Abstract
It has been seen that in the last one decade, AI/ML/DL has been considered a core research area in healthcare as we know that kidney is one among the important internal body organs helps in regulation of the fluid within the body such that it relieves the body from the existence of the waste in the body. it is difficult to detect early on by normal clinical process. Many researchers have focused their work to identify the kidney disease or classify the kidney disease using computational technology because of the mortality rate is very high in kidney patients. Primary focus of this paper is review the current research work based on computational advancement in the area of kidney disease and also identify the gaps or future scope to identify and predict the kidney disease at earlier stage.
Highlights
Introduction of Chronic Kidney Disease (CKD)Initial intervention generally reduces severe disease progress
Near about 8 million peoples are affected by various kinds of kidney related diseases most of kidney disease categorized as Chronic Kidney Disease (CKD) or Acute Kidney Injury (AKI)
Various deep learning and machine learning models based on Computer-Aided Diagnosis techniques for CKD have been reported
Summary
Initial intervention generally reduces severe disease progress. A report published by Elsevier in February 2020 it shown in figure 1, is indicating the worldwide Growth of Kidney Disease problem is alarming. Chronic Kidney Disease are progressive and non-recoverable in most of cases and Acute kidney injury are 100% recoverable. It is observed that during the treatment of Acute kidney injury around 30% to 40% patients started to suffer the Chronic Kidney Disease (CKD), which is very serious and danger situation for the patients. To identify the kidney disease, nephrologist (doctors) suggests the different medical reports based on patient’s situation. These reports are urine(urinalysis) key parameters are (RBCs - £2 RBCs/hpf, WBCs - £2-5 WBCs/hpf), blood tests, imaging tests such as CT scans or MRIs, comprehensive metabolic panel, urine culture, complete blood count, liver or renal panel, Antibiotic Susceptibility Testing and kidney biopsy. In the view of the technological advancements for diagnosing the KD (Kidney Disease) several automated tools can help to identify and classify Kidney Disease Because of in healthcare various tools has been implemented to classify the multiple diseases using AI, Deep Learning and Machine Learning i.e advanced computational technologies can play an important role to predict Kidney Disease at earlier stages
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