A Platform for Multidisciplinary Medical Artificial Intelligence Development

• Conditions: Medical Artificial Intelligence; Medical Imaging

• Registration Number: NCT04890847
• Lead Sponsor: Sun Yat-sen University

Brief Summary

Biomedical deep learning (DL) often relies heavily on generating reliable labels for large-scale data and highly technical requirements for model training. To efficiently develop DL models, we established an integrated platform to introduce automation to both annotation and model training-the primary process of DL model development. Based on this platform, we quantitively validated and compared the annotation strategy and AI model development with the pure manual annotation method performed on medical image datasets from multiple disciplines.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-03-18
• Study First Submitted: 2021-03-18
• Primary Completion: 2021-04-01
• Status Verified: 2021-05
• Study First Submitted QC: 2021-05-16
• Last Update Submitted: 2021-05-16
• Study First Posted: 2021-05-18
• Last Update Posted: 2021-05-18
• Study Completion: 2021-05-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• have medical imaging record (including ophthalmology, pathology, radiography, blood cells, and endoscopy)

Exclusion Criteria

• unqualified medical imaging

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
annotation accuracy	baseline	calculate annotation accuracy for comparison between groups with using the annotation results

Secondary Outcome Measures

Name	Time	Method
AUC of model performance	baseline	calculate AI model AUCs for comparison between groups with using the model predicted results
accuracy of model performance	baseline	calculate AI model accuracy for comparison between groups with using the model predicted results
annotation time cost	baseline	calculate annotation time cost for comparison between groups with using the time recorded during the tests

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity; 🇨🇳 Guangzhou, Guangdong, China