Noologica is an Open Science biotech start-up developing a digital solution for the precise screening, triaging, diagnosis and monitoring of neurodivergent and mental health conditions. We are also registered as an independent research provider under the Health Research Council of New Zealand with our tools used locally and internationally for clinical studies.
Human behaviour and mental activities are controlled by goal-directed and anticipatory capabilities, currently called Executive Functions (EF). EF disorders are the most common features associated with mental health conditions in children. Present classifications of EF disorders (e.g. ADHD) have poor levels of accuracy. No measures or precise sets of observable characteristics are available to date, making it difficult and time consuming for clinicians to diagnose these conditions.
Addressing Sector Challenges
We are developing a low-cost digital solution that has the potential to minimise several hours of professional time needed to diagnose an individual. Our current test time is 30-45 minutes.
Our preliminary research is showing very high levels of sensitivity and specificity with >80% accuracy.
Our test uses objective measures and aims to distinguish between different neurodivergent and mental health conditions.
We have developed a new clinical method that combines mathematics and predictive modelling to describe and analyse the EF patterns from thought processes. These measures can be collected from an inference task, using a customised and digital version of the board-game 'Battleships'. We discovered that children with ADHD have characteristic
We have developed a new clinical method that combines mathematics and predictive modelling to describe and analyse the EF patterns from thought processes. These measures can be collected from an inference task, using a customised and digital version of the board-game 'Battleships'. We discovered that children with ADHD have characteristic patterns of EF, like a signature of the disorder. Noologica technology is based on this discovery and the hypothesis that mental health conditions in children display distinctive EF signatures.
This robust, low-cost, and efficient technology makes it a promising system to be used in digital screening, triaging, diagnosing and monitoring of mental health conditions.
We have undertaken a prospective diagnostic accuracy study, phases I and II with cohorts of ADHD children and typically developing controls (5 to 13-years-old). Participants were recruited from the primary paediatric unit within the Nelson Marlborough Health .
From the inference task, spatiotemporal measures were taken and used in a predic
We have undertaken a prospective diagnostic accuracy study, phases I and II with cohorts of ADHD children and typically developing controls (5 to 13-years-old). Participants were recruited from the primary paediatric unit within the Nelson Marlborough Health .
From the inference task, spatiotemporal measures were taken and used in a predictive model. Results yielded a very high classification accuracy, sensitivity and specificity in the independent testing/validating cohort.
We are currently undertaking a second clinical trial that is an extension of the first phase I & II trial. The participants for this updated study will be recruited from the Nelson Paediatric Research Unit. See Clinical Trials for more information.
We aim to improve the clinical processes of screening, diagnosis and
monitoring of neurodivergent and mental health conditions through a
low-cost solution to lower accessibility barriers and promote equitable health outcomes.
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