The future of Thoracic Surgery with Medlea
Personalized Preoperative Simulation
Our Digital Twin enables thoracic surgeons to explore, analyze, and plan procedures in detail for each patient before entering the operating room. Through high-resolution CT-based 3D reconstructions enhanced by AI-driven functional models, the surgeon can accurately assess nodules, peripheral airways, ventilation distribution, and vascular structures. This preoperative simulation supports more targeted clinical decisions and significantly reduces surgical risks.
Decision Support and Operative Strategy
Medlea integrates predictive models that estimate the functional impact of procedures—such as lobar or segmental resections—on residual lung capacity. The system presents results through intuitive dashboards and performance curves (ROC, AUC) for digital biomarkers like emphysema, fibrosis, pleural effusion, and ventilation-perfusion mismatch. This advanced decision support improves surgical planning and assists in selecting suitable candidates for minimally invasive or robotic procedures.
Integration into the Multidisciplinary Clinical Pathway
The Medlea platform is designed to seamlessly integrate into the multidisciplinary workflow, involving surgeons, pulmonologists, anesthesiologists, and OR nurses. Clinical cases can be shared via cloud or on-premise servers, enabling collaborative planning and case discussions. This digital approach enhances coordination, shortens surgical preparation time, and supports the implementation of ERAS (Enhanced Recovery After Surgery) protocols.
Mechanical Ventilation Optimization in the ICU
Patient-Specific Lung Modeling
In critical care settings, Medlea enables real-time, patient-specific modeling of lung structure and function using high-resolution CT scans. The system reconstructs detailed ventilation maps that highlight regional disparities, such as overdistension, hypoventilation, and recruitable lung tissue. This level of precision supports clinicians in assessing the underlying lung mechanics of each patient, moving beyond generalized ventilation protocols.
AI-Driven Ventilation Strategy Support
Medlea’s proprietary fractal AI engine simulates different ventilatory settings and their expected impact on gas exchange, lung stress, and compliance. Clinicians can use these simulations to compare strategies for PEEP titration, tidal volume adjustments, and prone positioning. By visualizing the outcomes, intensivists are empowered to adopt more protective and individualized ventilation strategies, minimizing the risk of ventilator-induced lung injury (VILI).
Clinical Decision-Making and Weaning Optimization
The platform also supports daily ICU decision-making by tracking ventilatory efficiency and predicting patient response to weaning attempts. Dynamic indicators derived from the Digital Twin provide early warning signs of decompensation or readiness for extubation. This facilitates a safer, more data-driven weaning process and contributes to reduced ICU stays and improved recovery outcomes.
Predictive Lung Diagnosis with Medlea
AI-Based Biomarker Detection
Medlea leverages advanced AI to detect and quantify digital biomarkers from thoracic CT scans, enabling early identification of conditions such as emphysema, fibrosis, pulmonary nodules, pleural effusions, and post-COVID lesions. These biomarkers are extracted through fractal geometry analysis and machine learning models trained on diverse clinical datasets. The result is a highly sensitive, reproducible tool that can support radiologists and pulmonologists in identifying abnormalities before they become clinically evident.
Risk Stratification and Disease Progression Modeling
Beyond detection, Medlea provides predictive analytics to stratify patient risk and model disease progression over time. By integrating clinical parameters and imaging-derived features, the system forecasts potential deterioration or stability of pulmonary function. This empowers clinicians to tailor monitoring plans and therapeutic interventions to the individual trajectory of each patient, supporting both early intervention and long-term care strategies.
Screening and Preventive Care in High-Risk Populations
Medlea can also be deployed in screening programs targeting high-risk populations, such as smokers or patients with occupational lung exposure. The platform identifies subtle early signs of pathology that may go unnoticed in standard evaluations, contributing to timely diagnosis and preventive care. This use case is especially valuable in outpatient settings and population health initiatives aiming to reduce the burden of chronic respiratory disease.
Supporting Pharmaceutical Development for Lung Therapies
In-Silico Evaluation of Drug Response
Medlea’s Digital Twin offers a powerful platform for simulating and visualizing the effect of pharmaceutical compounds on lung structure and function. By integrating imaging data with AI-driven biophysical models, the system allows researchers to estimate changes in ventilation, tissue integrity, and perfusion following treatment. This non-invasive, in-silico approach accelerates early-stage evaluation of drug efficacy, reducing dependency on animal models or broad patient sampling in the initial phases.
Patient Stratification for Clinical Trials
In clinical research, Medlea supports smarter patient selection and stratification by identifying individuals who exhibit specific imaging biomarkers relevant to the mechanism of action of a drug. For example, in trials targeting fibrosis or emphysema, the platform can classify patients by severity, anatomical distribution, and progression risk. This targeted approach enhances trial design, increases statistical power, and reduces heterogeneity, ultimately contributing to more successful outcomes.
Quantitative Biomarkers for Efficacy Monitoring
Throughout the course of a clinical trial, Medlea provides consistent and quantitative monitoring of treatment response by tracking dynamic imaging biomarkers over time. Automated analysis of CT scans allows for objective comparison between baseline and follow-up, offering high-resolution insights into how the lungs are responding at both global and regional levels. These digital endpoints can complement spirometry and traditional clinical measures, enriching the dataset available to pharmaceutical partners and regulators.
