Deep Learning Model for Computer-Aided Diagnosis of Urolithiasis Detection from Kidney-Ureter-Bladder Images

Review Urology & Nephrology

Artificial intelligence in endourology: emerging technology for individualized care

Jessica C. Dai et al.

Summary: Artificial intelligence has made significant advancements in endourology, enabling optimized diagnosis, decision-making, and prediction of stone recurrence. It also provides new tools for bioinformatics research in this field.

CURRENT OPINION IN UROLOGY (2022)

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Article Computer Science, Information Systems

Deep Supervised Domain Adaptation for Pneumonia Diagnosis From Chest X-Ray Images

Yangqin Feng et al.

Summary: This paper proposes a novel method, Deep Supervised Domain Adaptation (DSDA), to automatically diagnose pneumonia from chest X-ray images. By transferring knowledge from a large-scale source dataset to a small-scale target dataset, DSDA aligns the distributions of the two domains and achieves promising performance in pneumonia diagnosis.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

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Review Medicine, General & Internal

Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions

Ee Jean Lim et al.

Summary: Radiomics has the potential to be used in the diagnosis and treatment of urolithiasis, allowing differentiation of stone types and prediction of outcomes and complications after endourological procedures.

JOURNAL OF CLINICAL MEDICINE (2022)

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Article Computer Science, Information Systems

DEEPFAKE Image Synthesis for Data Augmentation

Nawaf Waqas et al.

Summary: The field of medical imaging lacks a reliable and extensive dataset for training deep learning models. One approach to address this problem is using Generative Adversarial Networks (GANs) to synthesize DEEPFAKE images and augment the data. This study introduces Enhanced-GAN, which combines Progressive Growing GAN (PGGAN) with a subjective self-attention layer, spectral normalization in the discriminator, and pixel normalization in the generator. The performance of Enhanced-GAN is compared to PGGAN using AM Score and Mode Score, and the synthesized data from both models are evaluated using the U-net deep learning model with Dice Coefficient metrics.

IEEE ACCESS (2022)

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Review Computer Science, Artificial Intelligence

Convolutional neural networks in medical image understanding: a survey

D. R. Sarvamangala et al.

Summary: This article discusses the importance of imaging techniques in capturing anomalies of the human body and the use of convolutional neural networks in medical image understanding. It also explores various tasks and applications in medical image diagnosis, focusing on the challenges and potentials of CNN in this field.

EVOLUTIONARY INTELLIGENCE (2022)

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Article Urology & Nephrology

Trends in the prevalence of kidney stones in the United States from 2007 to 2016

Api Chewcharat et al.

Summary: The study revealed an overall increase in the prevalence of kidney stones in the US from 2007 to 2016, with higher prevalence in men compared to women. There were inconsistent trends in the prevalence of kidney stones among different races.

UROLITHIASIS (2021)

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Article Computer Science, Interdisciplinary Applications

Structure and Illumination Constrained GAN for Medical Image Enhancement

Yuhui Ma et al.

Summary: In this paper, a novel medical image quality enhancement method StillGAN is proposed, which outperforms traditional methods and other deep learning methods in experiments. By introducing local structure and illumination constraints to learn both overall characteristics and local details, StillGAN is suitable for different medical image datasets, contributing to improve the efficiency of automated screening, analysis and diagnosis of medical images.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2021)

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Article Urology & Nephrology

Application of Artificial Intelligence-Based Classifiers to Predict the Outcome Measures and Stone-Free Status Following Percutaneous Nephrolithotomy for Staghorn Calculi: Cross-Validation of Data and Estimation of Accuracy

B. M. Zeeshan Hameed et al.

Summary: The study developed a decision support system for predicting the postoperative outcome of kidney stone treatment procedures, particularly PCNL, with high accuracy. By using machine learning techniques and a multi-classifier scheme, the system could effectively predict the stone-free rate by extracting specific features and analyzing data.

JOURNAL OF ENDOUROLOGY (2021)

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Article Urology & Nephrology

Beyond Prevalence: Annual Cumulative Incidence of Kidney Stones in the United States

Gina Tundo et al.

Summary: This study shows that the incidence of nephrolithiasis among adults in the United States increased from 0.6% to 0.9% between 2005 and 2015, with men being more susceptible to the condition. The findings are important for anticipating urological care needs and guiding resource allocation.

JOURNAL OF UROLOGY (2021)

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Review Engineering, Electrical & Electronic

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

S. Kevin Zhou et al.

Summary: Deep learning has been applied in medical imaging tasks with remarkable success, however facing unique challenges. This survey article presents traits of medical imaging, clinical needs, technical challenges, and how emerging trends in DL are addressing these issues. Case studies include digital pathology and various imaging modalities in clinical practice.

PROCEEDINGS OF THE IEEE (2021)

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Article Computer Science, Artificial Intelligence

Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture

Sameena Pathan et al.

Summary: The COVID-19 pandemic has had a devastating global impact due to the lack of automated diagnostic tools and medical resources. This study proposes the use of a Convolutional Neural Network model to assist in diagnosing COVID-19 affected chest X-ray scans, achieving high classification accuracy, which can be used in clinical and remote settings for faster screening of affected patients.

APPLIED SOFT COMPUTING (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

A review of medical image data augmentation techniques for deep learning applications

Phillip Chlap et al.

Summary: Data augmentation has become a popular method for training deep learning models in the field of radiology and radiotherapy, where limited medical image datasets are often encountered. By generating additional training data, data augmentation can improve model performance and has been widely used in state-of-the-art deep learning models.

JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY (2021)

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Article Urology & Nephrology

Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

Masaki Kobayashi et al.

Summary: This study developed a CAD model using deep learning to detect urinary tract stones on plain X-ray images. The best F score was 0.752, with sensitivity and PPV of 0.872 and 0.662, respectively. Although PPV could be improved, the CAD model quickly and automatically detects urinary tract stones, providing potential assistance for primary care physicians in diagnosing urolithiasis.

BMC UROLOGY (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Comparison between Transfer Learning and Data Augmentation on Medical Images Classification

Ahmad Al-qerem et al.

Summary: This study investigated the improvement of image classification in small-sample datasets using two strategies: GANs data augmentation and transfer learning methods. Results show that transfer learning approach significantly outperforms classification-based data augmentation, saving time and achieving higher classification accuracy.

2021 22ND INTERNATIONAL ARAB CONFERENCE ON INFORMATION TECHNOLOGY (ACIT) (2021)

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Article Computer Science, Information Systems

Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification

Matej Gazda et al.

Summary: A self-supervised deep neural network pretrained on an unlabeled chest X-ray dataset is proposed for diagnosing respiratory diseases, showing competitive results without requiring large amounts of labeled training data.

IEEE ACCESS (2021)

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Article Mathematics, Applied

AUTOMATIC SEGMENTATION OF THE FEMUR AND TIBIA BONES FROM X-RAY IMAGES BASED ON PURE DILATED RESIDUAL U-NET

Weihao Shen et al.

Summary: The paper introduces a new approach based on pure dilated residual U-Net for the segmentation of the femur and tibia bones. Experimental results show that this method has fewer parameters, higher accuracy, and converges more rapidly compared to classical U-net and FusionNet, demonstrating its high performance.

INVERSE PROBLEMS AND IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Computer-aided diagnosis in the era of deep learning

Heang-Ping Chan et al.

MEDICAL PHYSICS (2020)

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Review Engineering, Biomedical

Deep learning in medical image registration: a review

Yabo Fu et al.

PHYSICS IN MEDICINE AND BIOLOGY (2020)

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Article Computer Science, Information Systems

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Andreas S. Panayides et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network

Leni Aziyus Fitri et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2020)

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Article Urology & Nephrology

Evaluation of modified STONE score in patients presenting to the emergency department with flank pain

Ahmet Uzun et al.

UROLOGICAL SCIENCE (2020)

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Article Urology & Nephrology

A lower urine white blood cell median can be a predictor of undiscovered urolithiasis in patients with acute urinary tract symptoms

Yuan Chen et al.

UROLOGICAL SCIENCE (2020)

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Article Environmental Sciences

PATIENT DOSES IN COMMON DIAGNOSTIC X-RAY EXAMINATIONS

Vasileios Metaxas et al.

RADIATION PROTECTION DOSIMETRY (2019)

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Article Computer Science, Theory & Methods

A survey on Image Data Augmentation for Deep Learning

Connor Shorten et al.

JOURNAL OF BIG DATA (2019)

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Article Computer Science, Artificial Intelligence

Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization

Anushri Parakh et al.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2019)

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Article Biology

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks

Martin Langkvist et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2018)

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Article Computer Science, Artificial Intelligence

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Maayan Frid-Adar et al.

NEUROCOMPUTING (2018)

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Article Computer Science, Artificial Intelligence

A survey on deep learning in medical image analysis

Geert Litjens et al.

MEDICAL IMAGE ANALYSIS (2017)

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Proceedings Paper Computer Science, Artificial Intelligence

Mask R-CNN

Kaiming He et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

Kenny H. Cha et al.

MEDICAL PHYSICS (2016)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiation Doses in Consecutive CT Examinations from Five University of California Medical Centers

Rebecca Smith-Bindman et al.

RADIOLOGY (2015)

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Article Radiology, Nuclear Medicine & Medical Imaging

Abdominal CT: Comparison of Low-Dose CT With Adaptive Statistical Iterative Reconstruction and Routine-Dose CT With Filtered Back Projection in 53 Patients

Yoshiko Sagara et al.

AMERICAN JOURNAL OF ROENTGENOLOGY (2010)

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Article Medicine, General & Internal

Prospective comparison of unenhanced spiral computed tomography and intravenous urography in the evaluation of acute renal colic

Jia-Hwia Wang et al.

JOURNAL OF THE CHINESE MEDICAL ASSOCIATION (2008)

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Review Medicine, General & Internal

Current concepts - Computed tomography - An increasing source of radiation exposure

David J. Brenner et al.

NEW ENGLAND JOURNAL OF MEDICINE (2007)

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Article Radiology, Nuclear Medicine & Medical Imaging

Comparative evaluation of organ and effective doses for paediatric patients with those for adults in chest and abdominal CT examinations

K. Fujii et al.

BRITISH JOURNAL OF RADIOLOGY (2007)

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Review Engineering, Biomedical

Computer-aided diagnosis in medical imaging: Historical review, current status and future potential

Kunio Doi

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2007)

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Review Urology & Nephrology

Modern approach of diagnosis and management of acute flank pain: Review of all imaging modalities

A Heidenreich et al.

EUROPEAN UROLOGY (2002)

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Deep Learning Model for Computer-Aided Diagnosis of Urolithiasis Detection from Kidney-Ureter-Bladder Images

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