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Diagnostics
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Innovative Advances in Diagnosis Through Artificial Intelligence
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Innovative Advances in Diagnosis Through Artificial Intelligence

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: closed (28 February 2026) | Viewed by 1783

Editor

Dr. Jorge Mateo Sotos

E-Mail Website
Guest Editor
Expert Medical Analysis Group, Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain
Interests: artificial intelligence; machine learning; deep learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) continues to revolutionize numerous fields, with its impact being particularly evident in healthcare diagnostics. AI-driven advancements in diagnosis promise enhanced precision, efficiency, and personalization in medical decision-making, addressing critical challenges in modern healthcare systems. The computational power and learning capabilities of AI algorithms have led to breakthroughs in pattern recognition, predictive analytics, and image processing. These advancements have redefined diagnostic approaches, allowing healthcare professionals to detect and treat conditions earlier and more accurately. Techniques such as machine learning, deep learning, and natural language processing have become indispensable tools in advancing diagnostics across a range of medical disciplines. This Special Issue, entitled "Innovative Advances in Diagnosis Through Artificial Intelligence", seeks to present cutting-edge research on AI-based diagnostic techniques and their applications in diverse healthcare domains. The scope of this Special Issue includes, but is not limited to, the following topics:          

  • Predictive diagnostics using machine learning         
  • Integration of AI in personalized healthcare       
  • Diagnostic systems leveraging multimodal data      
  • Early disease detection using AI tools
  • Advances and diagnosis in liver diseases
  • Smart diagnostic systems and wearable technology
  • Other related topics addressing AI and advanced diagnostics

Dr. Jorge Mateo Sotos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence
  • medical diagnostics
  • predictive analytics
  • machine learning
  • deep learning
  • digital pathology
  • early disease detection
  • personalized medicine
  • smart healthcare

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

22 pages, 29683 KB  
Article
Transfer Learning and Optimized Machine Learning Techniques for Multiclass Diabetic Retinopathy Classification Using Retinal Images
by Mohammad Reza Yousefi, Ali Bakrani, Elias Ebrahimzadeh and Amin Dehghani
Diagnostics 2026, 16(14), 2189; https://doi.org/10.3390/diagnostics16142189 - 14 Jul 2026
Viewed by 326
Abstract
Background/Objectives: Diabetic Retinopathy (DR) is a prevalent and severe complication of diabetes, caused by prolonged hyperglycemia that damages retinal microvasculature and may ultimately lead to vision loss or blindness. While convolutional neural networks (CNNs) have shown promise in automating DR detection via retinal [...] Read more.
Background/Objectives: Diabetic Retinopathy (DR) is a prevalent and severe complication of diabetes, caused by prolonged hyperglycemia that damages retinal microvasculature and may ultimately lead to vision loss or blindness. While convolutional neural networks (CNNs) have shown promise in automating DR detection via retinal imaging, traditional approaches often suffer from limited diagnostic accuracy, long training times, and reliance on small or imbalanced datasets. Objective: This study evaluates an integrated transfer-learning using adaptive training strategies for multiclass retinal image classification. Methods: The proposed framework integrates transfer learning, feature-space dimensionality reduction, and adaptive training strategies based on an ImageNet pretrained ResNet50 backbone to improve training stability, computational efficiency, and multiclass retinal image classification performance. Results: The proposed Transfer Learning (TL)-based model was trained and evaluated on a large, publicly available dataset of retinal images, achieving an overall accuracy of 84%, maximum class-specific accuracy of 89%, sensitivity of up to 97%, and an F1-score of 92%. These results demonstrate reasonable overall classification performance under constrained data conditions. Conclusions: The proposed framework demonstrates the feasibility of integrating transfer learning and adaptive training strategies for multiclass retinal image classification under constrained benchmark conditions. However, the study is limited by the use of heavily downsampled retinal images, and further validation on high-resolution clinical datasets is required before practical deployment. Future methodological refinement and validation on high-resolution clinical datasets may support development of computer-assisted retinal image analysis systems. Full article
(This article belongs to the Special Issue Innovative Advances in Diagnosis Through Artificial Intelligence)
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25 pages, 4233 KB  
Article
Usefulness of Laboratory-Based Machine Learning for Detection and Severity Classification of Acute Appendicitis in a Resource-Limited Healthcare Setting
by Margarita L. Martinez-Fierro, Jose G. Gonzalez-Rodarte, Sodel Vazquez-Reyes, Manuel Gonzalez-Plascencia, Idalia Garza-Veloz, Perla Velasco-Elizondo, Sidere M. Zorrilla-Alfaro, Jaime Y. Burciaga-Paez, Gonzalo Ibarra-Bañuelos, Luis A. Flores-Chaires and Alejandro Mauricio-Gonzalez
Diagnostics 2026, 16(7), 1090; https://doi.org/10.3390/diagnostics16071090 - 4 Apr 2026
Viewed by 897
Abstract
Background: Acute appendicitis is the most common abdominal surgical emergency, with diagnostic uncertainty greatest in resource-limited settings. Objectives: To develop and internally validate an interpretable, laboratory-driven machine learning approach to assist clinical decision-making in suspected appendicitis, including diagnosis, perforation detection, and [...] Read more.
Background: Acute appendicitis is the most common abdominal surgical emergency, with diagnostic uncertainty greatest in resource-limited settings. Objectives: To develop and internally validate an interpretable, laboratory-driven machine learning approach to assist clinical decision-making in suspected appendicitis, including diagnosis, perforation detection, and surgical severity stratification. Methods: A retrospective cohort of 246 patients with histopathologically confirmed appendicitis and 45 controls with similar abdominal pain was analyzed at a secondary-level hospital in Mexico. After cleaning and imputation, 41 laboratory variables were used to train three models: Random Forest for appendicitis detection and perforation identification, and Support Vector Machine for surgical severity stratification. Class imbalance was addressed with synthetic oversampling, and feature selection prioritized clinical interpretability. Results: Appendicitis detection achieved excellent discrimination (AUC = 0.94), correctly identifying 90% of cases, with 100% specificity. The perforation model reached 100% sensitivity (AUC = 0.875), prioritizing safe detection of high-risk cases, while severity stratification showed moderate performance (AUC = 0.721), correctly identifying 81% of complicated cases without imaging. Conclusions: Laboratory-based ML models accurately detected acute appendicitis and identified all perforated cases using routine data alone, while surgical severity stratification showed moderate discrimination in the absence of imaging. These findings demonstrate the feasibility of laboratory-driven decision support for early risk assessment in resource-limited emergency settings and support further external validation. Full article
(This article belongs to the Special Issue Innovative Advances in Diagnosis Through Artificial Intelligence)
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