Clinical trial NCT05869058: DCNN Developed for Detection and Assessing the Perfusion of PTG
Official title: Development and Improvement of a Deep Convolutional Neural Network for Detection and Assessing the Perfusion of Parathyroid Gland During Endoscopic Thyroidectomy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Thyroidectomy
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2023-05-22
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study 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.

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.

Eligibility criteria: Inclusion Criteria:

* The patients who undergo endoscopic thyroidectomy

Exclusion Criteria:

* hyperparathyroidism
* hypoparathyroidism
* neck surgery history
* cervical radiotherapy history

Primary outcomes:
measure: Area Under the Receiver Operating Characteristic Curve
timeFrame: 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen Memorial Hospital
state: Guangdong
Study officials:
affiliation: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
name: Peiliang Lin, M.D.
role: PRINCIPAL_INVESTIGATOR
References: