Deans' stroke musings: AI That Detects Post-Stroke Depression Type Can Help Stroke Survivors Get Right Treatment

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Thursday, July 1, 2021

AI That Detects Post-Stroke Depression Type Can Help Stroke Survivors Get Right Treatment

Why aren't we preventing depression by having EXACT STROKE REHAB PROTOCOLS LEADING TO 100% RECOVERY? This sounds impressive but it is not.

AI That Detects Post-Stroke Depression Type Can Help Stroke Survivors Get Right Treatment

Summary: New AI technology can detect a patient’s stroke depression type, and improve treatment options.

Source: Hiroshima University

An AI developed by Japanese researchers might soon help stroke survivors get the right treatment by detecting a patient’s post-stroke depression (PSD) type, a frequently seen but often overlooked neuropsychiatric manifestation after a stroke that could impair functional recovery.

The AI was developed by Hiroshima University (HU) researchers using a probabilistic artificial neural network called log-linearized Gaussian mixture network. The neural network was trained to distinguish between depression, apathy, or anxiety based on 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients.

Details about their research that analyzed the relationship between PSD and activities of daily living independence, degree of paralysis, stress awareness, and higher brain function using machine learning are published in Scientific Reports.

Early PSD detection

The researchers said each PSD type might have different underlying neuroanatomic mechanisms which could have a distinct impact on a patient’s functional recovery. And its early detection is crucial to give the appropriate treatment needed by the patient.

“Depression is a highly comorbid neuropsychiatric symptom during the acute and subacute phase after a stroke and has been reported to negatively influence functional and cognitive recovery. Thus, early diagnosis and intervention are crucial for post-stroke depression,” study author Seiji Hama, a research associate at HU’s Graduate School of Biomedical and Health Science, said.

“However, PSD is multifactorial, and associated neurological symptoms may hinder the detection process. This study is the first step in aiming to accurately diagnose PSD using data obtained in routine practice without any special equipment.”

The researchers tested the AI’s diagnostic accuracy through the receiver operating characteristic curve which visually evaluates the performance of a machine learning algorithm by giving it an area under the curve (AUC) score. An AUC score of 1.0 means a perfect performance. The PSD detection AI scored above 0.85.

Stress threshold hypothesis

Various post-stroke physical disorders, cognitive dysfunction, and mood disorders associated with stress responses are intricately intertwined, making it difficult to understand the cause of PSD and, therefore, making its diagnosis challenging.