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Prospective multicenter randomized crossover study on automated thyroid ultrasound examination and artificial intelligence image analysis based on embodied intelligence system

Prospective multicenter randomized crossover study on automated thyroid ultrasound examination and artificial intelligence image analysis based on embodied intelligence system

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600125858
Enrollment
Unknown
Registered
2026-06-01
Start date
2026-06-01
Completion date
Unknown
Last updated
2026-06-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Thyroid lesions

Interventions

Gold Standard:This study employed an independent blinded reference standard. All acquired ultrasound images were independently evaluated by two senior, board-certified ultrasound experts who were not
Index test:Each subject underwent three different thyroid ultrasound scanning methods: 1. Thyroid ultrasound scanning by senior physicians
2. Thyroid ultrasound scanning for junior doctors
3. Embodied Intelligence Ultrasound Robot IS-MAN Scanning System.

Sponsors

The First Affiliated Hospital of Sun Yat-Sen University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Male and female aged between 18 and 80 years; 2. Volunteer to participate in clinical trial research.

Exclusion criteria

Exclusion criteria: Exclusion criteria include open trauma or infection in the neck, inability to cooperate with the standard examination (such as inability to remain in a supine position or inability to follow the examination instructions), a history of total thyroidectomy, or any high-risk conditions such as bleeding.

Design outcomes

Primary

MeasureTime frame
Sensitivity;

Secondary

MeasureTime frame
Completeness of thyroid scanning;Image quality assessment;Detection rate;specificity;accuracy;area under the receiver operating characteristic curve (AUC);Subjective score by participants;Positive predictive value;Negative predictive value;Natural language processing evaluation metrics;

Countries

China

Contacts

Public ContactWang Wei

The First Affiliated Hospital of Sun Yat-Sen University

wangw73@mail.sysu.edu.cn+86 20 8776 5183

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jun 11, 2026