Deep Learning Algorithms for Assessment of Post-Thyroidectomy Scar Subtype

Abstract

The rising incidence of thyroid cancer globally is increasing the number of thyroidectomies, causing visible scars that can greatly affect the quality of life due to cosmetic, psychological, and social impacts. In this study, we explored the application of deep learning algorithms to objectively assess post-thyroidectomy scar morphology using computer-aided diagnosis. This study was approved by the Institutional Review Board of Yonsei University College of Medicine (approval no. 3-2021-051). A dataset comprising 7524 clinical photographs from 3565 patients with post-thyroidectomy scars was utilized. We developed a deep learning model using a convolutional neural network (CNN), specifically the ResNet 50 model and introduced a multiple clinical photography learning (MCPL) method. The MCPL method aimed to enhance the model’s understanding by considering characteristics from multiple images of the same lesion per patient. The primary outcome, measured by the area under the receiver operating characteristic curve (AUROC), demonstrated the superior performance of the MCPL model in classifying scar subtypes compared to a baseline model. Confidence variation analysis showed reduced discrepancies in the MCPL model, emphasizing its robustness. Furthermore, we conducted a decision study involving five physicians to evaluate the MCPL model’s impact on diagnostic accuracy and agreement. Results of the decision study indicated enhanced accuracy and reliability in scar subtype determination when the confidence scores of the MCPL model were integrated into decision-making. Our findings suggest that deep learning, particularly the MCPL method, is an effective and reliable tool for objectively classifying post-thyroidectomy scar subtypes. This approach holds promise for assisting professionals in improving diagnostic precision, aiding therapeutic planning, and ultimately enhancing patient outcomes in the management of post-thyroidectomy scars.