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A Platform for Multidisciplinary Medical Artificial Intelligence Development

NCT04890847 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2021-05-18

No results posted yet for this study

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.

Conditions

  • Medical Artificial Intelligence
  • Medical Imaging

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Principal Investigators

  • Haotian Lin, Ph.D, M.D. · Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2021-03-18
Primary Completion
2021-04-01
Completion
2021-05-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04890847 on ClinicalTrials.gov