Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Lin et al. (Int Urol Nephrol, 2025) contribute to the literature on abdominal aortic calcification (AAC) in chronic kidney disease (CKD) using interpretable machine learning. However, several limitations hinder its clinical applicability. The cross-sectional design restricts causal inference, while the lack of external validation limits generalizability. Critical confounders such as pharmacologic interventions and lifestyle factors are omitted, risking bias in the model. In addition, treating CKD as a binary variable oversimplifies its complexity. Despite the promising use of SHAP analysis, the study lacks clinical translation for actionable risk stratification and personalized treatment. Future research should address these gaps to enhance the model's clinical utility.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s11255-025-04597-w | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Deep Learning-Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for Circumcision Eligibility.
JMIR Res Protoc
September 2025
Department of Urology, Faculty of Medicine, Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Background: Circumcision is a widely practiced procedure with cultural and medical significance. However, certain penile abnormalities-such as hypospadias or webbed penis-may contraindicate the procedure and require specialized care. In low-resource settings, limited access to pediatric urologists often leads to missed or delayed diagnoses.
View Article and Find Full Text PDFPredicting HOMO-LUMO Gaps Using Hartree-Fock Calculated Data and Machine Learning Models.
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFMultimodal Deep Learning for Predicting Postoperative Vault and Selecting ICL Sizes Using AS-OCT and UBM Images.
J Cataract Refract Surg
July 2025
Department of Ophthalmology, West China Hospital of Sichuan University, Chengdu City, Sichuan Province, China.
Purpose: To develop and validate a multimodal deep-learning model for predicting postoperative vault height and selecting implantable collamer lens (ICL) sizes using Anterior Segment Optical Coherence Tomography (AS-OCT) and Ultrasound Biomicroscope (UBM) images combined with clinical features.
Setting: West China Hospital of Sichuan University, China.
Design: Deep-learning study.
Predicting Unplanned Readmission Risk in Patients With Cirrhosis: Complication-Aware Dynamic Classifier Selection Approach.
JMIR Med Inform
September 2025
College of Medical Informatics, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China, 86 13500303273.
Background: Cirrhosis is a leading cause of noncancer deaths in gastrointestinal diseases, resulting in high hospitalization and readmission rates. Early identification of high-risk patients is vital for proactive interventions and improving health care outcomes. However, the quality and integrity of real-world electronic health records (EHRs) limit their utility in developing risk assessment tools.
View Article and Find Full Text PDFDiagnostic and Screening AI Tools in Brazil's Resource-Limited Settings: Systematic Review.
JMIR AI
September 2025
Faculty of Medicine, Universidade Federal de Alagoas, Av. Lourival Melo Mota, S/n - Tabuleiro do Martins, Maceió, 57072-900, Brazil, 558232141461.
Background: Artificial intelligence (AI) has the potential to transform global health care, with extensive application in Brazil, particularly for diagnosis and screening.
Objective: This study aimed to conduct a systematic review to understand AI applications in Brazilian health care, especially focusing on the resource-constrained environments.
Methods: A systematic review was performed.