The startup develops medical software that utilizes diagnostic algorithms to enable healthcare providers to quickly identify pediatric sleep disorders. This platform enhances data analysis efficiency, allowing doctors to prioritize treatment for children in urgent need of care.
Funding
$400K 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
Diagnosing pediatric sleep disorders is complex, time-consuming, and requires specialized expertise, leading to delayed diagnoses and treatment for many children. Traditional sleep studies generate large volumes of data that require manual analysis, creating bottlenecks and straining clinical resources. A significant percentage of children with sleep disorders remain undiagnosed, increasing their risk of long-term health complications.
Solution
Seluna is developing a Software as a Medical Device (SaMD) that leverages machine-learning algorithms to streamline the diagnosis of pediatric sleep disorders. The platform analyzes data from hospital sleep studies, providing clinicians with objective insights and detailed reports to increase diagnostic accuracy and efficiency. Seluna's technology adapts to variations in data caused by comorbidities, ensuring reliable results across diverse patient populations. By automating data analysis and providing clinical decision support, Seluna aims to reduce departmental strain, shorten waiting lists, and improve patient care. The software generates interpretability reports, showing clinicians how the diagnostic algorithms arrive at their conclusions, fostering trust and transparency.
Target Audience
The primary target audience includes clinicians specializing in pediatric sleep medicine, hospitals, and healthcare systems seeking to improve the efficiency and accuracy of sleep disorder diagnoses in children.
Features
- Machine-learning algorithms analyze data from hospital sleep studies.
- Adaptable to variations in data and diagnostics due to comorbidities.
- Generates detailed, patient-specific clinical reports with data visualization.
- Provides objective insights to support clinicians in making diagnoses.
- Offers an interpretability report, showing how the diagnostic algorithms work.
- Designed with custom software architecture, allowing algorithms to be rapidly re-trained to diagnose a range of health conditions.
- Online learning ensures algorithms stay up to date with clinical practice.