Research Technology for Epilepsy

Can AI hold the answer to better epilepsy treatment?

doc.ai, a digital health medical research company, announced the opening of an Epilepsy Digital Health Trial focusing on how Artificial Intelligence (A.I.) can help identify the right treatment, for the right patient, at the right time.

Epilepsy is a neurological disorder that affects 65 million people globally. It is characterized by unprovoked and recurrent seizures. A seizure is an electro chemical storm in the brain. Depending on what parts of the brain are involved and other factors, seizures can produce different effects. For example, small seizures may produce sensations; larger ones can bring confusion, loss of awareness and memory; and even larger  seizures will cause falling and shaking.

Doctors are limited in how they decide which of the more than 25 Epilepsy medications to give their patients. They make their best educated guess. There are thousands of possible combinations of drugs, the potential outcome for patients is to suffer from adverse side effects, without realizing the benefits of a drug that works for them.

Doc.ai, a Palo Alto based digital healthcare company, has announced it will work with the Stanford University School of Medicine and the Stanford Epilepsy Center to improve the precision of treatment options for Epilepsy. And more specifically to test how AI can help create predictive and accurate models for treating or curing diseases.

Patients will track their seizure episodes and other diverse sets of data using the doc.ai mobile app. Doc.ai’s artificial intelligence platform then analyzes the data and helps identify models to potentially improve treatment.

Enrollment onto the trial

The at-home, HIPAA-compliant and IRB-approved Digital Health Trial seeks to enroll up to 1,000 eligible participants between September 2019 and September 2020.

Once enrolled via the doc.ai app, participants will keep an online diary tracking their seizure episodes and side effects of medication for three months.

Along with the self-reported data, the study will collect and analyze patients’ health data, including an array of personal-omics information, such as the genome (DNA test provided by Kailos), phenome to capture physical traits, physiome for exercise and activity tracking, pharmacome or medication tracking and finally, the exposome, which includes environmental exposures.

The trial also leverages doc.ai A.I. technologies such as natural language processing for participants to capture a photograph of their medication bottles for automatic import.


source- inc.com

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