Cardiometabolic biomarker prediction based on retinal fundus image
<p dir="ltr">Diagnosing common noncommunicable diseases, such as cardiovascular disease and diabetes, typically relies on blood sample analysis for biomarker measurement. This process is invasive, time-consuming, and relatively expensive. To address these limitations, deep learning m...
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2025
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| _version_ | 1864513525556183040 |
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| author | Syed Abdullah Basit (18021787) |
| author2 | Hamada R.H. Al-Absi (22928605) Saleh Musleh (15279190) Tanvir Alam (638619) |
| author2_role | author author author |
| author_facet | Syed Abdullah Basit (18021787) Hamada R.H. Al-Absi (22928605) Saleh Musleh (15279190) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Syed Abdullah Basit (18021787) Hamada R.H. Al-Absi (22928605) Saleh Musleh (15279190) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2025-07-25T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2025.111734 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Cardiometabolic_biomarker_prediction_based_on_retinal_fundus_image/30971449 |
| 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 Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Retinal fundus imaging Deep learning Cardiometabolic biomarkers Non-invasive diagnostics Qatar Biobank Artificial intelligence Cardiovascular disease Diabetes |
| dc.title.none.fl_str_mv | Cardiometabolic biomarker prediction based on retinal fundus image |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diagnosing common noncommunicable diseases, such as cardiovascular disease and diabetes, typically relies on blood sample analysis for biomarker measurement. This process is invasive, time-consuming, and relatively expensive. To address these limitations, deep learning methods were leveraged to estimate common cardiometabolic biomarkers using retinal fundus (RF) images. The study utilized 15,802 RF images from 5,653 participants in the Qatar Biobank (QBB), leading to the development of 19 deep-learning models to estimate biomarkers across seven categories: demographics and body composition, blood pressure, lipid profile, blood profile, hormones, kidney function, and metabolites. The proposed model outperformed existing models for the QBB-specific cohort across all biomarkers, achieving higher R-squared (R<sup>2</sup>) values, lower mean absolute error (MAE), and higher area under the curve (AUC). The proposed model achieved excellent performance in demographic predictions with age (MAE: 2.56, R<sup>2</sup> : 0.93) and gender (Accuracy: 96%, AUC: 0.94). For cardiovascular markers, it showed moderate predictability with systolic blood pressure (MAE: 8.02, R<sup>2</sup> : 0.49) and diastolic blood pressure (MAE: 6.06, R<sup>2</sup> : 0.45). For metabolic markers, the model demonstrated varying performance, with hemoglobin showing strong prediction (MAE: 0.79, R<sup>2</sup> : 0.60) while lipid markers showed moderate performance (total cholesterol MAE: 0.63, R<sup>2</sup> : 0.29). For creatinine, a kidney function marker, we achieved the best results with MAE: 9.00, R<sup>2</sup> : 0.33. Stratified analyses revealed systematic performance variations across gender, age, and disease-specific subgroups, with better predictions in males, young-agers, and non-diabetic participants. External validation of the CAD group confirms the effect of age, gender, and disease on prediction results, suggesting the need for personalized background in consideration for developing AI models. This study presents a promising approach for non-invasive biomarker estimation using retinal images, potentially revolutionizing early intervention and treatment planning in healthcare.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.111734" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.111734</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_de149d6245ee2f4a294f8c172d4f7206 |
| identifier_str_mv | 10.1016/j.engappai.2025.111734 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30971449 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cardiometabolic biomarker prediction based on retinal fundus imageSyed Abdullah Basit (18021787)Hamada R.H. Al-Absi (22928605)Saleh Musleh (15279190)Tanvir Alam (638619)Biomedical and clinical sciencesCardiovascular medicine and haematologyEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningRetinal fundus imagingDeep learningCardiometabolic biomarkersNon-invasive diagnosticsQatar BiobankArtificial intelligenceCardiovascular diseaseDiabetes<p dir="ltr">Diagnosing common noncommunicable diseases, such as cardiovascular disease and diabetes, typically relies on blood sample analysis for biomarker measurement. This process is invasive, time-consuming, and relatively expensive. To address these limitations, deep learning methods were leveraged to estimate common cardiometabolic biomarkers using retinal fundus (RF) images. The study utilized 15,802 RF images from 5,653 participants in the Qatar Biobank (QBB), leading to the development of 19 deep-learning models to estimate biomarkers across seven categories: demographics and body composition, blood pressure, lipid profile, blood profile, hormones, kidney function, and metabolites. The proposed model outperformed existing models for the QBB-specific cohort across all biomarkers, achieving higher R-squared (R<sup>2</sup>) values, lower mean absolute error (MAE), and higher area under the curve (AUC). The proposed model achieved excellent performance in demographic predictions with age (MAE: 2.56, R<sup>2</sup> : 0.93) and gender (Accuracy: 96%, AUC: 0.94). For cardiovascular markers, it showed moderate predictability with systolic blood pressure (MAE: 8.02, R<sup>2</sup> : 0.49) and diastolic blood pressure (MAE: 6.06, R<sup>2</sup> : 0.45). For metabolic markers, the model demonstrated varying performance, with hemoglobin showing strong prediction (MAE: 0.79, R<sup>2</sup> : 0.60) while lipid markers showed moderate performance (total cholesterol MAE: 0.63, R<sup>2</sup> : 0.29). For creatinine, a kidney function marker, we achieved the best results with MAE: 9.00, R<sup>2</sup> : 0.33. Stratified analyses revealed systematic performance variations across gender, age, and disease-specific subgroups, with better predictions in males, young-agers, and non-diabetic participants. External validation of the CAD group confirms the effect of age, gender, and disease on prediction results, suggesting the need for personalized background in consideration for developing AI models. This study presents a promising approach for non-invasive biomarker estimation using retinal images, potentially revolutionizing early intervention and treatment planning in healthcare.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2025.111734" target="_blank">https://dx.doi.org/10.1016/j.engappai.2025.111734</a></p>2025-07-25T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2025.111734https://figshare.com/articles/journal_contribution/Cardiometabolic_biomarker_prediction_based_on_retinal_fundus_image/30971449CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/309714492025-07-25T15:00:00Z |
| spellingShingle | Cardiometabolic biomarker prediction based on retinal fundus image Syed Abdullah Basit (18021787) Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Retinal fundus imaging Deep learning Cardiometabolic biomarkers Non-invasive diagnostics Qatar Biobank Artificial intelligence Cardiovascular disease Diabetes |
| status_str | publishedVersion |
| title | Cardiometabolic biomarker prediction based on retinal fundus image |
| title_full | Cardiometabolic biomarker prediction based on retinal fundus image |
| title_fullStr | Cardiometabolic biomarker prediction based on retinal fundus image |
| title_full_unstemmed | Cardiometabolic biomarker prediction based on retinal fundus image |
| title_short | Cardiometabolic biomarker prediction based on retinal fundus image |
| title_sort | Cardiometabolic biomarker prediction based on retinal fundus image |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Machine learning Retinal fundus imaging Deep learning Cardiometabolic biomarkers Non-invasive diagnostics Qatar Biobank Artificial intelligence Cardiovascular disease Diabetes |