A Platform for Multidisciplinary Medical Artificial Intelligence Development (AI)

Sun Yat-sen University

Status: Unknown

Conditions: Medical Imaging; Medical Artificial Intelligence

Study type: Observational

Funder types: Other

Identifiers

NCT04890847

AIplatform-2020

Details and patient eligibility

About

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.

Enrollment: 200 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

• unqualified medical imaging

Trial design

200 participants in 2 patient groups

human-machine collaboration group

Description:

healthcare professionals and machine collaboration for annotation and AI model development

pure mannual group

Description:

healthcare professionals for pure manual annotation and AI model development