Cardiac imaging: working towards fully-automated machine analysis & interpretation

Abstract

Introduction Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

An automated approach to fully self‐gated free‐running cardiac and respiratory motion‐resolved 5D whole‐heart MRI

“…The timepoint for which images are reconstructed in the cardiac cycle, their temporal resolution, and at which respiratory level they should be extracted can freely and retrospectively be defined by the operator to best answer the clinical question at hand. However, this ultimately and inevitably leads to a large amount of data or a vast parameter space that needs to be interrogated: The extraction of information that best answers the question of the operator may ultimately be guided by machine learning …”

“…However, this ultimately and inevitably leads to a large amount of data or a vast parameter space that needs to be interrogated: The extraction of information that best answers the question of the operator may ultimately be guided by machine learning. 40,41…”

Deep Learning in Cardiology

“…Moreover most of medical data belong to the normal cases instead to abnormal, making them highly unbalanced. Other challenges of applying deep learning in medicine that previous literature has identified are data standardization/availability/dimensionality/volume/quality issues, difficulty in acquiring the corresponding annotations and noise in annotations [239], [240], [242], [246]. More specifically, in [245] the authors note that deep learning applications on small vessel disease have been developed using only a few representative datasets and they need to be evaluated in large multi-center datasets.…”

“…In [250] the authors note that the black-box nature of AI methods like deep learning is against the concept of evidence-based medicine and it raises legal and ethical issues in using them in clinical practice. This lack of interpretability is the main reason that medical experts resist using these models and there are also legal restrictions regarding the medical use of the non-interpretable applications [246]. On the other hand, any model can be placed in a 'human-machine decision effort' axis [261] including statistical ones that medical experts rely on for everyday clinical decision making.…”

Cardiovascular risk assessment models: Have we found the perfect solution yet?

“…In addition, the role of professional societies guiding the methodology, acquisition, and interpretation of the cardiac imaging studies in a standardized fashion as well as use of machine learning/ artificial intelligence becomes an important factor in the overall improvement of the CVD outcomes prediction in modern clinical practice. 39…”