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Diagnostics
Special Issues
Digital Technology and Artificial Intelligence in Ophthalmology
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Digital Technology and Artificial Intelligence in Ophthalmology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 437

Special Issue Editor

Prof. Dr. Yanwu Xu

E-Mail Website
Guest Editor
1. Chinese Academy of Sciences, Bei**g, China
2. Singapore Eye Research Institute, Singapore, Singapore
3. School of Future Technol, South China University of Technology, Guangzhou, China
Interests: ophthalmic image analysis; medical image analysis; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

1. Aim:

The cost of blindness to society and individuals is huge, and many cases can be avoided by early intervention. Early and reliable diagnosis strategies and effective treatments are therefore a world priority.

Automatic and semi-automatic ophthalmic image analysis has been widely used in the study of ophthalmic disease assisted diagnosis and treatment, and increasingly percolating into clinical practice.  Significant challenges remain in terms of reliability and validation, number and type of conditions considered, multimodal analysis (e.g., fundus, optical coherence tomography, scanning laser ophthalmoscopy), novel imaging technologies, and the effective transfer of advanced computer vision and machine learning technologies, to mention a few.

This Special Issue aims to bring together scientists, clinicians, and students from multiple disciplines in the growing ophthalmic image analysis community, such as electronic engineering, computer science, mathematics, and medicine, to discuss the latest advancements in the field.

2. Scope：

  • Computer-aided detection and diagnosis of disease
  • Image analysis of novel ophthalmic imaging modalities
  • Multimodal ophthalmic image analysis
  • Cross-modal image generation
  • Ophthalmic image atlases
  • Ophthalmic image analysis in animals
  • Registration of ophthalmic images, including multimodal
  • Segmentation of structures (e.g., vasculature, lesions, landmarks)
  • Combined analysis of images of the eye and other organs
  • Crowd sourcing
  •  ……

Prof. Dr. Yanwu Xu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ophthalmic image analysis
  • multimodal analysis
  • optical coherence tomography
  • digital Technology
  • artificial intelligence

Published Papers (1 paper)

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Research

15 pages, 2431 KiB  
Article
Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy
by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Yasin Gormez, Emek Guldogan and Luca Paolo Ardigò
Diagnostics 2024, 14(13), 1364; https://doi.org/10.3390/diagnostics14131364 - 27 Jun 2024
Viewed by 310
Abstract
Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid [...] Read more.
Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM). Methods: A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro–Wilk test for normality, the Kruskal–Wallis H test for group differences, and the Mann–Whitney U test with Bonferroni correction for post-hoc comparisons. Results: The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR. Conclusion: The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management. Full article
(This article belongs to the Special Issue Digital Technology and Artificial Intelligence in Ophthalmology)
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