RESERACH AREA
AI in Medicine, Modeling & Simulation
Introduction
The AI in Medicine, Modeling & Simulation research area is the computational engine of INVITROVO, dedicated to transforming raw biological and clinical data into predictive knowledge. This discipline powers advanced machine learning, artificial intelligence (AI), and sophisticated computational modeling to simulate complex biological phenomena, predict disease progression, and personalize therapeutic strategies. By creating in silico representations of human biology and patient cohorts, we aim to accelerate the entire translational pipeline of precision medicine, from basic discovery and drug development to clinical decision support systems.
Research Spectrum
Our research encompasses the full lifecycle of data analysis and simulation, providing deep insight into complex biomedical problems. The spectrum of our research includes:
- AI for Precision Theranostics: Developing deep learning algorithms for automated analysis of medical images (e.g., MRI, CT, ultrasound) and pathology slides, enabling ultra-fast, highly accurate, and objective diagnostic/therapeutics support.
- Physiologically Based Modeling: Building computational models of organs, systems, and whole-body physiology to accurately predict the absorption, distribution, metabolism, and excretion (ADME) of novel drug candidates using powerful software (e.g., MATLAB, COMSOL).
- Digital Twin Technology: Creating personalized, dynamic computational “twins” of individual patients. These models integrate genomic, lifestyle, and clinical data to predict responses to various treatments and optimize intervention timing.
- Biomechanical and Systems Simulation: Using finite element analysis (FEA) and computational fluid dynamics (CFD) to model mechanical stresses in implants, blood flow dynamics, and the physical interaction between devices and biological tissues using powerful software (e.g., ANSYS, COMSOL).
- Predictive Clinical Analytics: Employing machine learning models to analyze vast electronic health record (EHR) data, identifying hidden risk factors, predicting hospital readmissions, and optimizing resource allocation.
Core Objectives
Our core objectives are focused on deploying computational power to deliver smarter, safer, and faster medical solutions. Our primary goals are:
- To Accelerate Drug and Target Discovery: To utilize AI to rapidly screen millions of compounds and identify novel therapeutic targets, drastically reducing the time and cost of pharmaceutical R&D.
- To Improve Clinical Decision Support: To deploy AI tools that provide clinicians with evidence-based predictions and risk assessments, leading to more informed and timely therapeutic choices.
- To Validate in silico Testing: To establish computational models and simulation platforms as validated alternatives to traditional animal and human trials, improving ethical standards and efficiency.
- To Translate Data into Personalization: To ensure every model we build is actionable at the individual patient level, supporting the future of truly personalized, preventative, and predictive medicine so called “Precision Medicine”.
