Automatic Detection of Vegetations With Transesophageal Echocardiography in Infective Endocarditis Using Artificial Intelligence.

Infective endocarditis (IE) is a life-threatening condition frequently associated with endocardial lesions known as vegetations. Detection and characterization of these lesions are critical for a proper diagnosis and management of the disease according to the current standard practice, but current human analysis techniques present severe limitations such as very basic set of measurements and high inter-operator variability. This is a retrospective observational study across 7 hospitals with 329 IE patients. An AI-based model was trained to detect vegetations in transesophageal echocardiographic (TEE) images. We measured the accuracy of the system both in terms of vegetation detection at the frame level (i.e., answering the question "is there any vegetation in this image and, if so, where is it?") and vegetation diagnosis at the patient level (i.e., "does this patient have a vegetation?"). Two different architectures, YOLO and DETR, were evaluated within the AI-based model framework, and a comparative analysis of their performance was performed. The model exhibited strong diagnostic capability, achieving an area under the receiver-operating characteristic curve (AUROC) of 0.91 (average positive predictive value = 0.81, true positive rate = 0.83). Vegetation detection at the frame level also achieved promising performance metrics (positive predictive value = 0.83, true positive rate = 0.75). The algorithm achieved high-performance metrics detecting vegetations and identifying patients with vegetations, which can facilitate and accelerate IE diagnosis by non-expert cardiologists.