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Background <p>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.</p> Methods <p>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.</p> Results <p>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 (<i>p</i> = 0.004).</p> Conclusion <p>PG-AI generated accurate segmentation masks of PGs in real-time intraoperative images, providing reliable visual guidance to surgeons during identification.</p>

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A novel automated parathyroid glands detection and segmentation method in thyroidectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月13日

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  • Fan Yu,
  • Xiaolei Yi,
  • Zihan Lin,
  • Hong Chen,
  • Jie Kang,
  • Yinyue Wu,
  • Xuehai Ding,
  • Quanyong Luo,
  • Bo Wu

摘要

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.