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Deep Neural Network Based Artificial Intelligence System for Thyroid Scintigraphy Evaluation of Thyroid Disease

Deep Neural Network Based Artificial Intelligence System for Thyroid Scintigraphy Evaluation of Thyroid Disease

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000029469
Enrollment
Unknown
Registered
2020-02-02
Start date
2020-02-01
Completion date
Unknown
Last updated
2020-02-03

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

Conditions

thyroid diseases

Interventions

deep&#32
neural&#32
network
Gold Standard:99mTc thyroid scintigraphy
Index test:An&#32
artificial&#32
intelligence&#32
(AI)&#32
model&#32
based&#32
on&#32
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Sponsors

Department of Nuclear Medicine, West China Hospital of Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
14 Years to 90 Years

Inclusion criteria

Inclusion criteria: We collected images from individual patients who were suspected to have thyroid diseases and underwent thyroid scintigraphy in the last ten years.

Exclusion criteria

Exclusion criteria: Then, cases with improper injection, improper imaging process, and poor image quality, the patients who had primary bone tumor and the ones did not undergo follow-up examinations were excluded as well.

Design outcomes

Primary

MeasureTime frame
areas under curve of receiver operating characteristic;sensitivity;speci?city;accuracy;—

Countries

China

Contacts

Public ContactZhen Zhao

West China Hospital of Sichuan University

zhaozhen1982@126.com+86 13408418720

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026