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Embedded deep learning in ophthalmology: making ophthalmic imaging smarter.
Petteri Teikari1, Raymond P Najjar1, Leopold Schmetterer1
1Visual Neurosciences Group, Singapore Eye Research Institute, Singapore.
Therapeutic Advances in Ophthalmology
|March 27, 2019
Summary
Deep learning in ophthalmology is advancing, with a focus on embedding AI into imaging devices for automated, high-quality image acquisition. This integration promises more accurate diagnostics and efficient data management in clinical practice.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Deep learning (DL) shows promise in ophthalmology for diagnosis and prognosis.
- Limited research exists on embedding DL systems within ophthalmic imaging devices for automated acquisition.
- Current DL applications primarily focus on post-acquisition analysis rather than integrated image capture.
Purpose of the Study:
- To review current and future directions of 'active acquisition'-embedded deep learning in ophthalmology.
- To explore how embedded DL can enhance image quality with minimal human intervention.
- To discuss the potential impact of embedded DL on clinical diagnostics and data management.
Main Methods:
- Review of existing literature and future trends in embedded deep learning for ophthalmic imaging.
- Discussion of hardware advancements enabling low-cost, high-performance embedded systems.
- Exploration of a three-layer computation framework (edge, fog, cloud) for clinical systems.
Main Results:
- 'Active acquisition' via embedded DL can significantly improve image quality and reduce operator intervention.
- Embedded DL systems, particularly at the edge layer, can act as automatic data curation tools.
- Enhanced data quality from edge-layer processing benefits both electronic health records and cloud-based data mining.
Conclusions:
- Embedding deep learning into ophthalmic imaging devices ('active acquisition') is a key future direction.
- This approach enhances image quality, leading to more robust DL-based clinical diagnostics.
- Improved data curation at the edge layer optimizes data for electronic health records and cloud analytics.
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