DCNN Developed for Detection and Assessing the Perfusion of PTG

NCT ID: NCT05869058

Last Updated: 2025-12-03

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-06-13
• Study Completion Date: 2026-10-31

Brief 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.

Detailed 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.

Conditions

Thyroidectomy

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

a deep convolutional neural network

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• The patients who undergo endoscopic thyroidectomy

Exclusion Criteria

• hyperparathyroidism
• hypoparathyroidism
• neck surgery history
• cervical radiotherapy history

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Peiliang Lin, M.D.

Role: PRINCIPAL_INVESTIGATOR

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Locations

Sun Yat-sen Memorial Hospital

Guangzhou, Guangdong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Peiliang Lin, M.D.

Role: CONTACT

linpliang3@mail.sysu.edu.cn

0086-020-34071439

Facility Contacts

Peiliang Lin, M.D.

Role: primary

linpliang3@mail.sysu.edu.cn

+862034071439

Other Identifiers

SYSKY-2022-177-01

Identifier Type: -

Identifier Source: org_study_id