Deans' stroke musings: Can AI be utilized to identify brain damage following a stroke?

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.

Sunday, March 31, 2024

Can AI be utilized to identify brain damage following a stroke?

Once you identify the specific area where the damage is; WHAT IS THE EXACT REHAB PROTOCOL TO BE USED TO RECOVER FROM THAT DAMAGE? Totally incomplete research!

Can AI be utilized to identify brain damage following a stroke?

The study examined the potential of artificial intelligence (AI), specifically the large language model GPT-4, to help locate brain lesions resulting from strokes. These lesions play a crucial role in predicting the long-term effects of a stroke and determining the appropriate treatment and prognosis for affected individuals. The research, conducted by Dr. Jung-Hyun Lee from SUNY Downstate Health Sciences University, utilized text from health histories and neurologic exams of 46 stroke patients to train GPT-4 in accurately identifying the location and extent of lesions in the brain.

The findings of the study revealed that GPT-4 was successful in locating lesions in the brains of many participants, determining the side of the brain affected as well as the specific brain region, with the exception of lesions in the cerebellum and spinal cord. The AI model demonstrated a sensitivity of 74% and a specificity of 87% in identifying the side of the brain with lesions, and a sensitivity of 85% and a specificity of 94% in pinpointing the brain region involved. Additionally, GPT-4 showed consistency in its results for the number of brain lesions, side of the brain, and brain regions in a majority of cases.

Although GPT-4 was able to provide accurate answers for 41% of participants when combining responses to all three questions across all three times, the study notes that further refinement and validation are needed before its clinical use. A key limitation of the study is that the accuracy of GPT-4 relies on the quality of information it receives, and detailed health histories and neurologic exam information may not always be available for all stroke patients. However, the potential of AI models like GPT-4 to assist in locating brain lesions after a stroke is seen as promising, particularly in underserved regions where access to neurologic care is limited.

The study highlights the importance of accurate identification of brain lesions following a stroke, as this information can significantly impact the long-term outcomes and treatment strategies for affected individuals. By leveraging the capabilities of AI models like GPT-4 to analyze health histories and neurologic exam data, neurologists may be able to streamline the diagnostic process and improve the efficiency of lesion localization. This advancement has the potential to reduce disparities in healthcare access and delivery, especially in regions where neurologic care is scarce.

Moving forward, further research and validation are needed to enhance the accuracy and reliability of AI models like GPT-4 in locating brain lesions after a stroke. As technology continues to evolve, the integration of AI in neurology practice holds promise for improving patient outcomes and enhancing the accessibility and quality of healthcare services worldwide. Continued collaboration between neurologists, researchers, and AI experts will be crucial in harnessing the full potential of artificial intelligence in advancing neurologic care and addressing the global health challenges posed by strokes and other neurological conditions.