Functional lung reconstruction
Reconstruct organs with patient-specific pathophysiology from multimodal clinical and radiological data.
Virtual Lung for surgery and critical care
Inaccurate management of lung function drives complications and unreimbursed healthcare costs. Medlea builds a patient-specific virtual lung that models lung function from clinical and radiological data — evidence-based insight for surgery and critical-care research.
Unreimbursed cost
Complications generate care that is not reimbursed.
Ventilator-induced injury
Ventilation mismatched to the patient can injure the lung.
Rehospitalization
Patients return to hospital after discharge.
Functional lung reconstruction
Reconstruct organs with patient-specific pathophysiology from multimodal clinical and radiological data.
Adaptive ventilation
Patient-specific ventilation strategy support for the ICU.
Predictive analysis
Risk maps and AI-derived biomarkers for surgery, ICU and prognostic research.
Pharmacological simulations
In-silico evaluation of drug response on lung structure and function.
Pre-operative evaluation
Plan thoracic interventions on a patient-specific digital twin before entering the OR.
Adaptive ventilation
Patient-specific ventilation strategy support for the ICU.
Predictive analysis
Risk maps and AI-derived biomarkers for surgery, ICU and prognostic research.
Pharmacological simulations
In-silico evaluation of drug response on lung structure and function.
Pre-operative evaluation
Plan thoracic interventions on a patient-specific digital twin before entering the OR.
The agentic workflow
- ● Multimodal clinical and radiological data are automatically processed
- ● Cardiorespiratory organs are reconstructed with patient-specific pathophysiology
- ● Morphofunctional biomarkers are derived and analyzed longitudinally
One platform, the pathology you choose
Imaging AI usually arrives with its diseases already chosen. Ours works the other way round: your department names the pattern, annotates its own cases, and the model is trained inside your perimeter.
How the platform works →- ● The pattern is described by clinicians, not fixed by us.
- ● Annotation, training and inference stay on the hospital's own GPU.
- ● The output is measurements, never a disease label.
They are working with us
