Application and Evaluation of Vision-LSTM Modeling in Diagnostic Ultrasound Imaging of TI-RADS Category 4b Thyroid Nodules

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.

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Key information

Conditions

Thyroid Cancer Endocrine Gland Neoplasms Endocrine System Diseases Head and Neck Neoplasms Neoplasms Neoplasms by Site Thyroid Diseases Thyroid Neoplasms

Age range

20 year–78 year

Sex eligibility

All sexes

Study type

Observational

Primary location

QianfoshanH

Jinan, Shandong, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

(1) Patients with thyroid nodules visible on ultrasound who underwent biopsy and/or surgical resection. (2) Diagnosed as TI-RADS category 4b on the basis of preoperative ultrasound images by two sonographers with more than 5 years of experience in thyroid ultrasound diagnosis. (3) All nodules underwent puncture biopsy or surgery to obtain pathologic results.

Exclusion criteria

(1)The quality of the patient's ultrasound images was poor. (2) The patient has incomplete clinical and imaging data. (3) The patient has had thyroid surgery or other treatment.

Treatment and study plan

Primary outcomes

Accuracy of diagnostic models

Time frame: Immediately evaluated after the diagnostic model was built

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).

Sponsors and collaborators

Lead sponsor

Ma Zhe

Other

Registry information

Official study title

Application and Evaluation of Vision-LSTM Model in Diagnostic Ultrasound Imaging of TI-RADS Class 4b Thyroid Nodules

Important dates

Study start: 2022
Primary completion: 2024
Study completion: 2024
First posted: Mar 27, 2026
Registry last updated: Mar 27, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed