BMC surgery · 2026
A novel automated parathyroid glands detection and segmentation method in thyroidectomy.
СтатьяНазвание и аннотация: Перевод в очереди
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Yu Fan, Yi Xiaolei, Lin Zihan, Chen Hong, Kang Jie, Wu Yinyue, Ding Xuehai, Luo Quanyong, Wu Bo
Аннотация
BACKGROUND: Intraoperative 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.
METHODS: We 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.
RESULTS: PG-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).
CONCLUSION: PG-AI generated accurate segmentation masks of PGs in real-time intraoperative images, providing reliable visual guidance to surgeons during identification.
METHODS: We 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.
RESULTS: PG-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).
CONCLUSION: PG-AI generated accurate segmentation masks of PGs in real-time intraoperative images, providing reliable visual guidance to surgeons during identification.