Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review

Quantification of Replacement Fibrosis in Aortic Stenosis: A Narrative Review on the Utility of Cardiovascular Magnetic Resonance Imaging

Aortic stenosis (AS) is associated with the development of replacement myocardial fibrosis/scar. Given the dose-dependent relationship between scar and clinical outcomes after aortic valve replacement (AVR) surgery, scar quantity may serve as an important risk-stratification tool to aid decision-making on the optimal timing of AVR. Scar is non-invasively assessed and quantified by cardiovascular magnetic resonance (CMR) imaging. Several quantification techniques exist, and consensus on the optimal technique is lacking. These techniques range from a visual manual method to fully automated ones. This review describes the different scar quantification techniques used and highlights their strengths and shortfalls within the context of AS. The two most commonly used techniques in AS include the semi-automated signal threshold versus reference mean (STRM) and full-width half-maximum (FWHM) techniques. The accuracy and reproducibility of these techniques may be hindered in AS by the coexistence of diffuse interstitial fibrosis and the presence of relatively small, non-bright scars. The validation of these techniques against histology, which is the current gold standard for scar quantification in AS, is limited. Based on the best current evidence, the STRM method using a threshold of three standard deviations above the mean signal intensity of remote myocardium is recommended. The high reproducibility of the FWHM technique in non-AS cohorts has been shown and merits further evaluation within the context of AS. Future directions include the use of quantitative T1 mapping for the detection and quantification of scar, as well as the development of serum biomarkers that reflect the fibrotic status of the myocardium in AS.

Importance of Cardiovascular Magnetic Resonance Applied to Congenital Heart Diseases in Pediatric Age: A Narrative Review

Congenital heart diseases (CHDs) represent a heterogeneous group of congenital defects, with high prevalence worldwide. Non-invasive imaging is essential to guide medical and surgical planning, to follow the patient over time in the evolution of the disease, and to reveal potential complications of the chosen treatment. The application of cardiac magnetic resonance imaging (CMRI) in this population allows for obtaining detailed information on the defects without the necessity of ionizing radiations. This review emphasizes the central role of CMR in the overall assessment of CHDs, considering also the limitations and challenges of this imaging technique. CMR, with the application of two-dimensional (2D) and tri-dimensional (3D) steady-state free precession (SSFP), permits the obtaining of very detailed and accurate images about the cardiac anatomy, global function, and volumes' chambers, giving essential information in the intervention planning and optimal awareness of the postoperative anatomy. Nevertheless, CMR supplies tissue characterization, identifying the presence of fat, fibrosis, or oedema in the myocardial tissue. Using a contrast agent for angiography sequences or 2D/four-dimensional (4D) flows offers information about the vascular, valvular blood flow, and, in general, the cardiovascular system hemodynamics. Furthermore, 3D SSFP CMR acquisitions allow the identification of coronary artery abnormalities as an alternative to invasive angiography and cardiovascular computed tomography (CCT). However, CMR requires expertise in CHDs, and it can be contraindicated in patients with non-conditional devices. Furthermore, its relatively longer acquisition time and the necessity of breath-holding may limit its use, particularly in children under eight years old, sometimes requiring anesthesia. The purpose of this review is to elucidate the application of CMR during the pediatric age.

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation

BACKGROUND

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

METHODS

Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis.

RESULTS

These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives.

CONCLUSIONS

Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Deep learning for classification of late gadolinium enhancement lesions based on the 16-segment left ventricular model

PURPOSE

This study aimed to develop and validate a deep learning-based method that allows for segmental analysis of myocardial late gadolinium enhancement (LGE) lesions.

METHODS

Cardiac LGE data from 170 patients with coronary artery disease and non-ischemic heart disease were used for training, validation, and testing. Short-axis images were transformed to polar space after identification of the left ventricular (LV) center point and anterior right ventricular (RV) insertion point. Images were obtained after dividing the polar transformed images into segments based on the 16-segment LV model. Five different deep convolutional neural network (CNN) models were developed and validated using the labeled data, where the image after the division corresponded to a segment, and the lesion labeling was based on the 16-segment LV model. Unseen testing data were used to evaluate the performance of the lesion classification.

RESULTS

Without manual lesion segmentation and annotation, the proposed method showed an area under the curve (AUC) of 0.875, and a precision, recall, and F1-score of 0.723, 0.783, and 0.752, respectively for the lesion class when the pretrained ResNet50 model was tested for all slice images. The two pretrained models of ResNet50 and EfficientNet-B0 outperformed the three non-pretrained CNN models in terms of AUCs (0.873-0.875 vs. 0.834-0.841).

CONCLUSION

The proposed method is based on learning a deep CNN model from polar transformed images to predict LGE lesions with good accuracy and does not require time-consuming annotation procedures such as lesion segmentation.

The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol

INTRODUCTION

Research utilising artificial intelligence (AI) and cardiovascular magnetic resonance (CMR) is rapidly evolving with various objectives, however AI model development, generalisation and performance may be hindered by availability of robust training datasets including contrast enhanced images.

METHODS

NotIs CMR is a large UK, prospective, multicentre, observational cohort study to guide the development of a biventricular AI scar model. Patients with ischaemic heart disease undergoing clinically indicated contrast-enhanced cardiac magnetic resonance imaging will be recruited at Nottingham University Hospitals NHS Trust and Mid-Yorkshire Hospital NHS Trust. Baseline assessment will include cardiac magnetic resonance imaging, demographic data, medical history, electrocardiographic and serum biomarkers. Participants will undergo monitoring for a minimum of 5 years to document any major cardiovascular adverse events. The main objectives include (1) AI training, validation and testing to improve the performance, applicability and adaptability of an AI biventricular scar segmentation model being developed by the authors and (2) develop a curated, disease-specific imaging database to support future research and collaborations and, (3) to explore associations in clinical outcome for future risk prediction modelling studies.

CONCLUSION

NotIs CMR will collect and curate disease-specific, paired imaging and clinical datasets to develop an AI biventricular scar model whilst providing a database to support future research and collaboration in Artificial Intelligence and ischaemic heart disease.

What Matters in Radiological Image Segmentation? Effect of Segmentation Errors on the Diagnostic Related Features

Segmentation is a crucial step in extracting the medical image features for clinical diagnosis. Though multiple metrics have been proposed to evaluate the segmentation performance, there is no clear study on how or to what extent the segmentation errors will affect the diagnostic related features used in clinical practice. Therefore, we proposed a segmentation robustness plot (SRP) to build the link between segmentation errors and clinical acceptance, where relative area under the curve (R-AUC) was designed to help clinicians to identify the robust diagnostic related image features. In experiments, we first selected representative radiological series from time series (cardiac first-pass perfusion) and spatial series (T2 weighted images on brain tumors) of magnetic resonance images, respectively. Then, dice similarity coefficient (DSC) and Hausdorff distance (HD), as the widely used evaluation metrics, were used to systematically control the degree of the segmentation errors. Finally, the differences between diagnostic related image features extracted from the ground truth and the derived segmentation were analyzed, using the statistical method large sample size T-test to calculate the corresponding p values. The results are denoted in the SRP, where the x-axis indicates the segmentation performance using the aforementioned evaluation metric, and the y-axis shows the severity of the corresponding feature changes, which are expressed in either the p values for a single case or the proportion of patients without significant change. The experimental results in SRP show that when DSC is above 0.95 and HD is below 3 mm, the segmentation errors will not change the features significantly in most cases. However, when segmentation gets worse, additional metrics are required for further analysis. In this way, the proposed SRP indicates the impact of the segmentation errors on the severity of the corresponding feature changes. By using SRP, one could easily define the acceptable segmentation errors in a challenge. Additionally, the R-AUC calculated from SRP provides an objective reference to help the selection of reliable features in image analysis.