DiaNet v2 deep learning based method for diabetes diagnosis using retinal images
Abstract Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential....
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-49677-y |
| _version_ | 1797349991895793664 |
|---|---|
| author | Hamada R. H. Al-Absi Anant Pai Usman Naeem Fatma Kassem Mohamed Saket Arya Rami Abu Sbeit Mohammed Bashir Maha Mohammed El Shafei Nady El Hajj Tanvir Alam |
| author_facet | Hamada R. H. Al-Absi Anant Pai Usman Naeem Fatma Kassem Mohamed Saket Arya Rami Abu Sbeit Mohammed Bashir Maha Mohammed El Shafei Nady El Hajj Tanvir Alam |
| author_sort | Hamada R. H. Al-Absi |
| collection | DOAJ |
| description | Abstract Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA. |
| first_indexed | 2024-03-08T12:38:21Z |
| format | Article |
| id | doaj.art-53e38a63e8de47e2a633911b984b405d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-08T12:38:21Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-53e38a63e8de47e2a633911b984b405d2024-01-21T12:17:31ZengNature PortfolioScientific Reports2045-23222024-01-0114111110.1038/s41598-023-49677-yDiaNet v2 deep learning based method for diabetes diagnosis using retinal imagesHamada R. H. Al-Absi0Anant Pai1Usman Naeem2Fatma Kassem Mohamed3Saket Arya4Rami Abu Sbeit5Mohammed Bashir6Maha Mohammed El Shafei7Nady El Hajj8Tanvir Alam9College of Science and Engineering, Hamad Bin Khalifa UniversityOphthalmology Section, Department of Surgery, Hamad Medical CorporationOphthalmology Section, Department of Surgery, Hamad Medical CorporationOphthalmology Section, Department of Surgery, Hamad Medical CorporationOphthalmology Section, Department of Surgery, Hamad Medical CorporationOphthalmology Section, Department of Surgery, Hamad Medical CorporationEndocrine Section, Department of Medicine, Hamad Medical CorporationOphthalmology Section, Department of Surgery, Hamad Medical CorporationCollege of Science and Engineering, Hamad Bin Khalifa UniversityCollege of Science and Engineering, Hamad Bin Khalifa UniversityAbstract Diabetes mellitus (DM) is a prevalent chronic metabolic disorder linked to increased morbidity and mortality. With a significant portion of cases remaining undiagnosed, particularly in the Middle East North Africa (MENA) region, more accurate and accessible diagnostic methods are essential. Current diagnostic tests like fasting plasma glucose (FPG), oral glucose tolerance tests (OGTT), random plasma glucose (RPG), and hemoglobin A1c (HbA1c) have limitations, leading to misclassifications and discomfort for patients. The aim of this study is to enhance diabetes diagnosis accuracy by developing an improved predictive model using retinal images from the Qatari population, addressing the limitations of current diagnostic methods. This study explores an alternative approach involving retinal images, building upon the DiaNet model, the first deep learning model for diabetes detection based solely on retinal images. The newly proposed DiaNet v2 model is developed using a large dataset from Qatar Biobank (QBB) and Hamad Medical Corporation (HMC) covering wide range of pathologies in the the retinal images. Utilizing the most extensive collection of retinal images from the 5545 participants (2540 diabetic patients and 3005 control), DiaNet v2 is developed for diabetes diagnosis. DiaNet v2 achieves an impressive accuracy of over 92%, 93% sensitivity, and 91% specificity in distinguishing diabetic patients from the control group. Given the high prevalence of diabetes and the limitations of existing diagnostic methods in clinical setup, this study proposes an innovative solution. By leveraging a comprehensive retinal image dataset and applying advanced deep learning techniques, DiaNet v2 demonstrates a remarkable accuracy in diabetes diagnosis. This approach has the potential to revolutionize diabetes detection, providing a more accessible, non-invasive and accurate method for early intervention and treatment planning, particularly in regions with high diabetes rates like MENA.https://doi.org/10.1038/s41598-023-49677-y |
| spellingShingle | Hamada R. H. Al-Absi Anant Pai Usman Naeem Fatma Kassem Mohamed Saket Arya Rami Abu Sbeit Mohammed Bashir Maha Mohammed El Shafei Nady El Hajj Tanvir Alam DiaNet v2 deep learning based method for diabetes diagnosis using retinal images Scientific Reports |
| title | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| title_full | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| title_fullStr | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| title_full_unstemmed | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| title_short | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| title_sort | dianet v2 deep learning based method for diabetes diagnosis using retinal images |
| url | https://doi.org/10.1038/s41598-023-49677-y |
| work_keys_str_mv | AT hamadarhalabsi dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT anantpai dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT usmannaeem dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT fatmakassemmohamed dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT saketarya dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT ramiabusbeit dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT mohammedbashir dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT mahamohammedelshafei dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT nadyelhajj dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages AT tanviralam dianetv2deeplearningbasedmethodfordiabetesdiagnosisusingretinalimages |