Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency

• URL: http://arxiv.org/abs/2506.11508v1
• Date: Fri, 13 Jun 2025 07:09:12 GMT
• Title: Machine Learning-Based Quantification of Vesicoureteral Reflux with Enhancing Accuracy and Efficiency
• Authors: Muhyeeddin Alqaraleh, Mowafaq Salem Alzboon, Mohammad Subhi Al-Batah, Lana Yasin Al Aesa, Mohammed Hasan Abu-Arqoub, Rashiq Rafiq Marie, Firas Hussein Alsmad,
• Abstract summary: Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems.<n>This study investigates the use of machine learning to improve diagnostic consistency by analyzing voiding cystourethrogram (VCUG) images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vesicoureteral reflux (VUR) is traditionally assessed using subjective grading systems, which introduces variability in diagnosis. This study investigates the use of machine learning to improve diagnostic consistency by analyzing voiding cystourethrogram (VCUG) images. A total of 113 VCUG images were reviewed, with expert grading of VUR severity. Nine image-based features were selected to train six predictive models: Logistic Regression, Decision Tree, Gradient Boosting, Neural Network, and Stochastic Gradient Descent. The models were evaluated using leave-one-out cross-validation. Analysis identified deformation patterns in the renal calyces as key indicators of high-grade VUR. All models achieved accurate classifications with no false positives or negatives. High sensitivity to subtle image patterns characteristic of different VUR grades was confirmed by substantial Area Under the Curve (AUC) values. The results suggest that machine learning can offer an objective and standardized alternative to current subjective VUR assessments. These findings highlight renal calyceal deformation as a strong predictor of severe cases. Future research should aim to expand the dataset, refine imaging features, and improve model generalizability for broader clinical use.

Related papers

• A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Images [2.7440389071148386]
  Inhalation injuries present a challenge in clinical diagnosis and grading due to Conventional grading methods being subjective.<n>This study introduces a novel deep learning-based diagnosis assistant tool for grading inhalation injuries using bronchoscopy images.
  arXiv  Detail & Related papers  (2025-05-13T12:48:36Z)
• Metrics that matter: Evaluating image quality metrics for medical image generation [48.85783422900129]
  This study comprehensively assesses commonly used no-reference image quality metrics using brain MRI data.<n>We evaluate metric sensitivity to a range of challenges, including noise, distribution shifts, and, critically, morphological alterations designed to mimic clinically relevant inaccuracies.
  arXiv  Detail & Related papers  (2025-05-12T01:57:25Z)
• Enhanced Multi-Class Classification of Gastrointestinal Endoscopic Images with Interpretable Deep Learning Model [0.7349657385817541]
  This research introduces a novel approach to enhance classification accuracy using 8,000 labeled endoscopic images from the Kvasir dataset.<n>The proposed architecture eliminates reliance on data augmentation while preserving moderate model complexity.<n>The model achieves a test accuracy of 94.25%, alongside precision and recall of 94.29% and 94.24% respectively.
  arXiv  Detail & Related papers  (2025-03-02T08:07:50Z)
• CAVE-Net: Classifying Abnormalities in Video Capsule Endoscopy [0.1937002985471497]
  We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets.<n>We leverage the unique feature extraction capabilities of each model to enhance the overall accuracy.<n>By using these methods, the proposed framework, CAVE-Net, provides robust feature discrimination and improved classification results.
  arXiv  Detail & Related papers  (2024-10-26T17:25:08Z)
• Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin [0.0]
  Identifying the thromboembolism source in ischemic stroke is crucial for treatment and secondary prevention. This study describes a self-supervised deep learning approach in digital pathology of emboli for classifying ischemic stroke clot origin.
  arXiv  Detail & Related papers  (2024-05-01T23:40:12Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution [47.38982697349244]
  Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc. We propose incorporating active learning into dense regression models to address this problem. Active learning allows models to select the most informative samples for labeling, reducing the overall annotation cost while improving performance.
  arXiv  Detail & Related papers  (2023-05-27T16:33:43Z)
• Performance of GAN-based augmentation for deep learning COVID-19 image classification [57.1795052451257]
  The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
  arXiv  Detail & Related papers  (2023-04-18T15:39:58Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network [12.161266795282915]
  We propose a convolutional neural network (CNN)-based automatic classification method for accurate grading of prostate cancer (PCa) using whole slide histopathology images. A data augmentation method named Patch-Based Image Reconstruction (PBIR) was proposed to reduce the high resolution and increase the diversity of WSIs. A distribution correction module was developed to enhance the adaption of pretrained model to the target dataset.
  arXiv  Detail & Related papers  (2020-11-29T06:42:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.