LIVER disorder detection using Machine Learning

In this article, we will discuss how liver disorder detection using machine learning changes the way care is delivered to patients.

Like we have covered in our previous blog, Artificial Intelligence is increasingly being utilized by the healthcare service organizations and the medical fraternity to help combat rising incidents of disorders globally. The amount of data that AI systems can process and drill out insights is so huge that it might take years for medical professionals to arrive at the same insights. But ultimately it is not a replacement for medical professionals like we always advocate. AI is only an enabler of better diagnostics and care delivery and is in no way a replacement for your Doctor. But it does help your doctor to prescribe more efficient care to you. One area in healthcare other than Cancer Diagnostics that is being revolutionized with AI is Liver Disorder diagnostics and care delivery. First, let us have a fact check about liver disorders.

Liver disorders are one of the most deadly life threatening medical conditions in existence today. Often it runs a competition with cancer and at many times both get integrated which leads to fatalities in almost all instances of occurrences. 1 in every 10 Americans is prone to liver disorders every year which is not something to be proud of since that translates roughly into about 35 million patients. It is the 12th leading cause of death in the US.  When you first hear about liver disorders, the next thing you do is link it to the alcoholic behavior of the patient but this figure also includes those who suffer from nonalcoholic steatohepatitis (NASH or Fatty Liver) condition.

Liver disorder detection API

Medical professionals rely on systemic data from blood diagnostics to deal with Liver disorder identification and this is exactly the area where AI has a crucial role to play. AI enabled tools can help in creating automated laboratory environments which can detect symptoms or patterns of blood data that correspond to a known syndrome of liver disorder. We are talking about robotic diagnostics wherein millions of blood data records can be simultaneously analyzed by deep learning AI algorithms and the knowledge derived can be applied to new samples to study any signs of coincidence. In other words, once a blood sample is received in a laboratory, the AI system can scan for the presence of liver disorder strands in those blood samples by swiftly matching with its archive of medical data.

With more and more medical data being stored electronically across the world, it is easier for AI enabled analytical engines to gain access to millions of health records and dive deep to search for all possible patterns in blood samples that indicate a particular liver disorder. These AI tools can automatically collect data from blood testing and diagnostic equipment such as the value of blood parameters and run a comprehensive check of the patient’s previous medical history to evaluate possibilities of occurrences. Periodic medical check-up data or blood sample diagnostic data can be collected and analyzed along with relevant personal traits of the person such as alcoholic beverage intake patterns, food preferences, etc. and arrive at predictive diagnostics. For example, we could say that “hey you are more susceptible to liver diseases by this age if you continue this habit of alcohol intake regularly”.  This opens new avenues in lifestyle advisory for healthcare service providers as well.

AI can really change the way Liver care is delivered and with the ability to access information from global medical data banks, preventive care for liver disorders can be more efficiently streamlined. You do not have to always build AI systems from scratch for your healthcare systems as you can easily subscribe to AI services to integrate with your care delivery systems. To know more about how to improve your liver care and diagnostics services with AI solutions, drop us a line today and our consultants would be there to assist you with the advisory on the go.