NCT07496684

Brief Summary

The aim of this study was to evaluate the performance of artificial intelligence (AI) technology in the diagnosis of thyroid nodules, specifically in the field of ultrasound image analysis. It focuses on the accuracy and clinical feasibility of the AI system based on the Vision-LSTM model in the diagnosis of TI-RADS category 4b thyroid nodules.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
401

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2022

Typical duration for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 1, 2022

Completed
3 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 30, 2024

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 30, 2024

Completed
1.2 years until next milestone

First Submitted

Initial submission to the registry

March 16, 2026

Completed
11 days until next milestone

First Posted

Study publicly available on registry

March 27, 2026

Completed
Last Updated

March 27, 2026

Status Verified

March 1, 2026

Enrollment Period

3 years

First QC Date

March 16, 2026

Last Update Submit

March 22, 2026

Conditions

Thyroid Cancer

Outcome Measures

Primary Outcomes (1)

  • Accuracy of diagnostic models

    The study collected ultrasound imaging data from 401 cases of TI-RADS 4b thyroid nodules at our hospital and used this data to train and validate the Vision-LSTM model. The diagnostic results of the AI model were compared with those of junior and senior clinicians to evaluate its performance in terms of diagnostic accuracy and stability; model performance was quantified using metrics such as the area under the curve (AUC) and the precision-recall curve (PR curve).

    Immediately evaluated after the diagnostic model was built

Study Arms (2)

maligant

patients with maligant thyroid masses who underwent biopsy and/or surgical resection.

benign

patients with benign thyroid masses who underwent biopsy and/or surgical resection.

Eligibility Criteria

Age20 Years - 78 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with thyroid nodules who were seen at the First Affiliated Hospital of Shandong First Medical University from January 2022 to December 2024

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

QianfoshanH

Jinan, Shandong, China

Location

MeSH Terms

Conditions

Thyroid Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland NeoplasmsNeoplasms by SiteNeoplasmsHead and Neck NeoplasmsEndocrine System DiseasesThyroid Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
Chief Physician

Study Record Dates

First Submitted

March 16, 2026

First Posted

March 27, 2026

Study Start

January 1, 2022

Primary Completion

December 30, 2024

Study Completion

December 30, 2024

Last Updated

March 27, 2026

Record last verified: 2026-03

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)