PhD Scientific Days 2025

Poster Session II. - Z: Euniwell

Development of an Algorithm for Diabetic Retinopathy Discrimination in Primary Care

Előadó neve

Suárez Hernández Daniel, PhD

Előadó munkahelye

Murcia University

Előadó telefonszáma

0034-676365888

Előadó e-mail címe

daniel.suarez@um.es

Az előadás címe

Development of an Algorithm for Diabetic Retinopathy Discrimination in Primary Care

Szerző(k) neve és munkahelye

Daniel Suárez Hernández^1,1, Sandra Catalan Pallares², Paul Ximo Pluitjer Izquierdo², Ruben Cabrera Beirouthy³, Manuel Francisco Dolz Zaragoza², Javier Urios Durá⁴, Sara Jofresa Iserte⁵

1: Murcia University
2: Jaime I University
3: Orihuela Hospital
4: Benejuzar Health Centre
5: San Miguel Health Centre

Bemutatás módja

Poszter

Szekció

Poster Session II. - Z: Euniwell

Language of the presentation

English

Preferred session

Health Sciences

Összefoglaló szövege

Before the advent of AI in the current field of research, it was absolutely necessary for an ophthalmologist to thoroughly study each case of DR. Due to the low proportion of ophthalmologists compared to the population, the wait time for a medical appointment could be excessively long, allowing the disease to progress to a more severe stage that, if diagnosed early, could have been avoided. For a neural network to perform a task, it must first be trained with data, such as images or text, that allow the network to "understand" the problem. In the case of diabetic retinopathy (DR), these data sets are retinal images.
The experiments show that ResNet-50 and DenseNet-169 are the models with the best results for DR classification. ResNet-50 shows the best overall metrics, followed by DenseNet-169, both outperforming the RETFound model in most metrics, except accuracy and training time. However, although metrics such as AUPRC and F1-Score are competitive, they are not sufficiently high for fully autonomous diagnosis in a medical setting.
Regarding DR classification by levels (classes), the imbalance in the dataset drastically affects the model's accuracy. The best-represented classes 0 and 2 perform well, while classes 1 and 3 perform worse. Class 4, although less prevalent, achieves acceptable metrics due to more evident features in the images. Models such as DenseNet and ResNet, which retain information from early layers, show improved performance compared to VGG-19, highlighting the importance of preserving details for correct DR level classification.
In the inference tests, the results are lower than those of the training model, possibly due to the specific tuning for the training data. However, ResNet-50 and DenseNet-169 outperformed RETFound in the most frequently represented classes (0 and 1), although they have limitations in less frequent classes (2, 3, and 4). This reinforces the need to improve the representativeness of the dataset and fine-tune the models to balance their performance across all classes. Finally, it should be noted that, in a medical context, errors that underestimate disease severity can be more serious than those that overestimate it, so these models should be implemented with a conservative approach to ensure patient safety.
Funding: Research conducted with a grant from the Fisabio Foundation Unisalut Program Valencian Community.

University

University of Murcia

Other university, not listed above

Jaime I University

Supervisor

Manuel Francisco Dolz Zaragoza

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

phd.section.field

in doctoral studies after complex exam (PhD)

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

9385

Start

18:18

End

18:24