Our memory reflects our brain health and it is often lost in old age, majorly due to dementia.
Dementia is basically caused by a decline in brain function that is responsible for affecting memory, thinking, language and behavior.
A group of symptoms that affect social abilities to deal with everyday life can vary according to different categories.
Dementia can be categorized as four main types, including Alzheimer’s disease, vascular disease, dementia with Lewy body, and frontotemporal dementia.
With the help of the latest technological advancement scientists have developed new AI models that can help detect dementia with accuracy to treat it better before time.
A group of researchers at Orebro University have developed two new AI models that can analyze the brain’s electrical activity and accurately distinguish between healthy individuals and patients with dementia, especially Alzheimer’s.
Scientists believe that early diagnosis might be helpful in medical science.
Informatics researcher, Orebro University Muhammad Hanif informed that “Early diagnosis is crucial in order to be able to take proactive measures that slow down the progression of the disease and improve the patient’s quality of life.”
In the new study titled An Explainable and Efficient Deep Learning Framework for EEG-based diagnosis of Alzheimer’s Disease and Frontotemporal Dementia, researchers combined two advanced AI methods.
“Traditional machine learning models often lack transparency and are challenged by privacy concerns. Our study aims to address both issues,” says Hanif.
Method 1 was named as ‘Temporal Convolutional Networks’ TCN’s and the other method was described as ‘Long Short-Term Memory’ LSTM networks, to analyze EEG signals—a system that can determine whether a person is sick or healthy.
The researchers succeeded in interpreting the brain’s electrical signals.
As per the results, Alzheimer’s, frontotemporal dementia—the method achieved over 80 percent accuracy.
By dividing EEG signals into various frequency bands – alpha, beta and gamma waves – the AI can identify patterns linked to dementia.
The new AI algorithms can detect long-term changes in the signals and recognize subtle differences between diagnoses.
In addition to that, the explainable technology shows how AI can become a rapid, low-cost and privacy-safe tool for early diagnosis for dementia.
The researchers concluded that Electroencephalography EEG is already a simple and inexpensive method used for primary care and by combining it with AI models, this can be used as a new potential for wider use in healthcare, from specialist clinics to new future home testing.
Orebro University researcher Hanif informed that the research team is continuously putting efforts to explore more AI methods for efficiency and accuracy in medical diagnosis for this common disease.
“We plan to continue the research by expanding to larger and more diverse datasets, exploring more EEG features, including other types of dementia such as vascular dementia and Lewy body dementia.”
“At the same time, we will use explainable AI and ensure strict protection of patient data,” explained Hanif.
