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Trial Title:
Artificial Intelligent Accelerates the Learning Curve for Mastering Contrast-enhanced Ultrasound of Thyroid Nodules
NCT ID:
NCT05982821
Condition:
Thyroid Nodule
Conditions: Official terms:
Thyroid Nodule
Thyroid Diseases
Conditions: Keywords:
Ultrasonography
Contrast Media
Deep Learning
Thyroid Nodule
Learning Curve
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Other
Intervention name:
Artificial Intelligent
Description:
Artificial intelligence assisted radiologists to extract ultrasound features of thyroid
nodules.
Arm group label:
External test set
Arm group label:
Internal test set
Arm group label:
Training set
Summary:
The goal of this observational study is to learn about the learning curve for mastering
the thyroid imaging reporting and data system of contrast-enhanced ultrasound with the
assistance of artificial intelligence in patients with thyroid nodules. The main
questions it aims to answer are:
1. Can we develop a artificial intelligent software to assist doctors in the diagnosis
of thyroid nodules using contrast-enhanced ultrasound?
2. Can artificial intelligent reduce the number of cases and time for doctors to master
the contrast-enhanced ultrasound diagnosis of thyroid nodules?
Participants will be asked to undergo contrast-enhanced ultrasound examination and
ultrasound-guided fine-needle aspiration of thyroid nodules. Researchers will compare the
number of cases and time for doctors with and without artificial intelligent assistance
to master the contrast-enhanced ultrasound diagnosis of thyroid nodules to see if
artificial intelligent reduce the number of cases and time.
Criteria for eligibility:
Study pop:
Study population with the same indication for contrast-enhanced ultrasound and
fine-needle aspiration biopsy:
- Planned ablation or surgery;
- At least one suspicious ultrasound feature, such as hypoechoic/very hypoechoic,
irregular/lobulated margin, taller than wide, or punctate echogenic foci.
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
- Patients with thyroid nodules with a solid component ≥5 mm confirmed by conventional
ultrasound;
- Patients who underwent conventional ultrasound, contrast-enhanced ultrasound, and
fine-needle aspiration biopsy;
- Patients with a final benign or malignant pathological results.
Exclusion Criteria:
- Patients with cytopathology of Bethesda I, III, or IV and without final benign or
malignant pathology；
- Patients with a history of thyroid ablation or surgery;
- Patients with low-quality ultrasound images.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Locations:
Facility:
Name:
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Address:
City:
Guangzhou
Zip:
510289
Country:
China
Status:
Recruiting
Contact:
Last name:
Jingliang Ruan, PhD
Phone:
+8613694202230
Email:
ruanjl3@mail.sysu.edu.cn
Investigator:
Last name:
Jingliang Ruan, PhD
Email:
Principal Investigator
Start date:
January 3, 2024
Completion date:
December 31, 2026
Lead sponsor:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Source:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05982821
