DCNN Developed for Detection and Assessing the Perfusion of PTG

Thyroidectomy Clinical Trial

Official title:

Development and Improvement of a Deep Convolutional Neural Network for Detection and Assessing the Perfusion of Parathyroid Gland During Endoscopic Thyroidectomy

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05869058
• Other study ID #: SYSKY-2022-177-01
• Status: Recruiting
• 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
• 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

Related Conditions & MeSH terms

• Thyroidectomy

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