Artificial Intelligence for Intraoperative Guidance: Using an Object Detection Model in Conjunction With Data Augmentation to Detect Parathyroid Glands During Thyroidectomy

Abstract

Background: Postoperative hypoparathyroidism is a major complication of thyroidectomy that occurs when the parathyroid glands are inadvertently damaged during surgery. Although intraoperative images are rarely used to train AI because of its complex nature, our group recognized the potential of training AI to intraoperatively detect parathyroid glands using various augmentation methods. The aim of the present study was to train an effective artificial intelligence (AI) model to detect parathyroid glands during thyroidectomy and evaluate its accuracy. Methods: Video clips of the parathyroid gland were collected during thyroid lobectomy procedures. Confirmed parathyroid images were used to form three types of datasets according to augmentation status: baseline, geometric transformation, and generative adversarial network-based image inpainting. The average precision was set as the primary outcome regarding the performance of parathyroid detection performance. Findings: The performance of the AI model in detecting parathyroid glands based on baseline data was 77%. This performance was enhanced by applying both geometric transformation and image inpainting augmentation methods. Between the two, the geometric transformation data augmentation dataset demonstrated superior results compared to that of the image inpainting model. The average precision was 79% in the transformation dataset vs. 78.6% in the inpainting dataset. However, when the model was subjected to images of a completely different thyroidectomy approach, image inpainting methods proved to be more effective compared to both geometric transformation and baseline (46% vs. 37% vs. 33%). Conclusions: Our study demonstrated the utility of our AI model, which, when aided with appropriate augmentation methods, is potentially an effective and generalizable tool for intraoperative identification of parathyroid glands during thyroidectomy. Furthermore, this technology has significant potential to be easily extended to identifying important structures during other surgical procedures in general. Funding Information: Basic Science Research Program of the National Research Foundation of Korea [NRF-2019R1C1C1008384]. Declaration of Interests: We declare no competing interests. Ethics Approval Statement: Institutional review board approval was acquired (GDIRB2019-359), and research was conducted in accordance with the Declaration of Helsinki in its latest form.