Nephrology

Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains.

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined...

Deep computational pathology in breast cancer.

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the...

Development and Cross-Validation of a Nomogram for Chronic Kidney Disease Following Robot-Assisted Radical Cystectomy.

We sought to identify the factors associated with deterioration of renal functions after robot-assisted radical cystectomy, and to develop a nomogram...

Machine Learning in Radiomic Renal Mass Characterization: Fundamentals, Applications, Challenges, and Future Directions.

The purpose of this study is to provide an overview of the traditional machine learning (ML)-based and deep learning-based radiomic approaches, with ...

Predicting mortality in hemodialysis patients using machine learning analysis.

BACKGROUND: Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are pr...

A Single-Center Evaluation of Extended Infusion Piperacillin/Tazobactam for Empiric Treatment in the Intensive Care Unit.

Piperacillin/tazobactam (PTZ) extended infusion (EI) is often used empirically in the intensive care unit (ICU). Gram-negative (GN) organisms with PT...

Fusion of multiple segmentations of medical images using OVASSION and Deep Learning methods: Application to CT-Scans for tumoral kidney.

Nephroblastoma is the most common kidney tumour in children. Its diagnosis is based on imagery. In the SAIAD project, we have designed a platform for ...

Machine learning for predicting long-term kidney allograft survival: a scoping review.

Supervised machine learning (ML) is a class of algorithms that "learn" from existing input-output pairs, which is gaining popularity in pattern recogn...

Unplanned Conversion from Minimally Invasive to Open Kidney Surgery: The Impact of Robotics.

Unplanned conversion from minimally invasive surgery (MIS) to open surgery is a significant challenge, although the frequency of conversion for robot...

Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.

BACKGROUND: Community-acquired acute kidney injury (CA-AKI)-associated hospitalizations impose significant health care needs and contribute to in-hosp...

Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.

IMPORTANCE: Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patie...

Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence.

BACKGROUND: Chronic kidney disease (CKD) stage 3 was divided into two subgroups by eGFR (45 mL/ min 1.73 m). There is difference in prevalence of CKD,...

Serum total indoxyl sulfate and clinical outcomes in hemodialysis patients: results from the Japan Dialysis Outcomes and Practice Patterns Study.

BACKGROUND: Uremic toxins are associated with various chronic kidney disease-related comorbidities. Indoxyl sulfate (IS), a protein-bound uremic toxin...

Prospective Nonrandomized Comparison Between Open and Robot-Assisted Kidney Transplantation: Analysis of Midterm Functional Outcomes.

To compare short- and midterm outcomes of the first 55 patients undergoing robot-assisted kidney transplant (RAKT) with patients undergoing open kidn...

Prediction of the development of acute kidney injury following cardiac surgery by machine learning.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that results in increased morbidity and mortality after c...

Synergistic drug combinations and machine learning for drug repurposing in chordoma.

Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primar...

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice.

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their...

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections.

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes ...

Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data.

STUDY OBJECTIVE: Acute kidney injury occurs commonly and is a leading cause of prolonged hospitalization, development and progression of chronic kidne...

A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study.

BACKGROUND: To date, the missed diagnosis rate of pulmonary hypertension (PH) was high, and there has been limited development of a rapid, simple, and...