A Study of Detection of Paroxysmal Events Utilizing Computer Vision and Machine Learning (USF)

Brief summary (registry, in English)

Increased computational power has made it possible to implement complex image recognition tasks and machine learning to be implemented in every day usage. The computer vision and machine learning based solution used in this project (Nelli) is an automatic seizure detection and reporting method that has a CE mark for this specific use. The present study will provide data to expand the utility and detection capability of NELLI and enhance the accuracy and clinical utility of automated computer vision and machine learning based seizure detection.

What you commit to

• Study type: Observational
• Participants enrolled: 33
• From 15 Nov 2024 to the primary completion date, 31 Dec 2025
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 18 years to 99 years · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

Sites in registry order. Pick your city on the disease page to sort them by distance.