DiaNet v2 deep learning based method for diabetes diagnosis using retinal images
<p dir="ltr">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 me...
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513510261653504 |
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| author | Hamada R. H. Al-Absi (16726299) |
| author2 | Anant Pai (19206079) Usman Naeem (14332701) Fatma Kassem Mohamed (19206082) Saket Arya (17993869) Rami Abu Sbeit (19206085) Mohammed Bashir (5593550) Maha Mohammed El Shafei (19206088) Nady El Hajj (686554) Tanvir Alam (638619) |
| author2_role | author author author author author author author author author |
| author_facet | Hamada R. H. Al-Absi (16726299) Anant Pai (19206079) Usman Naeem (14332701) Fatma Kassem Mohamed (19206082) Saket Arya (17993869) Rami Abu Sbeit (19206085) Mohammed Bashir (5593550) Maha Mohammed El Shafei (19206088) Nady El Hajj (686554) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hamada R. H. Al-Absi (16726299) Anant Pai (19206079) Usman Naeem (14332701) Fatma Kassem Mohamed (19206082) Saket Arya (17993869) Rami Abu Sbeit (19206085) Mohammed Bashir (5593550) Maha Mohammed El Shafei (19206088) Nady El Hajj (686554) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2024-01-18T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-023-49677-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DiaNet_v2_deep_learning_based_method_for_diabetes_diagnosis_using_retinal_images/26363230 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Ophthalmology and optometry Health sciences Health services and systems Information and computing sciences Machine learning Diabetes mellitus (DM) Chronic metabolic disorder Retinal images Diabetes diagnosis DiaNet model Medical Imaging |
| dc.title.none.fl_str_mv | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-49677-y" target="_blank">https://dx.doi.org/10.1038/s41598-023-49677-y</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_01a8ef320c10be7b92a7ae3cc86cde7f |
| identifier_str_mv | 10.1038/s41598-023-49677-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26363230 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DiaNet v2 deep learning based method for diabetes diagnosis using retinal imagesHamada R. H. Al-Absi (16726299)Anant Pai (19206079)Usman Naeem (14332701)Fatma Kassem Mohamed (19206082)Saket Arya (17993869)Rami Abu Sbeit (19206085)Mohammed Bashir (5593550)Maha Mohammed El Shafei (19206088)Nady El Hajj (686554)Tanvir Alam (638619)Biomedical and clinical sciencesClinical sciencesMedical biochemistry and metabolomicsOphthalmology and optometryHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningDiabetes mellitus (DM)Chronic metabolic disorderRetinal imagesDiabetes diagnosisDiaNet modelMedical Imaging<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-49677-y" target="_blank">https://dx.doi.org/10.1038/s41598-023-49677-y</a></p>2024-01-18T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-023-49677-yhttps://figshare.com/articles/journal_contribution/DiaNet_v2_deep_learning_based_method_for_diabetes_diagnosis_using_retinal_images/26363230CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263632302024-01-18T09:00:00Z |
| spellingShingle | DiaNet v2 deep learning based method for diabetes diagnosis using retinal images Hamada R. H. Al-Absi (16726299) Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Ophthalmology and optometry Health sciences Health services and systems Information and computing sciences Machine learning Diabetes mellitus (DM) Chronic metabolic disorder Retinal images Diabetes diagnosis DiaNet model Medical Imaging |
| status_str | publishedVersion |
| 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 |
| topic | Biomedical and clinical sciences Clinical sciences Medical biochemistry and metabolomics Ophthalmology and optometry Health sciences Health services and systems Information and computing sciences Machine learning Diabetes mellitus (DM) Chronic metabolic disorder Retinal images Diabetes diagnosis DiaNet model Medical Imaging |