Artificial Intelligence, or AI, has been making waves across industries, and healthcare is no exception.
The technology is allowing clinicians to understand diseases better, obtain quicker and more precise diagnostic results, and use vast volumes of data to create appropriate treatment options and care plans.
It’s no wonder that AI in the healthcare industry is anticipated to expand significantly, growing from $20.9 billion in 2024 to an estimated $148.4 billion by 2029.
AI is making a difference in all areas of healthcare, but one area in which its impact is significant is nephrology care. According to an NHS expert, AI can predict kidney failure six times faster. AI models can also improve outcomes for kidney transplant patients.
That is not all, however. There are several other ways AI is revolutionizing kidney disease management. We’ll explore a few of them here:
#1 Decreasing Hospitalization Rates Among Dialysis Patients
A national study in China showed that one-fourth of chronic dialysis patients were admitted to the hospital more than once a year. Hospitalization can interfere with regular dialysis treatments, which can reduce the quality of life of patients. It can substantially increase healthcare system costs.
AI is playing a key role in decreasing hospitalizations for dialysis patients, which is a huge breakthrough. Predictive AI models can show when a patient is at risk for hospitalization.
The Imminent Hospitalization Predictive Model, or IHPM, for instance, can examine over 1,000 factors from dialysis center records, which includes lab results and treatment data. Computer algorithms then process this information and tabulate a list of the top predictors of hospitalization within 7 days, along with a numerical risk score.
The Dialysis Hospitalization Reduction Program (DHRP)—another machine learning tool and model—can determine the probability of repeat hospital admissions for a single patient within a year. It analyzes historical data from more than 100,000 hemodialysis patients treated in-center to identify patterns that lead to hospitalization.
As AI models can give nurses a quick overview of patients’ risk variables, it allows for prompt care team interventions.
#2 Forecasting Acute Kidney Injury
Acute kidney injury (AKI) is a serious concern patients often encounter after cardiac surgery.
A study published in Wiley Online Library examined the presence of AKI during the first 7 days after heart transplantation. Of the 430 patients, 388, or 72% of patients, developed AKI, with 162, or 30%, experiencing severe AKI.
AI is proving to be a lifesaver here. A decrease in urine output or a rapid rise in serum creatinine levels characterizes AKI.
AI algorithms can analyze the urine and predict oliguria—a medical term for low urine output. Real-time and continuous urine output monitoring can help healthcare professionals better predict AKI risk.
A decline in urine output might be the only early sign of AKI, remarks FIZE Medical. Elaborating further, it states that it’s a valuable indicator for healthcare professionals to recognize and manage the conduction promptly. Early detection of API can help improve patient outcomes.
#3 Identifying Signs of Arteriovenous Access Failure in Advance
Permanent vascular access is required for patients with end-stage renal disease.
According to the National Library of Medicine, arteriovenous fistulas (AVFs) are preferred over prosthetic grafts or hemodialysis catheters. They are, however, at risk of failure.
A retrospective study published in Nature revealed that 20 to 50% of AVFs fail to mature sufficiently for use as vascular access in hemodialysis.
Machine learning models can predict the likelihood of an AVF failure, however. They analyze the conditions as well as behaviors that lead to AVF failures, which help them determine whether a fistula can malfunction.
Scientists have also created a smartphone app that analyzes images of a dialysis patient’s vascular access to identify the presence of an access aneurysm and its stage. That is, a weakened area in the vein or artery used to create an arteriovenous fistula (AVF) or graft.
The app employs a deep learning algorithm, similar to those used in facial recognition, to analyze photos of vascular access taken by patients or caregivers. The algorithm detects patterns in the images and quickly notifies users of any aneurysm presence and its severity.
Clinicians can then determine the appropriate treatment, such as surgery or other interventions. The app has the potential to prolong the lifespan of AVFs and arteriovenous grafts while reducing the risk of ruptures.
AI and the Future of Kidney Care
The impact of AI on kidney disease management is nothing short of revolutionary.
The integration of AI into healthcare is still in its early stages, but the progress so far is inspiring. As these technologies continue to evolve, they hold the potential to improve diagnosis, personalize treatments, and help people with kidney disease live healthier, longer lives.
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