artificial kidney

The mortality rate of patients with sepsis-induced acute kidney injury (S-AKI) is notably elevated. The initial categorization of prognostic indicators has a beneficial impact on elucidating and enhancing disease outcomes. This study aimed to predict the mortality risk of S-AKI patients by employing machine learning techniques. The sample size determined by a four-step procedure yielded 1508 samples. The research design necessitated the inclusion of individuals with S-AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database...

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes...

BACKGROUND: The use of artificial cardiac pacemakers has grown steadily in line with the aging population. OBJECTIVES: To determine the rates of hospital readmissions and complications after pacemaker implantation or pulse generator replacement and to assess the impact of these events on annual treatment costs from the perspective of the Unified Health System (SUS). METHODS: A prospective registry, with data derived from clinical practice, collected during index hospitalization and during the first 12 months after the surgical procedure...

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy...

The American Transplant Congress (ATC) 2023, held in San Diego, California, emerged as a pivotal platform showcasing the latest advancements in organ machine perfusion, a key area in solid organ and tissue transplantation. This year's congress, attended by over 4500 participants, including leading experts, emphasized innovations in machine perfusion technologies across various organ types, including liver, kidney, heart, and lung. A total of 85 abstracts on organ machine perfusion were identified. Noteworthy advancements included the use of normothermic machine perfusion in mitigating ex-situ reperfusion injury in liver transplantation, the potential of biomarkers in assessing organ quality, and the impact of machine perfusion on graft survival and ischemic cholangiopathy incidence...

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce...

A seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems...

In today's industrialized society food consumption has changed immensely toward heightened red meat intake and use of artificial sweeteners instead of grains and vegetables or sugar, respectively. These dietary changes affect public health in general through an increased incidence of metabolic diseases like diabetes and obesity, with a further elevated risk for cardiorenal complications. Research shows that high red meat intake and artificial sweeteners ingestion can alter the microbial composition and further intestinal wall barrier permeability allowing increased transmission of uremic toxins like p-cresyl sulfate, indoxyl sulfate, trimethylamine n-oxide and phenylacetylglutamine into the blood stream causing an array of pathophysiological effects especially as a strain on the kidneys, since they are responsible for clearing out the toxins...

BACKGROUND: Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed. METHODS: A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients...

Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the concurrently used models are of limited discriminative or calibration power and there is a critical need to improve the selection process. We aimed to assess the value of various artificial intelligence (AI) algorithms to improve the risk stratification index. We evaluated pre-transplant variables among 66 914 live-donor kidney transplants (performed between 01/12/2007-01/06/2021) from the United Network of Organ Sharing database, randomized into training (80%) and test (20%) sets...

Acute kidney injury (AKI) is a common problem in critically ill patients and is associated with increased morbidity and mortality. Since 2012, AKI has been defined according to the KDIGO (Kidney Disease Improving Global Outcome) guidelines. As some biomarkers are now available that can provide useful clinical information, a new definition including a new stage 1S has been proposed by an expert group of the Acute Disease Quality Initiative (ADQI). At this stage, classic AKI criteria are not yet met, but biomarkers are already positive defining subclinical AKI...

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used...

African swine fever (ASF) is a disease that is a growing threat to the global swine industry. Regulations and restrictions are placed on swine movement to limit the spread of the virus. However, these are costly and time-consuming. Therefore, this study aimed to determine if high-pressure processing (HPP) sanitization techniques would be effective against the ASF virus. Here, it was hypothesized that HPP could inactivate or reduce ASF virus infectivity in tissue homogenates. To test this hypothesis, 30 aliquots of each homogenate (spleen, kidney, loin) were challenge-infected with the Turin/83 strain of ASF, at a 10 7...

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores...

Background : Although acute kidney injury (AKI) is a common complication in patients undergoing hematopoietic stem cell transplantation (HSCT), its prophylaxis remains a clinical challenge. Attempts at prevention or early diagnosis focus on various methods for the identification of factors influencing the incidence of AKI. Our aim was to test the artificial intelligence (AI) potential in the construction of a model defining parameters predicting AKI development. Methods : The analysis covered the clinical data of children followed up for 6 months after HSCT...

BACKGROUND: The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for renal replacement therapy (RRT) and disease progression for patients with stage 3-5 CKD. METHODS: This was a retrospective, closed cohort, observational study...

OBJECTIVES: To perform a systematic review on artificial intelligence(AI) performances to detect urinary stones. METHODS: A PROSPERO-registered(CRD473152) systematic search of Scopus, Web of Science, Embase and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume")...

Trends in high-sensitivity cardiac troponin I (hs-cTnI) after lung transplant (LT) and its clinical value are not well stablished. This study aimed to determine kinetics of hs-cTnI after LT, factors impacting hs-cTnI and clinical outcomes. LT recipients from 2015 to 2017 at Toronto General Hospital were included. Hs-cTnI levels were collected at 0-24 h, 24-48 h and 48-72 h after LT. The primary outcome was invasive mechanical ventilation (IMV) >3 days. 206 patients received a LT (median age 58, 35...

Sugar in the blood can harm individuals and their vital organs, potentially leading to blindness, renal illness, as well as kidney and heart diseases. Globally, diabetic patients face an average annual mortality rate of 38%. This study employs Chi-square, mutual information, and sequential feature selection (SFS) to choose features for training multiple classifiers. These classifiers include an artificial neural network (ANN), a random forest (RF), a gradient boosting (GB) algorithm, Tab-Net, and a support vector machine (SVM)...