated the ability to predict ABI categories in patients with non-compressible arteries, offering a promising solution for more accessible and reliable PAD assessments.</p>

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Enabling ankle-brachial index prediction from doppler sounds using deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

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  • Adrit Rao,
  • Kevin Battenfield,
  • Arash Fereydooni,
  • Akshay Chaudhari,
  • Oliver Aalami

摘要

The ability to perform accurate point-of-care assessment of limb perfusion is critical for safe clinical decision-making. A formal ankle-brachial index (ABI) is typically required prior to supervised exercise therapy (SET) for peripheral arterial disease (PAD) or compression therapy for venous stasis ulcers. However, ABI measurements cannot be reported in patients with calcified, non-compressible tibial vessels. In this study, we introduce AutoABI, a deep learning algorithm that classifies ABI categories directly from circulatory Doppler sounds to improve the accessibility of point-of-care ABI assessment. AutoABI was trained and tested on a limited size dataset of 791 recordings from 198 patients and predicts ABI categories of <0.5, 0.5–0.7, 0.7–0.9, and >0.9. The algorithm achieved strong discriminatory performance with average areas under the receiver operating characteristic curve (AUCs) of 0.95, 0.96, 0.94, and 0.97 for the respective ABI ranges. Additional testing demonstrated the ability to predict ABI categories in patients with non-compressible arteries, offering a promising solution for more accessible and reliable PAD assessments.