Innovative solution that uses artificial intelligence, including deep learning, to diagnose respiratory diseases, predict mortality risk and assess the probability of ICU admission based on CT scans and, optionally, other clinical data, enabling the application of precision medicine in pulmonology.
Early diagnosis and evaluation of respiratory diseases enables healthcare institutions to provide accurate and personalized care to patients, leading to better outcomes.
The "exceptions to the rule," people who show no clear signs of respiratory problems despite having them, and those with clinically significant severity require specialized care that may not be evident by conventional methods.
Analysis of lung CT scans can significantly increase the workload of radiologists and is often a bottleneck in clinical care.
Deep Horus addresses these challenges by employing Machine Learning-based predictive models to enable real-time automatic respiratory disease diagnosis and risk prediction based on CT scans and, optionally, other patient data.
Experience the future of respiratory disease diagnosis and risk assessment. Improve patient care with Deep Horus.
Machine learning diagnostics: employ state-of-the-art machine learning models, including deep learning, to accurately diagnose various respiratory diseases.
Mortality risk prediction: To assess the mortality risk of patients with respiratory conditions, enabling proactive intervention and personalized care planning.
Probability of admission to the ICU: predicts the probability of admission to the ICU of patients with respiratory diseases, allowing hospitals to allocate resources efficiently.
Efficiency and workload management: automate CT scan analysis and reduce the burden on radiologists and clinical staff, optimizing resource utilization.
