inHEART Models

“inHEART MODELS is a suite of medical image processing software tools that enables 3D visualization and analysis of anatomical structures. This software suite is composed of three software as a medical device components:”

“This software module uses a machine-learning based approach with the following characteristics: Training dataset: 796 cases (3D CT original images and the segmentation masks) from previously manually performed segmentations (time period 2018-2022); origins of the data are public and private clinical and hospital institutions located in US (40%) and Europe (60%). Training process: Machine learning algorithm (UNet) is trained applying data augmentation (including patch mirroring) and regularization (including Instance Normalization, Dropout, Data Augmentation, Weight Initialization, Leaky ReLU Activation) methods. Loss functions (soft dice loss and binary cross entropy) and optimization methods (stochastic gradient descent) are used during learning over a fixed number of 1000 epochs, each consisting of 250 iterations with a batch size of 2.”

“Machine learning algorithm (UNet) is trained applying data augmentation (including patch mirroring) and regularization (including Instance Normalization, Dropout, Data Augmentation, Weight Initialization, Leaky ReLU Activation) methods. Loss functions (soft dice loss and binary cross entropy) and optimization methods (stochastic gradient descent) are used during learning over a fixed number of 1000 epochs, each consisting of 250 iterations with a batch size of 2.”

“IEC 81001-5-1 Edition 1.0 2021-12, Health software and health IT systems safety, effectiveness and security – Part 5-1 Security – Activities in the product life cycle”