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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Detalles Bibliográficos
Autor: Guijarro Heeb, Torben
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
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repository_id_str
spelling Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathyGuijarro Heeb, TorbenDiabetic retinopathyAngiographyBiochemical markersMachine learningDiabetesretinopatia diabèticaangiografia per tomografia de coherència òpticabiomarcadors quantitatiusaprenentatge automàticdiabetis mellitus tipus 1diabetic retinopathyoptical coherence tomography angiographyquantitative biomarkersmachine learningtype 1 diabetes mellitusRetinopatia diabèticaAngiografiaMarcadors bioquímicsaprenentatge automàticDiabetisÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticThis 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.Universitat Politècnica de CatalunyaVellido Alcacena, AlfredoRomero Merino, Enrique20242024-05-1620242024-09-23master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/414788reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4147882026-05-27T15:37:01Z
dc.title.none.fl_str_mv Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
title Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
spellingShingle Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
Guijarro Heeb, Torben
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
aprenentatge automàtic
Diabetis
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
title_full Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
title_fullStr Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
title_full_unstemmed Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
title_sort Evaluating quantitative biomarkers in optical coherence tomography angiography images to predict diabetic retinopathy
dc.creator.none.fl_str_mv Guijarro Heeb, Torben
author Guijarro Heeb, Torben
author_facet Guijarro Heeb, Torben
author_role author
dc.contributor.none.fl_str_mv Vellido Alcacena, Alfredo
Romero Merino, Enrique
dc.subject.none.fl_str_mv 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
aprenentatge automàtic
Diabetis
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic 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
aprenentatge automàtic
Diabetis
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-05-16
2024
2024-09-23
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/414788
url https://hdl.handle.net/2117/414788
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869408656843341824
score 15,811543

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