A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification

Frontiers in Cardiovascular Medicine

article

Markus Huellebrand¹  Matthias Ivantsits¹  Lennart Tautz²  Sebastian Kelle¹  Anja Hennemuth¹ 

[1] Institute of Cardiovascular Computer-Assisted Medicine, Charité—Universitätsmedizin Berlin;Cardiovascular Research and Development;German Centre for Cardiovascular Research;German Heart Center Berlin;Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf

关键词: visual analytics;    co-learning;    machine learning;    CMR;    human in the loop (HITL);    cardiovascular phenotyping;    artificial intelligence;    classification;   

DOI  :  10.3389/fcvm.2022.829512

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes.

【 授权许可】

CC BY