DCNN Developed for Detection and Assessing the Perfusion of PTG
For patients and families
In plain language
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- What is being studied
- The protocol lists: a deep convolutional neural network.
- Who it may be relevant to
- Registry conditions: Thyroidectomy. Basic parameters: 18 years — 70 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development and Improvement of a Deep Convolutional Neural Network for Detection and Assessing the Perfusion of Parathyroid Gland During Endoscopic Thyroidectomy
Overview
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.
Interventions
- 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
Primary outcome measures
- Area Under the Receiver Operating Characteristic Curve [Time frame: 3 years]
Eligibility criteria
Inclusion criteria
- The patients who undergo endoscopic thyroidectomy
Exclusion criteria
- hyperparathyroidism
- hypoparathyroidism
- neck surgery history
- cervical radiotherapy history
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Sun Yat-sen Memorial Hospital — Guangzhou
Identifiers
NCT: NCT05869058 · SYSKY-2022-177-01