Automated Hand Flexor Tendon-Thickness Measurement in Systemic Sclerosis.

OBJECTIVE

Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time-consuming process prone to interobserver variability. This study aimed to develop and evaluate an automated method for standardized measurement of finger flexor tendon thickness on longitudinal ultrasonography images in patients with SSc.

METHODS

In this proof-of-concept study, ultrasonography images were obtained from the multicenter HANDSOME cohort. The training dataset included 687 images from 59 individuals. The independent testing dataset consisted of 150 images from 15 individuals. A deep learning-based pipeline using nnU-Net was developed to automatically segment the flexor tendon, metacarpal head, and proximal phalanx base. The metacarpophalangeal joint was subsequently localized, after which tendon thickness was automatically measured perpendicular to the tendon. Automated measurements were compared with manual expert measurements.

RESULTS

Automated tendon-thickness measurements were successfully obtained in all images (100%). The model demonstrated high segmentation performance, with Dice similarity coefficients of 0.90 for the flexor tendon, 0.83 for the proximal phalanx base, and 0.76 for the metacarpal head. Automated thickness measurements showed strong agreement with manual measurements (intraclass correlation coefficient 0.83). The mean absolute measurement error was 0.25 mm, corresponding to a relative measurement error of 8.0%. Visual expert evaluation classified 90% of automated segmentations as clinically acceptable.

CONCLUSION

Automated analysis of longitudinal hand ultrasonography images enables accurate measurement of flexor tendon thickness. This approach may facilitate standardized tendon assessment and support studies investigating tendon involvement.