DCNN Developed for Detection and Assessing the Perfusion of PTG
Development and Improvement of a Deep Convolutional Neural Network for Detection and Assessing the Perfusion of Parathyroid Gland During Endoscopic Thyroidectomy
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
300 participants
Jun 13, 2023
OBSERVATIONAL
Conditions
Summary
Since the anatomical location and appearance of the parathyroid gland (PTG) vary, detection of the PTG and preserving the blood supply are among the difficulties encountered during a thyroidectomy procedure. We are planning to train a deep convolutional neural network based on a larger sample of endoscopic images to develop a model to assist surgeons in detection of PTG during endoscopic thyroidectomy. Furthermore, we would like to train a DCNN to predict blood perfusion based on endoscopic images comparing to indocyanine green fluorescence angiography as reference standard, and assess the performance of DCNN in predicting postoperative hypoparathyroidism.
Eligibility
Inclusion Criteria1
- The patients who undergo endoscopic thyroidectomy
Exclusion Criteria4
- hyperparathyroidism
- hypoparathyroidism
- neck surgery history
- cervical radiotherapy history
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Interventions
a deep convolutional neural network developed for detection and assessing the perfusion of parathyroid gland during endoscopic thyroidectomy
Locations(1)
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NCT05869058