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A survey on computer aided diagnosis for ocular diseases
Zhuo Zhang1, Ruchir Srivastava, Huiying Liu
1Institute for Infocomm Research, 1 Fusionopolis Way, Singapore, Singapore. zzhang@i2r.a-star.edu.sg.
BMC Medical Informatics and Decision Making
|September 2, 2014
Summary
Computer Aided Diagnosis (CAD) systems offer automated detection for ocular diseases, aiding clinicians and improving patient access. Further development in integrating diverse data sources promises significant advancements in automated ocular disease diagnosis.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Computer Aided Diagnosis (CAD) automates ocular disease detection, gaining attention from clinicians and researchers.
- CAD systems provide objective insights, reducing clinician workload and enabling early patient diagnosis.
Purpose of the Study:
- To review ocular CAD methodologies across different data types.
- To investigate databases and algorithms for detecting various ocular diseases.
- To analyze the advantages and limitations of current ocular CAD approaches.
Main Methods:
- Literature review of ocular CAD methodologies.
- Analysis of algorithms and databases for clinical, genetic, and imaging data.
- Comprehensive investigation of recent developments in ocular disease CAD.
Main Results:
- Studied CAD methods using clinical, genetic, and imaging data.
- Summarized recent advancements in CAD for Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration, and Pathological Myopia.
- Evaluated the strengths and weaknesses of various ocular CAD techniques.
Conclusions:
- Ocular CAD has progressed significantly, but fully automatic systems are still under development.
- Integrating clinical knowledge and heterogeneous data sources holds potential for future breakthroughs in ocular CAD.
- The clinical importance of advanced, automated CAD systems for ocular diseases is substantial.
