Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Ministry of Health, China | OTHER_GOV |
| Xidian University | OTHER |
Not provided
Not provided
Not provided
Not provided
The prevention and treatment of diseases via artificial intelligence represents an ultimate goal in computational medicine. Application scenarios of the current medical algorithms are too simple to be generally applied to real-world complex clinical settings. Here, the investigators use "deep learning" and "visionome technique", an novel annotation method for artificial intelligence in medical, to create an automatic detection and classification system for four key clinical scenarios: 1) mass screening, 2) comprehensive clinical triage, 3) hyperfine diagnostic assessment, and 4) multi-path treatment planning. The investigator also establish a telemedicine system and conduct clinical trial and website-based study to validate its versatility.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Eligible patients for AI test. | Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ophthalmology diagnostic system. | Device | An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases. |
|
| Measure | Description | Time Frame |
|---|---|---|
| The proportion of accurate, mistaken and miss detection of the ophthalmology diagnostic system. | Up to 5 years |
Not provided
Not provided
Inclusion Criteria:
Not provided
Not provided
Not provided
Not provided
A prospective study of patients and residents who use the web platform for diagnosis.
Not provided
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhongshan Ophthalmic Center, Sun Yat-sen University | Guangzhou | Guangdong | 510000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32572198 | Derived | Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22. |
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D005128 | Eye Diseases |
| D003231 | Conjunctivitis |
| D007634 | Keratitis |
| D011625 | Pterygium |
| D002386 | Cataract |
| ID | Term |
|---|---|
| D003229 | Conjunctival Diseases |
| D003316 | Corneal Diseases |
| D007905 | Lens Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided