Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
This thesis aims to evaluate the utility of quantitative biomarkers derived from optical coherence tomography angiography (OCTA) images for classifying diabetic retinopathy (DR) severity in patients with Type 1 diabetes mellitus (DM). An intensive image transformation process was implemented. We fir...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/414788 |
| Acceso en línea: | https://hdl.handle.net/2117/414788 |
| Access Level: | acceso abierto |
| Palabra clave: | Diabetic retinopathy Angiography Biochemical markers Machine learning Diabetes retinopatia diabètica angiografia per tomografia de coherència òptica biomarcadors quantitatius aprenentatge automàtic diabetis mellitus tipus 1 diabetic retinopathy optical coherence tomography angiography quantitative biomarkers machine learning type 1 diabetes mellitus Retinopatia diabètica Angiografia Marcadors bioquímics Diabetis Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | This thesis aims to evaluate the utility of quantitative biomarkers derived from optical coherence tomography angiography (OCTA) images for classifying diabetic retinopathy (DR) severity in patients with Type 1 diabetes mellitus (DM). An intensive image transformation process was implemented. We first applied two distinct vessel segmentation approaches, Fiji ImageJ for large vessels and Python libraries for comprehensive segmentation, to extract retinal vasculature from OCTA images of the superficial and deep capillary plexuses. A wide range of quantitative vascular biomarkers, including tortuosity metrics, foveal avascular zone measurements, vessel density, and novel geometric and morphological metrics, were then calculated to characterize retinal vascular changes associated with DR progression. Binary classification tasks were formulated to detect the presence of DM, discriminate DR stages, and identify early DR development. Feature selection techniques and robust machine learning models were employed to classify DR severity and evaluate the diagnostic performance of the OCTA-derived biomarkers. The Fiji ImageJ segmentation approach, focusing on large vessels, achieved higher accuracies across all tasks compared to the comprehensive segmentation approach. In feature transformation, the combination of multiple biomarkers consistently outperformed using only tortuosity metrics. The most informative biomarkers identified include vessel perimeter, parafoveal vessel density, and average tortuosity index. The strengths of the thesis lie in the large Type 1 DM cohort, the application of two segmentation approaches, and the extensive evaluation of a wide range of biomarkers. Limitations include potential limited generalizability to Type 2 DM, the influence of image quality on biomarker extraction and dataset imbalance. In conclusion, this study demonstrates the potential of OCTA-derived quantitative biomarkers in classifying DR severity and highlights the importance of a comprehensive assessment of retinal vascular health. The findings contribute to advancing early DR detection and management strategies, aiming to improve clinical outcomes for patients with Type 1 DM. |
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