AI & Neural Network for Pediatric Cataract
Researchers from China have developed an artificial intelligence (AI) platform that can diagnose congenital cataracts as accurately as ophthalmologists.
What is Cataract?
A cataract is clouding of the natural lens of the eye. It is extremely common among the elderly population but can also affect infants at birth or develop at a young age.
“In children, cataract causes more visual disability than any other form of treatable blindness. Children with untreated, visually significant cataracts face a lifetime of blindness at tremendous quality of life and socioeconomic costs to the child, the family, and the society”- American Academy of Ophthalmology (AAO).
Detecting Cataracts in Children:
The asymptomatic progression of pediatric cataracts makes it difficult for early detection. Not to mention that young kids can’t really express themselves. However, catching the disease early is critical; late diagnosis often result in poor visual outcomes.
Problems:
- Diagnosis requires expertise, especially in children and neonates.
- There is a relatively small number of ophthalmologists for the size of our population; with an even smaller number of pediatric ophthalmologists.
- Experts are concentrated in specialized centers but their coverage is largely insufficient.
- Rural areas get almost no coverage.
- This lack of accessibility causes lifetime vision loss to affected children.
Solution: AI
A team in China tried to address the problem of lack of pediatric ophthalmologists by using Artificial Intelligence (AI), specifically deep learning and convolutional neural networks. They built an AI machine, CC-Cruiser, that has three main functions: 1) Diagnosis 2) Risk stratification 3) Treatment suggestions.
Their initial work was done on congenital cataract cases only (amongst other types of pediatric cataracts) but further studies are ongoing.
Future: AI requires Big Data
A cloud-based platform has been created for multi-hospital collaboration; Doctors at hospitals around the country can access the platform and upload patient images. More data will be collected this way, which is essential to AI’s learning and fine-tuning.
“The limited resources of patients and the isolation of the data in individual hospitals represent a bottleneck in data usage. Building a collaborative cloud platform for data integration and patient screening is an essential step.”-Professor Lin Haotian of Sun Yat-Sen University
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