The ResNet-18 deep-learning model achieved an AUC of 0.889 for classifying right heart masses on 2D transthoracic echocardiography, significantly outperforming support vector machines.
Observational (n=100)
No
Can AI-assisted radiomics accurately classify benign and non-benign right heart masses on 2D echocardiography?
AI-assisted radiomics using deep learning and machine learning models provides high diagnostic accuracy for classifying right heart masses on 2D echocardiography.
Effect estimate: AUC difference 0.254 (95% CI -0.453 to -0.055)
Absolute Event Rate: 0.889% vs 0.635%
p-value: p=0.013
Abstract Background The rarity of right heart masses challenges diagnostic proficiency, while reproducibility is affected by the echocardiography operator. Artificial intelligence (AI)-based imaging tools may help address these limitations. Methods In this retrospective study (2013–2024), we enrolled surgical patients with right heart masses and obtained preoperative transthoracic (TTE) and transesophageal (TEE) echocardiographic images. Two-dimensional (2D) TTE ( n = 98) and TEE ( n = 87) images underwent radiomics analysis. Binary classification models were developed to differentiate benign from non-benign lesions using five machine-learning (ML) algorithms (decision trees, logistic regression, random forests, support vector machines (SVMs), extreme gradient boosting (XGBoost)). ML performance was compared with that of a deep-learning model based on the residual network (ResNet)-18 architecture using standard evaluation metrics such as the area under the curve (AUC). Results In 2D TTE analysis, ResNet-18 achieved the highest AUC (0.889), followed by XGBoost (0.836) and decision tree (0.815). ResNet-18 significantly outperformed SVM ( P = 0.013) and logistic regression ( P = 0.028), but showed no significant differences versus XGBoost ( P = 0.408), decision tree ( P = 0.429), or random forest ( P = 0.053). In 2D TEE analysis, SVM achieved the highest AUC (0.959), followed by XGBoost (0.924) and random forest (0.906), with no significant differences among these models (all P > 0.05). ResNet-18 (AUC = 0.900) significantly outperformed only the decision tree ( P = 0.027). Conclusion ResNet-18 showed the highest TTE AUC and outperformed SVM and logistic regression, but was comparable to other ML models. SVM achieved the highest TEE AUC, with no significant differences among top models. These findings provide a preliminary AI benchmark for right heart mass diagnosis, though external validation is needed.
Chen et al. (Sat,) conducted a observational in Right heart masses (n=100). ResNet-18 deep-learning model vs. Machine-learning models (including Support Vector Machine) was evaluated on Area under the curve (AUC) for differentiating benign from non-benign right heart masses on 2D TTE (AUC difference 0.254, 95% CI -0.453 to -0.055, p=0.013). The ResNet-18 deep-learning model achieved an AUC of 0.889 for classifying right heart masses on 2D transthoracic echocardiography, significantly outperforming support vector machines.