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Validation of the Utility of Rare Disease Intelligence Platform

Primary Purpose

Cataract, Artificial Intelligence

Status
Completed
Phase
Not Applicable
Locations
China
Study Type
Interventional
Intervention
CC-Cruiser
Sponsored by
Sun Yat-sen University
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Cataract

Eligibility Criteria

undefined - 80 Years (Child, Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

  • Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital.

Exclusion Criteria:

-

Sites / Locations

  • Zhongshan Ophthalmic Center, Sun Yat-sen University
  • Department of Ophthalmology, Guangdong General Hospital, Guangdong Academy of Medical Sciences
  • Department of Ophthalmology, First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine
  • Department of Ophthalmology, Qingyuan People's Hospital

Arms of the Study

Arm 1

Arm Type

Experimental

Arm Label

Eligible patients for CC-Cruiser test

Arm Description

Outcomes

Primary Outcome Measures

The proportion of accurate, mistaken and miss detection of CC-Cruiser.

Secondary Outcome Measures

Full Information

First Posted
April 13, 2016
Last Updated
April 21, 2016
Sponsor
Sun Yat-sen University
Collaborators
Ministry of Health, China, Xidian University
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1. Study Identification

Unique Protocol Identification Number
NCT02748044
Brief Title
Validation of the Utility of Rare Disease Intelligence Platform
Official Title
Validation of the Utility of Rare Disease Intelligence Platform: A Multicenter Cluster Clinical Trial
Study Type
Interventional

2. Study Status

Record Verification Date
April 2016
Overall Recruitment Status
Completed
Study Start Date
January 2012 (undefined)
Primary Completion Date
April 2016 (Actual)
Study Completion Date
April 2016 (Actual)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Sun Yat-sen University
Collaborators
Ministry of Health, China, Xidian University

4. Oversight

Data Monitoring Committee
Yes

5. Study Description

Brief Summary
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. The artificial intelligence for systematic clinical application has not yet been successfully validated. Currently, the main prevention strategy for rare diseases is to build specialized care centers. However, these centers are scattered, and their coverage is insufficient, resulting in inadequate health care among a large proportion of rare disease patients. Here, the investigators use "deep learning" to create CC-Cruiser, an intelligence agent involving three functional networks: "pick-up networks" for diagnostics, "evaluation networks" for risk stratification and "strategist networks" to provide assisted treatment decisions. The investigator also establish a cloud intelligence platform for multi-hospital collaboration and conduct clinical trial and website-based study to validate its versatility.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Cataract, Artificial Intelligence

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
53 (Actual)

8. Arms, Groups, and Interventions

Arm Title
Eligible patients for CC-Cruiser test
Arm Type
Experimental
Intervention Type
Device
Intervention Name(s)
CC-Cruiser
Intervention Description
An artificial intelligence to make comprehensive evaluation and treatment decision of congenital cataracts
Primary Outcome Measure Information:
Title
The proportion of accurate, mistaken and miss detection of CC-Cruiser.
Time Frame
Up to 4 years

10. Eligibility

Sex
All
Maximum Age & Unit of Time
80 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria: Patients who underwent ophthalmic examination of the eye and recorded their ocular information in the collaborating hospital. Exclusion Criteria: -
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Haotian Lin, M.D., Ph.D
Organizational Affiliation
Zhongshan Ophthalmic Center, Sun Yat-sen University
Official's Role
Study Director
First Name & Middle Initial & Last Name & Degree
Yizhi Liu, M.D., Ph.D
Organizational Affiliation
Zhongshan Ophthalmic Center, Sun Yat-sen University
Official's Role
Study Chair
First Name & Middle Initial & Last Name & Degree
Erping Long, M.D., Ph.D
Organizational Affiliation
Zhongshan Ophthalmic Center, Sun Yat-sen University
Official's Role
Principal Investigator
Facility Information:
Facility Name
Zhongshan Ophthalmic Center, Sun Yat-sen University
City
Guangzhou
State/Province
Guangdong
ZIP/Postal Code
510060
Country
China
Facility Name
Department of Ophthalmology, Guangdong General Hospital, Guangdong Academy of Medical Sciences
City
Guangzhou
State/Province
Guangdong
ZIP/Postal Code
510080
Country
China
Facility Name
Department of Ophthalmology, First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine
City
Guangzhou
State/Province
Guangdong
ZIP/Postal Code
510405
Country
China
Facility Name
Department of Ophthalmology, Qingyuan People's Hospital
City
Qingyuan
State/Province
Guangdong
ZIP/Postal Code
511518
Country
China

12. IPD Sharing Statement

Citations:
PubMed Identifier
26339020
Citation
Lin H, Long E, Chen W, Liu Y. Documenting rare disease data in China. Science. 2015 Sep 4;349(6252):1064. doi: 10.1126/science.349.6252.1064-b. No abstract available.
Results Reference
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PubMed Identifier
26958831
Citation
Lin H, Ouyang H, Zhu J, Huang S, Liu Z, Chen S, Cao G, Li G, Signer RA, Xu Y, Chung C, Zhang Y, Lin D, Patel S, Wu F, Cai H, Hou J, Wen C, Jafari M, Liu X, Luo L, Zhu J, Qiu A, Hou R, Chen B, Chen J, Granet D, Heichel C, Shang F, Li X, Krawczyk M, Skowronska-Krawczyk D, Wang Y, Shi W, Chen D, Zhong Z, Zhong S, Zhang L, Chen S, Morrison SJ, Maas RL, Zhang K, Liu Y. Lens regeneration using endogenous stem cells with gain of visual function. Nature. 2016 Mar 17;531(7594):323-8. doi: 10.1038/nature17181. Epub 2016 Mar 9. Erratum In: Nature. 2017 Jan 26;541(7638):558.
Results Reference
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Links:
URL
http://www.gzzoc.com/
Description
Home page of Zhongshan Ophthalmic Center
URL
http://www.cnccclub.com/
Description
Homepage of Childhood Cataract Program of the Chinese Ministry of Health(CCPMOH)

Learn more about this trial

Validation of the Utility of Rare Disease Intelligence Platform

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