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Biomedicines
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New Advances in Kidney Diseases Research
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New Advances in Kidney Diseases Research

A special issue of Biomedicines (ISSN 2227-9059).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1160

Editor

Dr. Yi-Che Lee

E-Mail Website
Guest Editor
Division of Nephrology, Department of Internal Medicine, E-DA Hospital, Kaohsiung City, Taiwan
Interests: ESRD; CKD; dialysis

Special Issue Information

Dear Colleagues,

Kidney diseases continue to pose a significant challenge to global public health, affecting hundreds of millions of people and resulting in high mortality and a significant economic burden. With the rapid development of molecular biology, genomics, artificial intelligence (AI), big data analytics, and digital health, diagnosis and treatment strategies for kidney diseases are undergoing transformative changes. This Special Issue will showcase the latest advances in kidney disease research, from fundamental mechanistic studies to clinical applications and the integration of new technologies. By highlighting cutting-edge discoveries and innovative solutions, we hope to foster multidisciplinary collaboration and accelerate the translation of research findings into clinical practice. We warmly invite experts and scholars worldwide to contribute high-quality original research and reviews and to join us in promoting scientific progress and improving patient outcomes in the field of nephrology.

This Special Issue will focus on the latest advances and clinical applications in kidney disease research, encompassing basic science, translational medicine, and the integration of innovative technologies. Topics of interest include, but are not limited to, the following:

※  Early diagnosis and prognostic prediction of various kidney diseases (including both acute and chronic conditions);

※  Biomarkers and precision medicine strategies in nephrology;

※  Applications of artificial intelligence (AI), machine learning, and big data analytics in the diagnosis and management of kidney diseases;

※  Novel imaging techniques and digital pathology-assisted diagnostics;

※  Innovations in dialysis techniques and complication management;

※  Immunomodulation and long-term outcomes in kidney transplantation;

※  Stem cell and regenerative medicine approaches to kidney repair.

We welcome original research articles, comprehensive reviews, and short communications, especially those highlighting technological integration and interdisciplinary innovation.

Dr. Yi-Che Lee
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. Biomedicines is an international peer-reviewed open access monthly 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

  • kidney diseases
  • biomarkers
  • artificial intelligence
  • diagnostics
  • dialysis
  • immunomodulation
  • regenerative medicine

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

Published Papers (1 paper)

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Research

38 pages, 11462 KB  
Article
Artificial Intelligence in Renal Imaging: A Multi-Dataset Study for Kidney Disease Classification
by Berçem Afşar Karatepe and Burak Tasci
Biomedicines 2026, 14(5), 1105; https://doi.org/10.3390/biomedicines14051105 - 14 May 2026
Viewed by 472
Abstract
Objectives: To develop and rigorously evaluate a Hybrid Multi-Path Attention Convolutional Neural Network (HMPA-CNN) for the classification of kidney diseases across heterogeneous institutional datasets and imaging modalities. Materials and Methods: The proposed HMPA-CNN employs dual parallel pathways to disentangle spatial (3 × 3 [...] Read more.
Objectives: To develop and rigorously evaluate a Hybrid Multi-Path Attention Convolutional Neural Network (HMPA-CNN) for the classification of kidney diseases across heterogeneous institutional datasets and imaging modalities. Materials and Methods: The proposed HMPA-CNN employs dual parallel pathways to disentangle spatial (3 × 3 convolutions) and textural (5 × 5 convolutions) representations, followed by attention-based feature recalibration and gated fusion. Performance was assessed on five geographically distinct datasets comprising 29,148 CT and MRI images collected from Turkey, Bangladesh, Jordan, Iraq, and publicly available international sources. The evaluation framework included three-class tumor discrimination, four-class renal pathology classification, six-class tumor subtyping, binary kidney stone detection, and chronic kidney disease (CKD) assessment under cross-modality conditions. Results: The model achieved 99.76% overall accuracy on the KidneyNeXt three-class dataset, 99.96% on the four-class multi-institutional CT dataset, and 99.74% on the independent Jordan cohort under a four-class configuration. In the more granular six-class tumor subtyping task, overall accuracy was 96.36%. The same architecture achieved 93.85% overall accuracy on the MRI-based CKD classification task, suggesting that the framework can be adapted to a different imaging modality. Across most classification tasks, specificity exceeded 99%, with benign–malignant misclassification remaining below 2%. Performance declined to 91.96% for kidney stone detection, reflecting the intrinsic difficulty of small-object localization in axial CT images. Conclusions: The dual-path architecture consistently preserved high discriminative performance across institutions, diagnostic granularities, and imaging modalities. Its stable specificity and low benign–malignant confusion suggest potential utility as a supportive tool within renal imaging workflows, particularly for screening and structured diagnostic assistance. Clinically, benign–malignant misclassification is the most critical error, as it may delay oncologic evaluation or lead to unnecessary follow-up. Therefore, the model should be used as a decision-support tool rather than an autonomous diagnostic system. Further prospective validation is required to determine its impact in routine clinical practice. Full article
(This article belongs to the Special Issue New Advances in Kidney Diseases Research)
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