ArticleBMC surgery2026
A novel automated parathyroid glands detection and segmentation method in thyroidectomy.
Article in BMC surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundIntraoperative preservation of parathyroid glands (PGs) remained a significant challenge in thyroidectomy. Recently, deep learning has demonstrated considerable potential in medical applications. We proposed a novel intraoperative method for PG identification.
methodsWe developed a localization subnet based on YOLOX and a novel semantic segmentation model termed Trans-U-HRNet, collectively termed PG-AI. The dataset included 976 images from 121 patients undergoing open thyroidectomy, with images from 101 patients randomly split 8:2 for training and internal validation. PG detection was quantified using PG-AI, and its performance was visually compared with near-infrared autofluorescence (NIRAF) imaging and assessments by surgeons with varying experience levels.
resultsPG-AI achieved an accuracy of 91.1% and a recall rate of 86.5% on the internal validation set. The recognition rates of PG-AI were 88.7% and 85.0% on the internal and external validation sets, respectively, in visualization. PG-AI showed 72.1% agreement with NIRAF imaging, and the combined approaches successfully identified all PGs. In external validation, PG-AI significantly outperformed junior surgeons in recognition rate (p = 0.004).
conclusionPG-AI generated accurate segmentation masks of PGs in real-time intraoperative images, providing reliable visual guidance to surgeons during identification.
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