MathWorks MedTech Forum

Overview

In silico medicine presents a groundbreaking approach to medical device development by utilizing advanced computational models and simulations to create virtual human environments. This method addresses traditional challenges such as lengthy animal testing, high costs, and ethical issues by enabling comprehensive and controlled testing of a wider range of scenarios than physical experiments allow. By replacing physical testing with digital simulations, in silico medicine dramatically reduces development time, costs, and ethical concerns, allowing researchers to optimize device design, enhance safety, and speed up time-to-market. This technology is set to revolutionize the medical device development process, providing innovative solutions to patients more efficiently.

In this session, we will delve into the potential of in silico medicine through a real-world case study involving an in silico electrophysiology model used for pacemaker device validation, showcasing how these models comply with FDA guidelines and significantly accelerate time-to-market.

Highlights

• Utilize computational modeling and simulation to create human physiological models for device development and verification.
• Implement real-time closed-loop simulations with medical devices to enhance device validation.
• Develop and verify medical devices using Physiological Closed Loop Control (PCLC) through in silico methods.
• Expedite the time-to-market for medical devices while ensuring compliance with regulatory standards.
• Follow FDA guidelines on computational modeling and simulation (CM&S) to advance in silico medicine practices.

Overview

Medical images come from multiple sources such as MRI, CT, X-ray, ultrasound, and PET/SPECT. Engineers and scientists must visualize and analyze these multi-domain image datasets to extract clinically meaningful information.

In this session, we’ll explore tools and algorithms MATLAB provides for end-to-end medical image analysis and AI workflows – I/O, 3D visualization, segmentation, labeling and analysis of medical image data. Learn how to import, visualize, preprocess, register, segment, and label medical image data and train and use AI models on the data.

Highlights

In this session, you will learn through demonstrations how to:

• Access and visualize medical images in the Medical Image Labeler App
  • Leverage AI to Interactively segment tissues of interest
  • Explore segmenting with MONAI Label
• Leverage segmented datasets and trained AI models to:
  • Perform finite element analysis study of spine
  • Deploy real-time segmentation on streaming video using NVIDIA Clara hardware leveraging Holoscan

Overview

Medical Device software engineers are increasingly looking at the iterative and incremental approach of agile practices to speed up their software development while balancing regulatory goals such as requirements traceability and rigorous documentation.

In this session, we’ll cover the Polyspace V&V product suite which provides a unified solution for managing, measuring, and monitoring your software quality throughout the development lifecycle.  You’ll see how the Polyspace tool suite can be used to confirm the quality of embedded software for infusion pumps, implanted therapy systems, robotic platforms, and other life-critical medical devices and fulfill IEC 62304 credits, up to class III devices.

Highlights

• Author, manage, and execute test cases and measure code coverage on the host and hardware target.
• Automatically generate tests to augment manually created test cases and address incomplete code coverage and boundary value testing
• Exhaustively verify most critical C and C++ code statements
• Check coding rules, including MISRA C/C++ and CERT C/C++
• Centrally manage your V&V process and use static analysis to complement dynamic testing
• Link tests to software requirements for IEC 62304 certification

Overview

In this session, you’ll learn how to use MATLAB Coder to generate readable and portable C code from your MATLAB algorithms to integrate into other applications outside MATLAB.  You’ll also hear how to accelerate your MATLAB algorithms within MATLAB by generating MEX files. This session’s demo will include an introduction to algorithm deployment options with MATLAB Compiler/Compiler SDK and MATLAB coder products and highlights the benefits of automated code translation.

Highlights

• MATLAB Coder: generate readable and portable C code from MATLAB algorithms for integration into external applications.
• Accelerate algorithms: enhance MATLAB algorithms within MATLAB using MEX files.
• Algorithm deployment options: introduction to MATLAB Compiler/Compiler SDK and MATLAB Coder products.
• Automated code translation: benefits and demonstrations of translating MATLAB code to C/C++.
• Datatype specification
• Code generation for classes/objects
• Introduction to GPU and HDL Coder apps

Overview

In silico medicine is the use of computational models and simulations to develop medical devices and pharmaceuticals. These simulation models aim to replicate the anatomy, physiology, and biological processes of the human body. In this hands-on workshop, attendees will learn how to model functional physiological models such as human lungs. Participants will gain practical experience in simulating and analyzing these models, which serve as critical testbeds for medical device development and verification.

Highlights

• Develop and refine models of human physiological systems using MATLAB and Simulink
• Learn to parameterize models for simulating and testing under various conditions
• Closed loop medical device validation using the virtual human model
• Real-time device validation with the human model using Hardware-In-The-Loop (HIL) technique, bridging the gap between virtual models and physical devices.

Workshop attendees need to bring their laptop. Instructions to get temporary MathWorks tool licensing and workshop files will be provided by MathWorks a few days prior to the event.