Decision support system for detection of hypertensive retinopathy using arteriovenous ratio
- Resource Type
- Authors
- Muhammad Sharif; Muhammad Usman Akram; Shahzad Akbar; Anam Tariq; Shoab Ahmad Khan
- Source
- Artificial Intelligence in Medicine. 90:15-24
- Subject
- Decision support system
Databases, Factual
Fundus Oculi
Computer science
Medicine (miscellaneous)
Hypertensive Retinopathy
02 engineering and technology
Diagnostic system
Severity of Illness Index
Renal Veins
Decision Support Techniques
Pattern Recognition, Automated
030218 nuclear medicine & medical imaging
03 medical and health sciences
Renal Artery
0302 clinical medicine
Hypertensive retinopathy
Predictive Value of Tests
Artificial Intelligence
Image Interpretation, Computer-Assisted
Arteriovenous ratio
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Diagnosis, Computer-Assisted
Grading (tumors)
Blindness
business.industry
Reproducibility of Results
Pattern recognition
Prognosis
medicine.disease
Trustworthiness
Computer-aided
020201 artificial intelligence & image processing
Artificial intelligence
business
- Language
- ISSN
- 0933-3657
Hypertensive Retinopathy (HR) caused by hypertension is a retinal disease which may leads to vision loss and blindness. Computer aided diagnostic systems for various diseases are being used in clinics but there is a need to develop an automated system that detects and grades HR disease. In this paper, an automated system is presented that detects and grades HR disease using Arteriovenous Ratio (AVR).The presented system includes three modules i.e. main component extraction, artery/vein (A/V) classification and finally AVR calculation and grading of HR. Proposed system uses vascular map and a set of hybrid features for A/V classification. The evaluation of proposed system is carried out using three datasets. The proposed system shows average accuracies of 95.14% for images of INSPIRE-AVR database, 96.82% for images of VICAVR database and 98.76% for local dataset AVRDB. These results support that the proposed system is trustworthy for clinical use in detection and grading of HR disease. Main contribution of proposed system is that it utilizes complete blood vessel map for A/V classification. These arteries and veins are then used to calculate AVR and grade HR cases based on AVR values. Another contribution of this article is that it presents a new dataset AVRDB for A/V classification and HR detection.