A Platform for Multidisciplinary Medical Artificial Intelligence Development (AI)

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.

Study Overview

• Status: Recruiting
• Conditions: Medical Imaging; Medical Artificial Intelligence

• Study Type: Observational
• Enrollment (Anticipated): 200

Contacts and Locations

• Study Contact: Name: Haotian Lin, Ph.D, M.D.; Phone Number: +86-020-87330274; Email: gddlht@aliyun.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Recruiting
      • Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
      • Contact: Haotian Lin, Ph.D, M.D.; Phone Number: +86-020-87330274; Email: gddlht@aliyun.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Probability Sample

Study Population

medical imaging for multiple disciplines including ophthalmology, pathology, radiography, blood cells, and endoscopy

Description

Inclusion Criteria:

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

Exclusion Criteria:

• unqualified medical imaging

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
human-machine collaboration group; healthcare professionals and machine collaboration for annotation and AI model development
pure mannual group; healthcare professionals for pure manual annotation and AI model development

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
annotation accuracy	calculate annotation accuracy for comparison between groups with using the annotation results	baseline

Secondary Outcome Measures

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

Collaborators and Investigators

• Sponsor: Sun Yat-sen University
• Investigators: Principal Investigator: Haotian Lin, Ph.D, M.D., Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Study record dates

Study Major Dates

• Study Start (Actual): March 18, 2021
• Primary Completion (Actual): April 1, 2021
• Study Completion (Anticipated): May 31, 2021

Study Registration Dates

• First Submitted: March 18, 2021
• First Submitted That Met QC Criteria: May 16, 2021
• First Posted (Actual): May 18, 2021

Study Record Updates

• Last Update Posted (Actual): May 18, 2021
• Last Update Submitted That Met QC Criteria: May 16, 2021
• Last Verified: May 1, 2021

More Information

Terms related to this study

• Other Study ID Numbers: AIplatform-2020

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No