An artificial intelligence-based landmark model for early detection of aortic stenosis in Cardiac MRI

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Abstract Background The utilisation of ratiometric intensity measures from cardiac magnetic resonance imaging (CMR) offers a promising biomarker for the diagnosis of aortic stenosis (AS)[1]. The Ao:LV ratio, an approximation of AS severity, is determined by comparing the relative blood signal intensity in the aorta and left ventricle (LV) within three-chamber (3Ch) cine sequences[1]. In patients with AS, the blood signal intensity of aortic blood above the valve is diminished compared to that within the LV in steady-state free precession (SSFP) 3Ch CMR cine. Although the Ao:LV ratio serves as a reliable indicator of AS severity, manual computation of this ratio may encounter difficulties due to visual obstructions caused by small LV cavities or prominent papillary muscles. Purpose This study aims to leverage artificial intelligence (AI) to automate the Ao:LV ratio analysis from 3Ch SSFP cine images and introduce the Ao:LA (aorta to left atrium) ratio, thus enhancing the diagnostic accuracy for AS severity. Methods Our methodology extends the ratiometric analysis to include both Ao:LV and Ao:LA ratios in three main steps: first, developing an AI-based algorithm for automated detection and tracking of cardiac landmarks; second, using these landmarks to automatically compute regions of interest (ROIs) in the cardiac chambers; and third, training and validating classification models with 5-fold cross-validation to select the most effective for AS severity classification. The final model's performance is assessed on a holdout test set. Data for model training and evaluation were sourced from CMR scans across three hospitals and two scanner types. Results We analysed 220 patients of which 78 patients had no AS and 122 had AS (mild AS, n = 29; moderate AS; n = 35, severe AS; n = 78). Our model yielded high efficacy in classifying AS, particularly in differentiating any grade of AS from a normal classification, which is clinically relevant. The Ao:LA ratio demonstrated a stronger correlation with AS severity class than Ao:LV, suggesting a more reliable biomarker. The proposed model demonstrated robust performance in AS classification, achieving an Area Under the Curve (AUC) of 0.845 in the automated binary classification of AS at any grade. The model exhibited a precision of 0.889, recall of 0.865, and an F1 score of 0.877 on a holdout set. Conclusion By the integration of AI in automating the analysis of CMR images through a novel ratiometric approach that includes Ao:LV and Ao:LA ratios, our model significantly improves the accuracy of early AS detection. This advancement enhances imaging strategies for AS evaluation, promoting more efficient and effective use of cardiac MRI sequences as a diagnostic tool for aortic stenosis. It has the potential to be implemented in real-time scanning protocols as a clinical decision support system.AI-inferred cardiac landmarks.ROC curve delineating AS prediction.