<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-14T17:02:44Z</responseDate><request verb="GetRecord" identifier="oai:uvadoc.uva.es:10324/80444" metadataPrefix="mods">https://uvadoc.uva.es/oai/request</request><GetRecord><record><header><identifier>oai:uvadoc.uva.es:10324/80444</identifier><datestamp>2025-12-10T20:01:35Z</datestamp><setSpec>com_10324_1134</setSpec><setSpec>com_10324_931</setSpec><setSpec>com_10324_894</setSpec><setSpec>col_10324_1213</setSpec></header><metadata><mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
<mods:name>
<mods:namePart>Diez-Hermano, Sergio</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Ganfornina Álvarez, María Dolores</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Vega-Lozano, Esteban</mods:namePart>
</mods:name>
<mods:name>
<mods:namePart>Sánchez Romero, Diego</mods:namePart>
</mods:name>
<mods:extension>
<mods:dateAvailable encoding="iso8601">2025-12-10T13:11:34Z</mods:dateAvailable>
</mods:extension>
<mods:extension>
<mods:dateAccessioned encoding="iso8601">2025-12-10T13:11:34Z</mods:dateAccessioned>
</mods:extension>
<mods:originInfo>
<mods:dateIssued encoding="iso8601">2020</mods:dateIssued>
</mods:originInfo>
<mods:identifier type="citation">Front Neurosci. 2020 Jun 4;14:516</mods:identifier>
<mods:identifier type="uri">https://uvadoc.uva.es/handle/10324/80444</mods:identifier>
<mods:identifier type="doi">10.3389/fnins.2020.00516</mods:identifier>
<mods:identifier type="publicationtitle">Frontiers in Neuroscience</mods:identifier>
<mods:identifier type="publicationvolume">14</mods:identifier>
<mods:identifier type="essn">1662-453X</mods:identifier>
<mods:abstract>The fruit fly compound eye is a premier experimental system for modeling human&#xd;
neurodegenerative diseases. The disruption of the retinal geometry has been historically&#xd;
assessed using time-consuming and poorly reliable techniques such as histology or&#xd;
pseudopupil manual counting. Recent semiautomated quantification approaches rely&#xd;
either on manual region-of-interest delimitation or engineered features to estimate the&#xd;
extent of degeneration. This work presents a fully automated classification pipeline&#xd;
of bright-field images based on orientated gradient descriptors and machine learning&#xd;
techniques. An initial region-of-interest extraction is performed, applying morphological&#xd;
kernels and Euclidean distance-to-centroid thresholding. Image classification algorithms&#xd;
are trained on these regions (support vector machine, decision trees, random forest,&#xd;
and convolutional neural network), and their performance is evaluated on independent,&#xd;
unseen datasets. The combinations of oriented gradient C gaussian kernel Support&#xd;
Vector Machine [0.97 accuracy and 0.98 area under the curve (AUC)] and fine-tuned&#xd;
pre-trained convolutional neural network (0.98 accuracy and 0.99 AUC) yielded the best&#xd;
results overall. The proposed method provides a robust quantification framework that&#xd;
can be generalized to address the loss of regularity in biological patterns similar to the&#xd;
Drosophila eye surface and speeds up the processing of large sample batches.</mods:abstract>
<mods:language>
<mods:languageTerm>spa</mods:languageTerm>
</mods:language>
<mods:accessCondition type="useAndReproduction">info:eu-repo/semantics/openAccess</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">http://creativecommons.org/licenses/by-nc-nd/4.0/</mods:accessCondition>
<mods:accessCondition type="useAndReproduction">Attribution-NonCommercial-NoDerivatives 4.0 Internacional</mods:accessCondition>
<mods:titleInfo>
<mods:title>Machine Learning Representation of Loss of Eye Regularity in a Drosophila Neurodegenerative Model</mods:title>
</mods:titleInfo>
<mods:genre>info:eu-repo/semantics/article</mods:genre>
</mods:mods></metadata></record></GetRecord></OAI-PMH>