Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression
This provides an automated tool for phonotrauma severity assessment, enabling large-scale studies to improve clinical understanding and patient care, though it is incremental as it builds on existing ordinal regression frameworks.
The paper tackles the problem of automatically classifying phonotrauma severity from vocal fold images, achieving predictive performance approaching that of clinical experts with well-calibrated uncertainty estimates.
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.