Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression

ML4H 2025 Symposium Submission239 Authors

Keywords: ordinal regression, soft label learning, phonotrauma, vocal folds

TL;DR: We present the first automated method for phonotrauma severity classification from vocal fold images. Our approach combines ordinal regression with soft label learning.

Track: Proceedings

Abstract: Phonotrauma refers to vocal fold tissue damage resulting from exposure to forces during voicing. It occurs on a continuum from mild to severe, and treatment options can vary based on severity. Assessment of severity involves a clinician’s expert judgment, which is costly and can vary widely in reliability. In this work, we present the first method for automatically classifying phonotrauma severity from vocal fold images. To account for the ordinal nature of the labels, we adopt a widely used ordinal regression framework. To account for label uncertainty, we propose a novel modification to ordinal regression loss functions that enables them to operate on soft labels reflecting annotator rating distributions. Our proposed soft ordinal regression method achieves predictive performance approaching that of clinical experts, while producing well-calibrated uncertainty estimates. By providing an automated tool for phonotrauma severity assessment, our work can enable large-scale studies of phonotrauma, ultimately leading to improved clinical understanding and patient care.

General Area: Applications and Practice

Specific Subject Areas: Medical Imaging, Supervised Learning

Data And Code Availability: Yes

Ethics Board Approval: Yes

Entered Conflicts: I confirm the above

Anonymity: I confirm the above

Submission Number: 239