Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation

[Artificial intelligence and radiomics : Value in cardiac MRI]

CLINICAL/METHODICAL ISSUE

Cardiac diseases are the leading cause of death. Many diseases can be specifically treated once a valid diagnosis is established. Cardiac magnetic resonance imaging (MRI) plays a central role in the workup of many cardiac pathologies. However, image acquisition as well as interpretation and related secondary image evaluation are time-consuming and complex.

STANDARD RADIOLOGICAL METHODS

Cardiac MRI is becoming increasingly established in international guidelines for the evaluation of cardiac function and differential diagnosis of a wide variety of cardiac diseases.

METHODOLOGICAL INNOVATIONS

Cardiac MRI has limited reproducibility due to the acquisition technique and interpretation of findings with complex secondary measurements. Artificial intelligence techniques and radiomics offer the potential to improve the acquisition, interpretation, and reproducibility of cardiac MRI.

PERFORMANCE

Research suggests that artificial intelligence and radiomic analysis can improve cardiac MRI in terms of image acquisition and also diagnostic and prognostic value. Furthermore, the implementation of artificial intelligence and radiomics may result in the identification of new biomarkers.

ACHIEVEMENTS AND PRACTICAL RECOMMENDATIONS

The implementation of artificial intelligence in cardiac MRI has great potential. However, the current level of evidence is still limited in some aspects; in particular there are too few prospective and large multicenter studies available. As a result, the algorithms developed are often not sufficiently validated scientifically and are not yet applied in clinical routine.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports

Background

Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports.

Methods

An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports).

Results

NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99.

Conclusions

NLP identified and classified HCM from CMR narrative text reports with very high performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02017-y.