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Updated: Jul 12, 2025
Establishment of a Simple and Effective Rat Model for Intraoperative Parathyroid Gland Imaging
Published on: August 17, 2022
[Application of near-infrared autofluorescence imaging-based convolution neural network in recognition of parathyroid
1Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology-Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing 100730, China.
Abstract:
Objective: To investigate the application value of near-infrared autofluorescence imaging-based convolution neural network (CNN) for automatic recognition of parathyroid gland. Methods: The data of 83 patients who underwent thyroid papillary cancer surgery in the Department of Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University from August 2020 to March 2022 were retrospectively analyzed, and a total of 725 autofluorescence images of parathyroid gland were collected during the surgery. Meanwhile, non-parathyroid fluorescence imaging videos in the operation area of 10 patients were also collected, and 928 non-parathyroid fluorescence images were captured from those videos. The fluorescence images of parathyroid and non-parathyroid glands were directly used as input features for deep learning to construct ResNet 34, VGGNet 16 and GoogleNet models for automatic parathyroid identification. The ability of different models to identify parathyroid glands was tested by indicators such as accuracy, specificity, sensitivity, precision, receiver operating characteristic curve and area under the curve (AUC). In addition, 30 fluorescence images of parathyroid and 35 fluorescence images of non-parathyroid glands in 13 patients with papillary thyroid cancer from March to May 2022 were collected to prospectively test the best performing CNN model. Results: Among the 83 patients, there were 25 males and 58 females, with the mean age of (46.7±12.4) years. In the binary classification (parathyroid gland and non-parathyroid gland), the ResNet 34 model performed the best in different CNN models, the accuracy, specificity, sensitivity and precision of the identification test set were 97.6%, 96.3%, 99.3% and 95.5%, and the AUC reached 0.978 (95%CI: 0.956-0.991). In the prospective test, the prediction accuracy of the ResNet 34 model reached 93.8%, and the AUC was 0.938 (95%CI: 0.853-0.984). Conclusion: The near-infrared autofluorescence imaging-based deep CNN has good application value in the automatic recognition of parathyroid gland, and can be used to assist the recognition and protection of parathyroid gland in thyroid cancer surgery.
