Clinical trial NCT03268031: Artificial Intelligence-assissted Glaucoma Evaluation
Official title: Development of Artificial Intelligence-assissted Diagnostic Program of Glaucoma
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Artificial Intelligence
Glaucoma
Keywords: artificial intelligence
glaucoma
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
Collaborators: name: Chinese Academy of Sciences
First posted: 2017-08-31
Enrollment: 10800
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Anyone who can complete visual field test and have BCVA\>0.1 can be enrolled. We will collect visual field test result and OCT images of both glaucoma and non-glaucoma patients.

Brief summary: Glaucoma is currently the second leading cause of irreversible blindness in the world. Our study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.

Detailed description: Glaucoma is currently the second leading cause of irreversible blindness in the world, which brings heavy burden to human society. Compared to other ocular diseases, diagnostic process of glaucoma is complicated depends on multiple test results, including visual field test, OCT, etc. How to diagnose glaucoma correctly and fast has always been a hot topic in glaucoma researches. Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which has been utilized in a lot of research fields such as automatic drive and medicine. The study intends to combine clinical data of glaucoma patients in Zhongshan Ophthalmic Center with Artificial Intelligence techniques to create programs that can screen and diagnose glaucoma.

Eligibility criteria: Inclusion Criteria:

1. BCVA\>0.1
2. able to complete reliable visual field test
3. no history of intraocular surgery or fundus laser

Exclusion Criteria:

1. unable to complete visual field test

Primary outcomes:
measure: Accuracy of diagnosis by artificial intelligence algorithm
timeFrame: from August 2017 to February 2021
Secondary outcomes:
measure: Sensitivity of diagnosis by artificial intelligence algorithm
timeFrame: from August 2017 to February 2021
measure: Specificity of diagnosis by artificial intelligence algorithm
timeFrame: from August 2017 to February 2021
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Zhongshan Ophthalmic Center
state: Guangdong
Study officials:
affiliation: Sun Yat-sen University
name: Xiulan Zhang, Doctor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Diprose W, Buist N. Artificial intelligence in medicine: humans need not apply? N Z Med J. 2016 May 6;129(1434):73-6.
citation: Quigley HA. Glaucoma. Lancet. 2011 Apr 16;377(9774):1367-77. doi: 10.1016/S0140-6736(10)61423-7. Epub 2011 Mar 30.
citation: Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology. 2016 Sep;123(9):1974-80. doi: 10.1016/j.ophtha.2016.05.029. Epub 2016 Jul 7.