On the Prediction of Diabetic Retinopathy Progression from OCTA Images Using a Deep Learning Framework
Purpose: The primary purpose of this study is to develop deep learning (DL) frameworks capable of assessing the progression of diabetic retinopathy (DR) in patients with Type 1 diabetes mellitus (DM1) over a 5-year span (±2 years), addressing a significant gap in the current literature and enhancing...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| 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:2117/430200 |
| Acceso en línea: | https://hdl.handle.net/2117/430200 |
| Access Level: | acceso abierto |
| Palabra clave: | Deep learning (Machine learning) Diabetic retinopathy Aprenentatge profund Diabetis mellitus tipus 1 Retinopatia diabètica Angiografia per tomografia de coherència òptica Xarxes neuronals convolucionals Aprenentatge per transferència Deep Learning Type 1 Diabetes Mellitus Diabetic Retinopathy Optical Coherence Tomography Angiography Convolutional Neural Networks Transfer Learning Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Purpose: The primary purpose of this study is to develop deep learning (DL) frameworks capable of assessing the progression of diabetic retinopathy (DR) in patients with Type 1 diabetes mellitus (DM1) over a 5-year span (±2 years), addressing a significant gap in the current literature and enhancing the potential for early detection in ophthalmological clinical applications. Data: The dataset utilized in this research comprises Optical Coherence Tomography Angiography (OCTA) images collected from a clinical trial conducted at the Hospital Clínic of Barcelona. This dataset includes images from 171 participants, focusing on patients with DM1, featuring varying degrees of DR progression over a 5-year period (±2 years), along with multiple image resolutions (3×3 mm and 6×6 mm) and capillary plexus depths - superficial capillary plexus (SCP) and deep capillary plexus (DCP). Methods: The data pipeline begins with image concatenation to create a unified fourdimensional (4D) OCTA input, combining 3×3 mm and 6×6 mm scans across two depth levels - the SCP and DCP - followed by min-max normalization. To address class imbalance, a combination of techniques was employed, including undersampling, class weights, batch balance, and data augmentation, both individually and in combination. Transfer Learning (TL) was applied using DenseNet201, Inception-V3, and VGG19, supervised learning (SL) models, chosen to leverage existing knowledge from ImageNet dataset. The main metrics used are F1-Score (F1), Accuracy (ACC) and Area Under the Curve (AUC). Results: The best-performing models, obtained through 5-fold cross-validation, are DenseNet201 (F1: 64.50%; ACC: 77.50%; AUC: 82.03%), with ACC per class of 53.33% for Better, 60.00% for Worse, and 83.72% for No Change, and Inception-V3 (F1: 58.03%; ACC: 71.79%; AUC: 76.55%), with per-class ACCs of 46.67% for Better, 48.57% for Worse, and 71.78% for No Change. DenseNet201 employed entry layers for detail extraction and added top layers above the base model, using a balanced batch size. In contrast, Inception-V3 added only top layers and applied data augmentation via cropping to 100% of the data, while also incorporating class weights to address imbalance. Conclusion: This study was the first to focus exclusively on the assessment of the progression of DR on DM1 patients, addressing a critical gap in the literature. Despite limitations in dataset size and resolution, it demonstrated that DenseNet201 and Inception-V3 are effective models when combined with TL and robust imbalancehandling techniques such as class weights, batch balance, and data augmentation - successfully minimizing misclassifications, particularly among patients likely to experience worsening. |
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