A machine learning based study to assess bone health in a diabetic cohort - Musleh, Saleh; Nazeemudeen, Anjanarani; Islam, Mohammad Tariqul; El Hajj, Nady; Alam, Tanvir (Informatics in Medicine Unlocked, 2022)
Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.
Keywords: Bone mineral density; DXA; Diabetes; Dual energy X-ray absorptiometry; Machine learning; Osteopenia; Osteoporosis; Qatar Biobank (QBB); dept.csc; peer-reviewed

Diabetic Macular Edema Detection based on Non-mydriatic Retinal Image - Gilbert, Njihia Muchori; Hamada, R. H. Al-Absi; Saleh, Musleh; Mohammad, Tariqul Islam; Tanvir, Alam (2023)
Keywords: dept.csc

DMEgrader: Android Mobile Application for Diabetic Macular Edema Grading Prediction - Al-Absi, Hamada R.H.; Muchori, Gilbert Njihia; Musleh, Saleh a; Islam, Mohammad Tariqul; Pai, Anant; Alam, Tanvir (2023 International Conference on Information Technology: Cybersecurity Challenges for Sustainable Cities, ICIT 2023 - Proceeding, 2023)
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. © 2023 IEEE.
Keywords: Deep Learning; Diabetes; Diabetic Macular Edema; Qatar Biobank; dept.csc

A pilot study on diabetes detection using handheld fundus camera and mobile app development - Al-Absi, Hamada R. H.; Muchori, Gilbert Njihia; Musleh, Saleh; Basit, Syed Abdullah; Islam, Mohammad Tariqul; Mou, Younss Ait; Alam, Tanvir (Discover Applied Sciences, 2025)
Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.
Keywords: Android Mobile application; Artificial intelligence; Computer graphics (images); Computer science; Computer vision; DMPred; Diabetes; Diabetes mellitus; Diabetic retinopathy; Fundus (uterus); Fundus camera; Handheld camera; Human–computer interaction; Medicine; Mobile apps; Mobile device; Ophthalmology; Ophthalmoscopy; Retinal; Retinal fundus image; World Wide Web; dept.csc; peer-reviewed