DCNN Developed for Detection and Assessing the Perfusion of PTG

Condition(s) studied

Investigational drug(s) / intervention(s)

a deep convolutional neural network: a deep convolutional neural network developed for detection and assessing the perfusion of parathyroid gland during endoscopic thyroidectomy

Study 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

Primary outcome measure(s)

• Area Under the Receiver Operating Characteristic Curve — 3 years
  Area Under the Receiver Operating Characteristic Curve

Trial sites (1)

More Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University trials in China

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