TY - GEN
T1 - DMEgrader
T2 - 11th International Conference on Information Technology, ICIT 2023
AU - Al-Absi, Hamada R.H.
AU - Muchori, Gilbert Njihia
AU - Musleh, Saleh
AU - Islam, Mohammad Tariqul
AU - Pai, Anant
AU - Alam, Tanvir
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - More than half a billion people worldwide are affected by diabetes, which is a prevalent non-communicable disease that can lead to critical health conditions, including vision loss. Diabetic Macular Edema (DME) is a primary cause of vision impairment and can eventually lead to blindness in diabetic patients. Early detection of DME and proper health management are crucial to controlling the disease. Retinal image-based AI-enabled diabetes diagnosis has gained significant attention as a non-invasive, fast, and reasonably accurate method for diagnosing DME. To make this technology accessible to underserved communities or areas lacking proper clinical facilities, a mobile application-based solution could have a significant impact. In this article, we describe how we transformed our previously published AI-enabled model into an Android-based mobile application, which is part of a two-phase research study. In the first phase, we developed a deep learning-based model that predicts DME grading using retinal images. In the second phase, we built a mobile application DMEgrader to make our model accessible via a mobile device. To the best of our knowledge, this is the first article to demonstrate necessary steps and code snippets to support developers in transforming deep learning models into Android based mobile applications for DME grading prediction.
AB - More than half a billion people worldwide are affected by diabetes, which is a prevalent non-communicable disease that can lead to critical health conditions, including vision loss. Diabetic Macular Edema (DME) is a primary cause of vision impairment and can eventually lead to blindness in diabetic patients. Early detection of DME and proper health management are crucial to controlling the disease. Retinal image-based AI-enabled diabetes diagnosis has gained significant attention as a non-invasive, fast, and reasonably accurate method for diagnosing DME. To make this technology accessible to underserved communities or areas lacking proper clinical facilities, a mobile application-based solution could have a significant impact. In this article, we describe how we transformed our previously published AI-enabled model into an Android-based mobile application, which is part of a two-phase research study. In the first phase, we developed a deep learning-based model that predicts DME grading using retinal images. In the second phase, we built a mobile application DMEgrader to make our model accessible via a mobile device. To the best of our knowledge, this is the first article to demonstrate necessary steps and code snippets to support developers in transforming deep learning models into Android based mobile applications for DME grading prediction.
KW - Deep Learning
KW - Diabetes
KW - Diabetic Macular Edema
KW - Qatar Biobank
UR - http://www.scopus.com/inward/record.url?scp=85171771482&partnerID=8YFLogxK
U2 - 10.1109/ICIT58056.2023.10225808
DO - 10.1109/ICIT58056.2023.10225808
M3 - Conference contribution
AN - SCOPUS:85171771482
T3 - 2023 International Conference on Information Technology: Cybersecurity Challenges for Sustainable Cities, ICIT 2023 - Proceeding
SP - 190
EP - 195
BT - 2023 International Conference on Information Technology
A2 - Jaber, Khalid Mohammad
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 August 2023 through 10 August 2023
ER -