Semantic web ontology for structured knowledge representation and clinical decision support in eye diseases

Area of Science:

• Ophthalmology
• Medical Informatics
• Knowledge Representation

Background:

• Vision impairment affects 2.2 billion globally, with over 1 billion cases being avoidable due to lack of eye care access.
• Ophthalmology faces significant knowledge organization challenges due to the complexity of eye diseases.
• Ontologies offer structured domain knowledge representation, enhancing data findability, accessibility, interoperability, and reusability.

Purpose of the Study:

• To develop a comprehensive Eye Disease Ontology (EDO) for organizing information on common eye diseases.
• To create a resource facilitating data extraction and analysis for both the public and medical professionals.
• To improve clinical diagnosis, research, medical education, and patient care in ophthalmology.

Main Methods:

• Developed the Eye Disease Ontology (EDO) using the NeOn technique in Protégé/OWL.
• Structured EDO with 566 classes, 16 object properties, 12 data properties, and 119 instances.
• Validated the ontology using competency questions, SPARQL queries, Hermit Reasoner, and OOPS Pitfalls Scanner.

Main Results:

• The Eye Disease Ontology (EDO) systematically categorizes common eye diseases (e.g., cataracts, glaucoma, AMD).
• EDO links diseases to symptoms, causes, risk factors, diagnostics, and treatments.
• Comprehensive documentation was created to promote EDO reuse in future applications.

Conclusions:

• The developed Eye Disease Ontology (EDO) serves as a valuable resource for managing complex ophthalmological information.
• EDO has the potential to significantly advance clinical diagnosis, research, and medical education in eye care.
• Promoting the reuse of EDO is crucial for its impact on future ophthalmology applications and patient care.