Horus ML is a company dedicated to the creation of personalized products based on artificial intelligence and machine learning in the healthcare sector, focusing on projects with a high component of R+D+i in the clinical-care field.
We develop solutions that help clinicians through automatic clinical image analysis, early diagnosis, personalized precision medicine and remote monitoring of chronic patients.
A team with an excellent level of professionalism that understands and quickly adapts to the needs of the clinicians.
We are delighted to have a company like ML Horus to innovate and generate greater precision in diagnostic processes.
Horus ML's knowledge in the field of AI is deep and multidisciplinary, and the models they use are state-of-the-art.
The dedication with which they ensure that every detail is explained in a clear and understandable manner has allowed us to integrate their AI models effectively and with confidence.
Improve patient care with Deep Horus, a revolutionary solution that uses machine learning, including deep learning, techniques to diagnose respiratory disease, predict mortality risk and assess the likelihood of ICU admission based on CT scans and, optionally, other patient data. Provide healthcare institutions with accurate and personalized information to improve patient outcomes.
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Horus Echonet is a product based on Deep Learning techniques that uses a three-dimensional convolutional neural network architecture model for video classification. This model is applied to echocardiography to semantically segment the left ventricle and to assess cardiac function by calculating the left ventricular ejection fraction or LVEF.
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Monitor and improve hydration levels with Horus Hydro, a cutting-edge solution that leverages wearable sensors and machine learning technology. Stay hydrated and protect your health with our customized hydration tracking system.
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Optimize patient management and resource allocation in mental health services with Mental Health Triage. Our innovative tool, powered by Machine Learning, classifies patients according to their characteristics, severity and clinical needs, providing an efficient and effective approach to prioritizing mental health care and providing patients with personalized mental services based on their needs.
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