TY - GEN
T1 - Digital Clinical Decision Support System for Screening of Eye Diseases
AU - Simegn, Gizeaddis Lamesgin
AU - Degu, Mizanu Zelalem
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - Eye diseases are a major cause of blindness, primarily affecting elderly individuals, especially in developing nations. Among various eye diseases, glaucoma, age-related macular degeneration, and cataracts are the most prevalent in sub-Saharan African nations. Many of these eye conditions are preventable or treatable if detected early. Unfortunately, in low-resource settings, due to a lack of screening mechanisms and poorly organized healthcare structures, people do not often seek early checkups before the disease progresses. Furthermore, most eye diseases do not exhibit clear symptoms in the early stages and may be irreversible. Developing countries’ health facilities lack modern and high-quality screening technologies and specialized experts. In addition, conventional eye screening methods are reliant on physicians’ expertise and knowledge, which can lead to misdiagnosis. This study proposes a digital eye disease diagnosis support system that integrates image acquisition, enhancement, retinal vessel extraction, machine learning-based segmentation, and cup-to-disk ratio determination with intraocular pressure measurement for automated eye disease screening. Our experimental results indicate that this proposed system has the potential to be utilized as a decision support system for eye disease diagnosis, particularly in resource-limited settings.
AB - Eye diseases are a major cause of blindness, primarily affecting elderly individuals, especially in developing nations. Among various eye diseases, glaucoma, age-related macular degeneration, and cataracts are the most prevalent in sub-Saharan African nations. Many of these eye conditions are preventable or treatable if detected early. Unfortunately, in low-resource settings, due to a lack of screening mechanisms and poorly organized healthcare structures, people do not often seek early checkups before the disease progresses. Furthermore, most eye diseases do not exhibit clear symptoms in the early stages and may be irreversible. Developing countries’ health facilities lack modern and high-quality screening technologies and specialized experts. In addition, conventional eye screening methods are reliant on physicians’ expertise and knowledge, which can lead to misdiagnosis. This study proposes a digital eye disease diagnosis support system that integrates image acquisition, enhancement, retinal vessel extraction, machine learning-based segmentation, and cup-to-disk ratio determination with intraocular pressure measurement for automated eye disease screening. Our experimental results indicate that this proposed system has the potential to be utilized as a decision support system for eye disease diagnosis, particularly in resource-limited settings.
KW - Cup-to-disk ratio
KW - Eye disease
KW - Glaucoma
KW - Image processing
KW - Intraocular pressure measurement
KW - Machine learning
KW - Segmentation
KW - Vessel extraction
UR - http://www.scopus.com/inward/record.url?scp=85180635146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180635146&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-41173-1_3
DO - 10.1007/978-3-031-41173-1_3
M3 - Conference contribution
AN - SCOPUS:85180635146
SN - 9783031411724
T3 - Green Energy and Technology
SP - 39
EP - 49
BT - Advancement of Science and Technology in Sustainable Manufacturing and Process Engineering
A2 - Mequanint, Kibret
A2 - Tsegaw, Assefa Asmare
A2 - Sendekie, Zenamarkos Bantie
A2 - Kebede, Birhanu
A2 - Gedilu, Ephrem Yetbarek
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th EAI International Conference on Advancements of Science and Technology, EAI ICAST 2022
Y2 - 4 November 2022 through 6 November 2022
ER -