Prediction of scar severity after thyroidectomy using deep learning

Thyroid cancer treatment often involves thyroidectomy, which can lead to significant cosmetic issues due to scarring. This study proposes artificial intelligence to predict the severity and characteristics of post-thyroidectomy scars using a dataset of 24,731 images from 5,306 patients. The deep learning model, ResNet 50, was trained to assess scar severity with a mean absolute error of 1.036 and a Pearson correlation coefficient of 0.640. The study highlights the model's performance variations across different scar types and underscores the challenge of data imbalance in severity levels. The AI-driven approach aims to guide appropriate treatment strategies for post-thyroidectomy scars.