AIMC Topic: Kidney

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Predicting renal damage in children with IgA vasculitis by machine learning.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Children with IgA Vasculitis (IgAV) may develop renal complications, which can impact their long-term prognosis. This study aimed to build a machine learning model to predict renal damage in children with IgAV and analyze risk factors for...

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images.

Journal of imaging informatics in medicine
To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning ...

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

Journal of nephrology
BACKGROUND: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR)...

Development of Convolutional Neural Network to Segment Ultrasound Images of Histotripsy Ablation.

IEEE transactions on bio-medical engineering
OBJECTIVE: Histotripsy is a focused ultrasound therapy that ablates tissue via the action of bubble clouds. It is under investigation to treat a number of ailments, including renal tumors. Ultrasound imaging is used to monitor histotripsy, though the...

Automated Analysis of Split Kidney Function from CT Scans Using Deep Learning and Delta Radiomics.

Journal of endourology
Differential kidney function assessment is an important part of preoperative evaluation of various urological interventions. It is obtained through dedicated nuclear medical imaging and is not yet implemented through conventional Imaging. We assess...

Development and External Validation of a Multidimensional Deep Learning Model to Dynamically Predict Kidney Outcomes in IgA Nephropathy.

Clinical journal of the American Society of Nephrology : CJASN
KEY POINTS: A dynamic model predicts IgA nephropathy prognosis based on deep learning. Longitudinal clinical data and deep learning improve predictive accuracy and interpretability in GN.

Vision-based estimation of manipulation forces by deep learning of laparoscopic surgical images obtained in a porcine excised kidney experiment.

Scientific reports
In robot-assisted surgery, in which haptics should be absent, surgeons experience haptics-like sensations as "pseudo-haptic feedback". As surgeons who routinely perform robot-assisted laparoscopic surgery, we wondered if we could make these "pseudo-h...

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

International journal of medical informatics
OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Bone
Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bla...