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Validation of a Universal Cataract Intelligence Platform

NCT03623971 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 500

Last updated 2018-08-09

No results posted yet for this study

Summary

This study established and validated a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multi-level clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.The datasets were labeled using a three-step strategy: (1) categorize slit lamp photographs into four separate capture modes; (2) diagnose each photograph as a normal lens, cataract or a postoperative eye; and (3) based on etiology and severity, further classify each diagnosed photograph for a management strategy of referral or follow-up. A deep residual convolutional neural network (CS-ResCNN) was used for the image classification task. Moreover, we integrated the cataract AI agent with a real-world multi-level referral pattern involving self-monitoring at home, primary healthcare, and specialized hospital services.

Conditions

  • Cataract
  • Artificial Intelligence

Interventions

DEVICE

Cataract AI agent

An artificial intelligence to make comprehensive evaluation and treatment decision of different types of cataracts.

Sponsors & Collaborators

  • Xidian University

    collaborator OTHER

  • Sun Yat-sen University

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2013-01-01
Primary Completion
2017-06-01
Completion
2017-06-01

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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 NCT03623971 on ClinicalTrials.gov