With the use of technology such as artificial intelligence (AI), it is possible to diagnose Alzheimer’s disease and cardiac conditions well in advance.
FREMONT, CA: Alzheimer’s disease and dementia have affected about 44 million people globally. Alzheimer’s is the sixth-leading cause of death in the U.S with around 5.5 million people suffering from its wrath. Though the majority of them, about 5.3 million are 65 years and older, almost 200,000 younger patients are also dealing with the early-onset of the condition. With the use of technology, such as artificial intelligence (AI), it is possible to diagnose Alzheimer's as early as six years before a clinical diagnosis.
AI for Alzheimer’s
Although there’s no cure for Alzheimer’s disease, drug advancements in recent years are halting the condition’s progression. However, the drugs must be administered early in the course to have an effect on the patients. Positron emission tomography (PET) scans to measure the levels of particular molecules such as glucose in the brain. As the condition progresses slowly, it is difficult to detect the change in glucose level. The above challenge can be addressed by using a machine learning algorithm for the early signs of AD.
AI for Cardiac Conditions
Currently, doctors use risk scores to decide upon treatment decisions for the cardiac conditions. However, they don’t get the desired levels of accuracy. AI has a huge potential to predict cardiac arrests by identifying patterns correlating to the state. Machine learning (ML) has the potential to exploit vast amounts of data and recognize intricate patterns that are difficult to detect using conventional means. Humans find it difficult to think beyond three or four dimensions. According to a study, higher dimensional patterns are better predictors than single-dimensional patterns. Machine learning can be a useful tool for the above purpose too.
ML algorithms learn progressively from the data and based on the extensive analyses, and it can delve into high dimensional patterns, which are critical to identifying patients who are expected to develop cardiac condition effectively. Such analyses can also allow doctors to personalize treatments based on the specific nature of the case.
Doctors collect patient-specific information, such as in the case of cardiac conditions and conditions like AD. ML can integrate this data and correctly predict individual risk.