Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks

TL;DR

This study presents a deep learning approach using convolutional neural networks to automatically analyze ultrasound images for monitoring Achilles tendon healing, showing high correlation with expert assessments and emphasizing ultrasound's clinical utility.

Contribution

The paper introduces custom CNN models for classification and regression to assess Achilles tendon healing from ultrasound images, demonstrating high accuracy and the importance of sagittal slices.

Findings

High correlation between model estimates and expert radiologists' assessments

Sagittal slices better model intratendinous healing parameters

Ultrasound imaging is effective for clinical healing assessment

Abstract

Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our models are trained and validated on an acquired dataset of over 250.000 sagittal and over 450.000 axial ultrasound slices. The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress. We also observe that parameters associated with i.a. intratendinous healing processes are better modeled with sagittal slices. We prove that ultrasound imaging is quantitatively useful for clinical assessment of Achilles tendon healing process and should be viewed as complementary to magnetic resonance imaging.