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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05869058
Other study ID # SYSKY-2022-177-01
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date June 13, 2023
Est. completion date October 2026

Study information

Verified date May 2023
Source Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Contact Peiliang Lin, M.D.
Phone 0086-020-34071439
Email linpliang3@mail.sysu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Description:

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. Resection of the PTG by mistake or interruption of the blood supply may lead to transient or permanent hypoparathyroidism, which would require short-term or lifelong calcium and/or vitamin D supplement. 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. Although several researchers indicated that indocyanine green fluorescence angiography could be used to assess the perfusion of the PTG intraoperatively, it may cause allergic reaction and need repetitive injection. Therefore, 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. This research may lead to the development of endoscopic modules in PTG detection and PTG perfusion prediction to reduce postoperative hypoparathyroidism.


Recruitment information / eligibility

Status Recruiting
Enrollment 300
Est. completion date October 2026
Est. primary completion date April 2026
Accepts healthy volunteers No
Gender All
Age group 18 Years to 70 Years
Eligibility Inclusion Criteria: - The patients who undergo endoscopic thyroidectomy Exclusion Criteria: - hyperparathyroidism - hypoparathyroidism - neck surgery history - cervical radiotherapy history

Study Design


Related Conditions & MeSH terms


Intervention

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

Locations

Country Name City State
China Sun Yat-sen Memorial Hospital Guangzhou Guangdong

Sponsors (1)

Lead Sponsor Collaborator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Area Under the Receiver Operating Characteristic Curve Area Under the Receiver Operating Characteristic Curve 3 years
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