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Biomedicines
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The Promise of Artificial Intelligence in Kidney Disease
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The Promise of Artificial Intelligence in Kidney Disease

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4036

Special Issue Editors

Dr. Md Mohaimenul Islam

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Guest Editor
College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
Interests: artificial intelligence; health informatics, big data; health equity; medication adherence
Special Issues, Collections and Topics in MDPI journals
Dr. Ming-Chin Lin

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Guest Editor
Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; machine learning; deep learning; electronic health record; patient safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) in kidney disease is revolutionizing nephrology by enhancing diagnostic accuracy, personalizing treatment, and predicting patient outcomes. AI algorithms and machine learning models analyze vast amounts of patient data to identify early signs of kidney disease, optimize treatment plans, and predict disease progression. These technologies enable more precise and timely interventions, improving patient care and reducing healthcare costs. As AI continues to evolve, it holds the promise of significantly improving the quality of life for kidney disease patients while addressing the challenges of data privacy and ethical considerations in healthcare. This Special Issue explores the transformative impact of AI technologies on nephrology. This Special Issue covers advancements in AI-driven diagnostics, treatment personalization, and predictive analytics, offering insights into how machine learning algorithms and data analytics are enhancing early detection and improving patient outcomes. This Special Issue also addresses ethical considerations, data privacy, and the integration of AI into clinical practice, providing a comprehensive overview of the current state and future directions of AI in kidney disease.

Dr. Md Mohaimenul Islam
Dr. Ming-Chin Lin
Guest Editors

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Keywords

  • artificial intelligence in kidney disease
  • chronic kidney disease
  • acute kidney injury
  • nephrology
  • prediction model
  • machine learning
  • deep learning
  • renal disease
  • ultrasound
  • medical image analysis

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Published Papers (3 papers)

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22 pages, 2506 KiB  
Article
Segmentation of ADPKD Computed Tomography Images with Deep Learning Approach for Predicting Total Kidney Volume
by Ting-Wen Sheng, Djeane Debora Onthoni, Pushpanjali Gupta, Tsong-Hai Lee and Prasan Kumar Sahoo
Biomedicines 2025, 13(2), 263; https://doi.org/10.3390/biomedicines13020263 - 22 Jan 2025
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Abstract
Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD [...] Read more.
Background: Total Kidney Volume (TKV) is widely used globally to predict the progressive loss of renal function in patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD). Typically, TKV is calculated using Computed Tomography (CT) images by manually locating, delineating, and segmenting the ADPKD kidneys. However, manual localization and segmentation are tedious, time-consuming tasks and are prone to human error. Specifically, there is a lack of studies that focus on CT modality variation. Methods: In contrast, our work develops a step-by-step framework, which robustly handles both Non-enhanced Computed Tomography (NCCT) and Contrast-enhanced Computed Tomography (CCT) images, ensuring balanced sample utilization and consistent performance across modalities. To achieve this, Artificial Intelligence (AI)-enabled localization and segmentation models are proposed for estimating TKV, which is designed to work robustly on both NCCT and Contrast-Computed Tomography (CCT) images. These AI-based models incorporate various image preprocessing techniques, including dilation and global thresholding, combined with Deep Learning (DL) approaches such as the adapted Single Shot Detector (SSD), Inception V2, and DeepLab V3+. Results: The experimental results demonstrate that the proposed AI-based models outperform other DL architectures, achieving a mean Average Precision (mAP) of 95% for automatic localization, a mean Intersection over Union (mIoU) of 92% for segmentation, and a mean R2 score of 97% for TKV estimation. Conclusions: These results clearly indicate that the proposed AI-based models can robustly localize and segment ADPKD kidneys and estimate TKV using both NCCT and CCT images. Full article
(This article belongs to the Special Issue The Promise of Artificial Intelligence in Kidney Disease)
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26 pages, 1254 KiB  
Article
Evaluating Feature Selection Methods for Accurate Diagnosis of Diabetic Kidney Disease
by Valeria Maeda-Gutiérrez, Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, Miguel Cruz, José M. Celaya-Padilla, Hamurabi Gamboa-Rosales, Alejandra García-Hernández, Huizilopoztli Luna-García and Klinge Orlando Villalba-Condori
Biomedicines 2024, 12(12), 2858; https://doi.org/10.3390/biomedicines12122858 - 16 Dec 2024
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Abstract
Background/Objectives: The increase in patients with type 2 diabetes, coupled with the development of complications caused by the same disease is an alarming aspect for the health sector. One of the main complications of diabetes is nephropathy, which is also the main [...] Read more.
Background/Objectives: The increase in patients with type 2 diabetes, coupled with the development of complications caused by the same disease is an alarming aspect for the health sector. One of the main complications of diabetes is nephropathy, which is also the main cause of kidney failure. Once diagnosed, in Mexican patients the kidney damage is already highly compromised, which is why acting preventively is extremely important. The aim of this research is to compare distinct methodologies of feature selection to identify discriminant risk factors that may be beneficial for early treatment, and prevention. Methods: This study focused on evaluating a Mexican dataset collected from 22 patients containing 32 attributes. To reduce the dimensionality and choose the most important variables, four feature selection algorithms: Univariate, Boruta, Galgo, and Elastic net were implemented. After selecting suitable features detected by the methodologies, they are included in the random forest classifier, obtaining four models. Results: Galgo with Random Forest achieved the best performance with only three predictors, “creatinine”, “urea”, and “lipids treatment”. The model displayed a moderate classification performance with an area under the curve of 0.80 (±0.3535 SD), a sensitivity of 0.909, and specificity of 0.818. Conclusions: It is demonstrated that the proposed methodology has the potential to facilitate the prompt identification of nephropathy and non-nephropathy patients, and thereby could be used in the clinical area as a preliminary computer-aided diagnosis tool. Full article
(This article belongs to the Special Issue The Promise of Artificial Intelligence in Kidney Disease)
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16 pages, 1144 KiB  
Article
Usage of the Anemia Control Model Is Associated with Reduced Hospitalization Risk in Hemodialysis
by Mario Garbelli, Maria Eva Baro Salvador, Abraham Rincon Bello, Diana Samaniego Toro, Francesco Bellocchio, Luca Fumagalli, Milena Chermisi, Christian Apel, Jovana Petrovic, Dana Kendzia, Jasmine Ion Titapiccolo, Julianna Yeung, Carlo Barbieri, Flavio Mari, Len Usvyat, John Larkin, Stefano Stuard and Luca Neri
Biomedicines 2024, 12(10), 2219; https://doi.org/10.3390/biomedicines12102219 - 28 Sep 2024
Viewed by 1580
Abstract
Introduction: The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESAs), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed [...] Read more.
Introduction: The management of anemia in chronic kidney disease (CKD-An) presents significant challenges for nephrologists due to variable responsiveness to erythropoietin-stimulating agents (ESAs), hemoglobin (Hb) cycling, and multiple clinical factors affecting erythropoiesis. The Anemia Control Model (ACM) is a decision support system designed to personalize anemia treatment, which has shown improvements in achieving Hb targets, reducing ESA doses, and maintaining Hb stability. This study aimed to evaluate the association between ACM-guided anemia management with hospitalizations and survival in a large cohort of hemodialysis patients. Methods: This multi-center, retrospective cohort study evaluated adult hemodialysis patients within the European Fresenius Medical Care NephroCare network from 2014 to 2019. Patients treated according to ACM recommendations were compared to those from centers without ACM. Data on demographics, comorbidities, and dialysis treatment were used to compute a propensity score estimating the likelihood of receiving ACM-guided care. The primary endpoint was hospitalizations during follow-up; the secondary endpoint was survival. A 1:1 propensity score-matched design was used to minimize confounding bias. Results: A total of 20,209 eligible patients were considered (reference group: 17,101; ACM adherent group: 3108). Before matching, the mean age was 65.3 ± 14.5 years, with 59.2% men. Propensity score matching resulted in two groups of 1950 patients each. Matched ACM adherent and non-ACM patients showed negligible differences in baseline characteristics. Hospitalization rates were lower in the ACM group both before matching (71.3 vs. 82.6 per 100 person-years, p < 0.001) and after matching (74.3 vs. 86.7 per 100 person-years, p < 0.001). During follow-up, 385 patients died, showing no significant survival benefit for ACM-guided care (hazard ratio = 0.93; p = 0.51). Conclusions: ACM-guided anemia management was associated with a significant reduction in hospitalization risk among hemodialysis patients. These results further support the utility of ACM as a decision-support tool enhancing anemia management in clinical practice. Full article
(This article belongs to the Special Issue The Promise of Artificial Intelligence in Kidney Disease)
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