Abstract 4363303: An Artificial Intelligence-Enhanced Echocardiographic Characterization of Cardiac Masses

Background: Cardiac masses are of challenging diagnostic characterization, with differentiation difficulties on echocardiographic images, particularly in differentiating tumors from thrombi. Artificial intelligence (AI), particularly machine learning and deep learning algorithms, may enhance image interpretation. Research Question: We implemented and evaluated an AI model for characterizing cardiac masses from transesophageal (TEE) images, with magnetic resonance imaging or post-surgical histology as gold standards. Methods: A total of 53 series of TEE images of intra-atrial masses obtained from 43 patients were analyzed. Twenty-one masses were ultimately diagnosed as cardiac tumors (76% myxomas), while the remaining were classified as thrombi, or endocarditis vegetations. Regions of interest were selected from single frames by 2 investigators blinded to the final diagnosis. A repeated nested cross-validation (CV) scheme was used, consisting of 100 repetitions of a 5-fold CV with stratification at the patient level based on target labels. Hyperparameter selection was done using a 5-repetition 2-fold CV. The model included a feature selection step, followed by regularized logistic regression. A permutation test was conducted by shuffling the target labels in each external CV repetition and comparing the area under the receiver operating characteristic curve (AUC-ROC) to confirm robustness of the findings. Results: The AUC-ROC for the standard dataset was (median [Interquartile range]) 0.64 [0.60, 0.67], compared to 0.51 [0.45, 0.58] for the permuted model (p<0.001, Mann–Whitney U test). The model performance metrics included a true positive rate of 0.54 [0.51, 0.57], a true negative rate of 0.64 [0.62, 0.69], a positive predictive value of 0.50 [0.46, 0.53], a negative predictive value of 0.70 [0.68, 0.72], and 0.61 [0.59, 0.63] accuracy ( Figure ). Conclusions: Thus, AI integration in echocardiography imaging can significantly enhance diagnostic precision in distinguishing cardiac tumors from other cardiac space-occupying lesions, thus contributing to improved clinical decision-making and appropriate second-tier resource utilization.