DCNN Developed for Detection and Assessing the Perfusion of PTG
ConditionThyroidectomy
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-70
SponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
The patients who undergo endoscopic thyroidectomy
Disqualifiers
hyperparathyroidism
hypoparathyroidism
neck surgery history
cervical radiotherapy history
Trial population
The patients, who choose to undergo endoscopic thyroidectomy due to thyroid diseases and have no history of hyperparathyroidism, hypoparathyroidism, neck surgery and cervical radiotherapy, are candidates of our study.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
a deep convolutional neural network
Diagnostic testa deep convolutional neural network developed for detection and assessing the perfusion of parathyroid gland during endoscopic thyroidectomy
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Area Under the Receiver Operating Characteristic Curve
Area Under the Receiver Operating Characteristic Curve
Time frame
3 yearsSponsors and contacts
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Source ClinicalTrials.gov