our technology

Our Software as a Medical Device (SaMD) supports doctors to rapidly diagnose childhood sleep disorders & prioritize treatment for children who urgently need it.

Product Benefits

Simplifying and streamlining the diagnostic pathway for paediatric sleep disorders.

Conclusive Analysis

Our machine learning algorithms retain diagnostic power even in the event of signal loss and noise, therefore reducing the number of inconclusive sleep studies.

Comorbidity Adaption

Our software pipeline automatically adjusts to account for variations in data and diagnostics due to the presence of comorbidities, ensuring accurate and reliable results.

Minimising Human Error

By generating detailed, patient-specific clinical reports with objective insights and full data visualization, our software supports clinicians to make a diagnosis with reduced human error.

Competitive Advantage


Clinicians will receive an interpretability report, showing  them exactly how our diagnostic algorithms ‘think’.


Dramatically increases the number of sleep studies which can be analysed without a subsequent increase in manpower. Online learning means our algorithms always stay up to date with clinical practice.


Through custom software architecture, our algorithms can be rapidly re-trained to diagnose a range of health conditions. Not just sleep disorders.

Technology You Can Trust

We prioritise analysing and mitigating bias in our models. We use cutting-edge techniques to understand and improve our models, ensuring they are fair, transparent, and effective in real-world healthcare scenarios

You ask, we answer

At Seluna, we value open communication. Here are answers to some of the most frequently asked questions about our technology and its impact.

No, not at all. Our goal at Seluna is to support clinicians, not replace them. By building tools that can quickly filter out any redundant information, we can give clinicians a clear picture of the information that matters, allowing them to make a diagnosis faster.

Healthcare inequalities are a widespread issue. For instance, someone in central London likely has better access to healthcare than someone in rural Scotland, and a single woman without children has more time for GP visits than a mother of three. Machine learning models learn by example and are typically trained on real-world datasets. A model trained on this data might assume people in rural Scotland need fewer healthcare resources than those living in cities, masking the fundamental issue of limited access. This behaviour has severe consequences if not analysed, regulated, or monitored.

Artificial intelligence is excellent at learning how to solve complex problems extremely quickly. Artificial intelligence algorithms can learn from real-world data to match the diagnostic capabilities of experts in a fraction of the time, freeing up professionals to focus on other critical tasks.

This is a critical question that researchers are still answering today. At Seluna, we conduct extensive analysis to understand exactly how our algorithms behave. Identifying and correcting flaws early ensures our algorithms are fair, transparent, and trustworthy before they reach end-users.

Children are generally overlooked and underrepresented in the medical device industry due to a relatively small market size in comparison to adults. However, they have the longest to live and the most to gain from early diagnosis and treatment. By treating children early, we can reduce long-term strain on healthcare providers through the prevention of chronic health conditions. This makes children the prime target for preventative healthcare.

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