Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica Abierto Inglés Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese... metadata Ahnaf Alavee, Kazi; Hasan, Mehedi; Hasnayen Zillanee, Abu; Mostakim, Moin; Uddin, Jia; Silva Alvarado, Eduardo René; de la Torre Diez, Isabel; Ashraf, Imran y Abdus Samad, Md mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, eduardo.silva@funiber.org, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR (2024) Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence. IEEE Access, 12. pp. 73950-73969. ISSN 2169-3536

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URL Oficial: http://doi.org/10.1109/ACCESS.2024.3405570

Resumen

Humans can carry various diseases, some of which are poorly understood and lack comprehensive solutions. Such a disease can exists in human eye that can affect one or both eyes is diabetic retinopathy (DR) which can impair function, vision, and eventually result in permanent blindness. It is one of those complex complexities. Therefore, early detection of DR can significantly reduce the risk of vision impairment by appropriate treatment and necessary precautions. The primary aim of this study is to leverage cutting-edge models trained on diverse image datasets and propose a CNN model that demonstrates comparable performance. Specifically, we employ transfer learning models such as DenseNet121, Xception, Resnet50, VGG16, VGG19, and InceptionV3, and machine learning models such as SVM, and neural network models like (RNN) for binary and multi-class classification. It has been shown that the proposed approach of multi-label classification with softmax functions and categorical cross-entropy works more effectively, yielding perfect accuracy, precision, and recall values. In particular, Xception achieved an impressive 82% accuracy among all the transfer learning models, setting a new benchmark for the dataset used. However, our proposed CNN model shows superior performance, achieving an accuracy of 95.27% on this dataset, surpassing the state-of-the-art Xception model. Moreover, for single-label (binary classifications), our proposed model achieved perfect accuracy as well. Through exploration of these advances, our objective is to provide a comprehensive overview of the leading methods for the early detection of DR. The aim is to discuss the challenges associated with these methods and highlight potential enhancements. In essence, this paper provides a high-level perspective on the integration of deep learning techniques and machine learning models, coupled with explainable artificial intelligence (XAI) and gradient-weighted class activation mapping (Grad-CAM). We prese...

Tipo de Documento:	Artículo
Palabras Clave:	Diabetic retinopathy, transfer learning, CNN, Xception, inception, Grad-CAM
Clasificación temática:	Materias > Ingeniería
Divisiones:	Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica
Depositado:	17 Jun 2024 23:30
Ultima Modificación:	17 Jun 2024 23:30
URI:	https://repositorio.uniromana.edu.do/id/eprint/12752

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Innovative Application of Chatbots in Clinical Nutrition Education: The E+DIEting_Lab Experience in University Students

Background/Objectives: The growing integration of Artificial Intelligence (AI) and chatbots in health professional education offers innovative methods to enhance learning and clinical preparedness. This study aimed to evaluate the educational impact and perceptions in university students of Human Nutrition and Dietetics, regarding the utility, usability, and design of the E+DIEting_Lab chatbot platform when implemented in clinical nutrition training. Methods: The platform was piloted from December 2023 to April 2025 involving 475 students from multiple European universities. While all 475 students completed the initial survey, 305 finished the follow-up evaluation, representing a 36% attrition rate. Participants completed surveys before and after interacting with the chatbots, assessing prior experience, knowledge, skills, and attitudes. Data were analyzed using descriptive statistics and independent samples t-tests to compare pre- and post-intervention perceptions. Results: A total of 475 university students completed the initial survey and 305 the final evaluation. Most university students were females (75.4%), with representation from six languages and diverse institutions. Students reported clear perceived learning gains: 79.7% reported updated practical skills in clinical dietetics and communication were updated, 90% felt that new digital tools improved classroom practice, and 73.9% reported enhanced interpersonal skills. Self-rated competence in using chatbots as learning tools increased significantly, with mean knowledge scores rising from 2.32 to 2.66 and skills from 2.39 to 2.79 on a 0–5 Likert scale (p < 0.001 for both). Perceived effectiveness and usefulness of chatbots as self-learning tools remained positive but showed a small decline after use (effectiveness from 3.63 to 3.42; usefulness from 3.63 to 3.45), suggesting that hands-on experience refined, but did not diminish, students’ overall favorable views of the platform. Conclusions: The implementation and pilot evaluation of the E+DIEting_Lab self-learning virtual patient chatbot platform demonstrate that structured digital simulation tools can significantly improve perceived clinical nutrition competences. These findings support chatbot adoption in dietetics curricula and inform future digital education innovations.

Producción Científica

Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Imanol Eguren García mail imanol.eguren@uneatlantico.es, Álvaro Lasarte García mail , Arturo Ortega-Mansilla mail arturo.ortega@uneatlantico.es, Thomas Prola mail thomas.prola@uneatlantico.es, Sandra Sumalla Cano mail sandra.sumalla@uneatlantico.es,

Elío Pascual

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Suicide Ideation Detection Using Social Media Data and Ensemble Machine Learning Model

Identifying the emotional state of individuals has useful applications, particularly to reduce the risk of suicide. Users’ thoughts on social media platforms can be used to find cues on the emotional state of individuals. Clinical approaches to suicide ideation detection primarily rely on evaluation by psychologists, medical experts, etc., which is time-consuming and requires medical expertise. Machine learning approaches have shown potential in automating suicide detection. In this regard, this study presents a soft voting ensemble model (SVEM) by leveraging random forest, logistic regression, and stochastic gradient descent classifiers using soft voting. In addition, for the robust training of SVEM, a hybrid feature engineering approach is proposed that combines term frequency-inverse document frequency and the bag of words. For experimental evaluation, “Suicide Watch” and “Depression” subreddits on the Reddit platform are used. Results indicate that the proposed SVEM model achieves an accuracy of 94%, better than existing approaches. The model also shows robust performance concerning precision, recall, and F1, each with a 0.93 score. ERT and deep learning models are also used, and performance comparison with these models indicates better performance of the SVEM model. Gated recurrent unit, long short-term memory, and recurrent neural network have an accuracy of 92% while the convolutional neural network obtains an accuracy of 91%. SVEM’s computational complexity is also low compared to deep learning models. Further, this study highlights the importance of explainability in healthcare applications such as suicidal ideation detection, where the use of LIME provides valuable insights into the contribution of different features. In addition, k-fold cross-validation further validates the performance of the proposed approach.

Producción Científica

Erol KINA mail , Jin-Ghoo Choi mail , Abid Ishaq mail , Rahman Shafique mail , Mónica Gracia Villar mail monica.gracia@uneatlantico.es, Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Isabel de la Torre Diez mail , Imran Ashraf mail ,

KINA

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In silico prediction, molecular docking and simulation of natural flavonoid apigenin and xanthoangelol E against human metapneumovirus

Human metapneumovirus (hMPV) is one of the potential pandemic pathogens, and it is a concern for elderly subjects and immunocompromised patients. There is no vaccine or specific antiviral available for hMPV. We conducted an in-silico study to predict initial antiviral candidates against human metapneumovirus. Our methodology included protein modeling, stability assessment, molecular docking, molecular simulation, analysis of non-covalent interactions, bioavailability, carcinogenicity, and pharmacokinetic profiling. We pinpointed four plant-derived bio-compounds as antiviral candidates. Among the compounds, apigenin showed the highest binding affinity, with values of − 8.0 kcal/mol for the hMPV-F protein and − 7.6 kcal/mol for the hMPV-N protein. Molecular dynamic simulations and further analyses confirmed that the protein-ligand docked complexes exhibited acceptable stability compared to two standard antiviral drugs. Additionally, these four compounds yielded satisfactory outcomes in bioavailability, drug-likeness, and ADME-Tox (absorption, distribution, metabolism, excretion, and toxicity) and STopTox analyses. This study highlights the potential of apigenin and xanthoangelol E as an initial antiviral candidate, underscoring the necessity for wet-lab evaluation, preclinical and clinical trials against human metapneumovirus infection.

Producción Científica

Hasan Huzayfa Rahaman mail , Afsana Khan mail , Nadim Sharif mail , Wasifuddin Ahmed mail , Nazmul Sharif mail , Rista Majumder mail , Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Isabel De la Torre Díez mail , Shuvra Kanti Dey mail ,

Rahaman

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Loneliness, Aloneness, and Adherence to the Mediterranean Diet in Southern Italian Individuals

Background/Objectives: Research across multiple disciplines has explored how nutrition is shaped by social isolation and feelings of loneliness, especially in the elderly population. Evidence from neuroscience highlights that loneliness may alter eating patterns, encouraging emotional eating or other compensatory food behaviors. Conversely, isolation from social contexts is often linked to a reduced variety of nutrient intake. This study set out to examine how psychosocial aspects, particularly social connectedness and feeling alone, relate to adherence to the Mediterranean diet among older adults residing in Sicily, southern Italy. Methods: Dietary habits of 883 adults were collected through food frequency questionnaires and assessed for adherence to the Mediterranean diet. Loneliness was measured through a targeted question from a standardized tool designed to capture depressive symptoms. Direct questions asked whether participants were engaged in social networks, such as family, friends and neighborhoods, or religious communities, in order to assess objective aloneness. Logistic regression analyses were performed to assess associations between variables of interest. Results: After accounting for potential confounders, both loneliness and aloneness showed an association with stronger adherence to the Mediterranean diet. Specifically, individuals experiencing loneliness and aloneness were less likely to have high adherence to the Mediterranean diet (OR = 0.28, 95% CI: 0.15, 0.51, and OR = 0.26, 95% CI: 0.12, 0.54, respectively). Conclusions: These findings underscore the importance of fostering social engagement among older populations, who may particularly benefit from maintaining active social ties to support healthier eating behaviors.

Producción Científica

Justyna Godos mail , Giuseppe Caruso mail , Marco Antonio Olvera-Moreira mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Melannie Toral-Noristz mail , Raynier Zambrano-Villacres mail , Alice Leonardi mail , Rosa M. G. Balzano mail , Fabio Galvano mail , Sabrina Castellano mail , Giuseppe Grosso mail ,

Godos

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Yerba Mate (Ilex paraguariensis) and Rheumatoid Arthritis: A Systematic Review of Mechanistic and Clinical Evidence

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease driven by persistent inflammation and oxidative stress. Ilex paraguariensis (yerba mate) contains bioactive compounds—particularly chlorogenic acids, quercetin, and rutin—with documented antioxidant and anti-inflammatory properties. Objectives: To systematically review the mechanistic and clinical evidence on Ilex paraguariensis and its main constituents in RA-relevant inflammatory, oxidative, and bone metabolic pathways. Methods: Following PRISMA 2020, PubMed/MEDLINE, LILACS, and SciELO were searched up to September 2025. Eligible studies included yerba mate preparations (last 10 years) or isolated compounds (last 5 years) assessing RA-relevant clinical, inflammatory, oxidative, or bone metabolic outcomes. Non-original studies were excluded. Owing to heterogeneity, findings were narratively synthesized, and risk of bias was evaluated using RoB 2, ROBINS-I, OHAT, and SYRCLE. Results: Twenty-three studies met inclusion criteria: 11 human (clinical or observational), 7 human-based in vitro, and 5 animal studies. Interventions with yerba mate infusions or standardized extracts suggest reductions in inflammatory markers (e.g., C-reactive protein, interleukin-6) and indicate improvements in glutathione-related oxidative balance. Evidence from isolated compounds, particularly quercetin and rutin, suggests comparable anti-inflammatory and antioxidant effects. Preclinical studies appear to indicate modulation of inflammatory and redox pathways relevant to RA. Conclusions: Yerba mate and its constituents show preliminary indications of anti-inflammatory and antioxidant effects with potential relevance to RA pathophysiology. However, in the absence of clinical trials in RA patients, conclusions remain tentative, constrained by small sample sizes, methodological heterogeneity, species differences, and internal validity concerns. Future research should include rigorously designed randomized trials and mechanistic studies using advanced human-relevant platforms, such as organoids and organ-on-chip systems.

Producción Científica

Manuela Cassotta mail manucassotta@gmail.com, Qingwei Cao mail , Haixia Hu mail , Carlos Rabeiro Martinez mail , Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Santos Gracia Villar mail santos.gracia@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Francesca Giampieri mail francesca.giampieri@uneatlantico.es,

Cassotta

Enhancing Early Detection of Diabetic Retinopathy Through the Integration of Deep Learning Models and Explainable Artificial Intelligence

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