AI & Cardiology
Heart Disease Deaths on rise by 34% in 26 years.
An estimated 62.5 million lives are lost annually due to CVD. From 15% in 1990 to 28% in 2016.
Ischemic heart disease and stroke contribute to 20% of all deaths in India, annually.
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Cardiology & Artificial Intelligence
As the patient generated data is on the rise (heart rate tracker by wearable technologies), Artificial Intelligence is becoming more relevant to manage the CVD. Various cloud-based software’s are already in the process of aggregating, cleaning and analysing the clients’ data. Further they indicate potential risks and provide predictive insights on healthcare outcomes. For instance, deep learning is used develop 3D model of patient’s arteries based on data extracted from CT Scan images. Algorithms are used to simulate blood flow and assess the impact of the blockages.
Why Artificial Intelligence cannot be ignored
- Medical Imaging: Healthcare companies are training machine learning algorithms to improve accuracy of patient scans to better detect disease.
- Risk Prediction: Machine learning is also put to extensive use to predict the risks of any detected cardio vascular disease.
- ECG monitoring: Deep learning is now a comprehensive aide to help automate the process of Atrial fibrillation (AFib), the most common abnormal heart rhythm.
Challenges
- Rise in personalised healthcare: With patients generating personalised data there is a need of personalised healthcare in the CVD domain.
- Migration from “top down” data management approach to “bottom up” data management approach is leading to demand for data analysers with new skill sets.
- There is a deep chasm when it comes to availability of data management skills that includes transformation of data into a uniform format and loading it into an analytical system for final analysis.
- Sheer volume (2,314 exabytes by 2020 – 48% increase annually from 2013) of medical data render physicians at loss as to which data focus on, to search for what and for which desired outcome?
- Machine learning takes different forms based on different schools of thoughts but still are far away from solving problems beyond the school of thoughts they are based on.
Bio Marker Test
Now it is possible to measure multiple bio markers in the same blood sample. 100 – 200 bio markers can now be measured in single drop. The information received is chaotic and if left to classical statistics, it would be very difficult to identify a clear signal.
Role of Artificial Intelligence
How the bio markers behave in a particular disease is studied extensively. This information is fed into the AI system. Later the AI system is referred to when a sample comes in for a test. Based on the behaviour of the Bio Marker the AI is able to provide specific results.
Bio-Medical Image Analysis Scientist, Image Analytics Scientists, Data Scientist – Image Processing are now adopting more and more on Artificial Intelligence to not only up the accuracy standards but also speed up the analysis.
It is time to upskill your current Image Scientists to work with Artificial Intelligence Tools as they have a better chance of adopting to newer AI technologies in the healthcare sector.
Advantages of an upskilled Workforce
Your organization may be considering adopting an emerging AI technology / tool for specific diseases. An upskilled AI team will be easily able to:
- Learn and adopt the technology faster.
- Implement & leverage the AI technology in mainstream in a shorter time.
- Reduced cost of On the Job learning.
- Will be able to assess and differentiate amongst different technologies available in the healthcare sector.
- Effectively work with AI as a team to achieve: Faster Detection & Accurate Diagnosis.