Bitsphi develops EEG-based diagnostic solutions that analyze brain connectivity patterns to objectively assess cognitive function, specifically targeting ADHD. By utilizing machine learning to identify normative and impaired connectivity, the company aims to provide a reliable alternative to subjective diagnostic criteria in neurodiagnosis.
Funding
$3.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current methods for diagnosing cognitive disorders like ADHD rely heavily on subjective assessments, leading to potential inaccuracies and inconsistencies in diagnosis. This subjectivity can result in delayed or incorrect diagnoses, impacting treatment plans and patient outcomes.
Solution
Bitsphi is developing EEG-based solutions that leverage brain connectivity patterns and machine learning to provide objective assessments of cognitive function. By analyzing EEG data, Bitsphi aims to identify normative and impaired brain connectivity patterns associated with cognitive deficiencies. This technology offers clinicians a tool to support more reliable and data-driven diagnoses, reducing the reliance on subjective criteria. The initial clinical application focuses on ADHD, with plans to expand the model to other mental disorders.
Target Audience
The primary target audience includes psychiatrists, psychologists, and other clinicians involved in the diagnosis and treatment of cognitive disorders such as ADHD.
Features
- EEG-based analysis of brain connectivity patterns
- Machine learning algorithms to identify normative and impaired connectivity
- Objective assessment of cognitive function, reducing reliance on subjective criteria
- Initial application focused on ADHD diagnosis
- Potential for expansion to other mental disorders