NCT03623971

Brief 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.

Trial Health

100
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
500

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started Jan 2013

Longer than P75 for not_applicable

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 1, 2013

Completed
4.4 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2017

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

June 1, 2017

Completed
1.2 years until next milestone

First Submitted

Initial submission to the registry

August 7, 2018

Completed
2 days until next milestone

First Posted

Study publicly available on registry

August 9, 2018

Completed
Last Updated

August 9, 2018

Status Verified

August 1, 2018

Enrollment Period

4.4 years

First QC Date

August 7, 2018

Last Update Submit

August 7, 2018

Conditions

CataractArtificial Intelligence

Keywords

CataractArtificial IntelligenceMedical Referral Pattern

Outcome Measures

Primary Outcomes (1)

  • Diagnostic accuracy of the cataract AI agent

    AUC: area under the receiver operating curve; accuracy (ACC) = (TP + TN) / (TP + TN + FP + FN); sensitivity (SEN) = TP / (TP + FN); specificity (SPE) = TN / (TN + FP); TP = true positive; TN = true negative; FP = false positive; FN = false negative.

    6 months

Study Arms (1)

Artificial Intelligence

EXPERIMENTAL

A universal diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of cataract.

Device: Cataract AI agent

Interventions

Cataract AI agentDEVICE

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

Artificial Intelligence

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the primary healthcare center.

You may not qualify if:

  • The patients who cannot cooperate with the examinations.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

MeSH Terms

Conditions

Cataract

Condition Hierarchy (Ancestors)

Lens DiseasesEye Diseases

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
DIAGNOSTIC
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Clinical Professor

Study Record Dates

First Submitted

August 7, 2018

First Posted

August 9, 2018

Study Start

January 1, 2013

Primary Completion

June 1, 2017

Study Completion

June 1, 2017

Last Updated

August 9, 2018

Record last verified: 2018-08