Automated Performance Assessment in Transoesophageal Echocardiography with Convolutional Neural Networks

Transoesophageal echocardiography (TEE) is a valuable diagnostic and monitoring imaging modality. Proper image acquisition is essential for diagnosis, yet current assessment techniques are solely based on manual expert review. This paper presents a supervised deep learning framework for automatically evaluating and grading the quality of TEE images. To obtain the necessary dataset, 38 participants of varied experience performed TEE exams with a high-fidelity virtual reality (VR) platform. Two Convolutional Neural Network (CNN) architectures, AlexNet and VGG, structured to perform regression, were finetuned and validated on manually graded images from three evaluators. Two different scoring strategies, a criteria-based percentage and an overall general impression, were used. The developed CNN models estimate the average score with a root mean square accuracy ranging between 84% − 93%, indicating the ability to replicate expert valuation. Proposed strategies for automated TEE assessment can have a significant impact on the training process of new TEE operators, providing direct feedback and facilitating the development of the necessary dexterous skills

Dynamic changes in the ischemic mitral annulus: Implications for ring sizing

Objectives: Contrary to the rest of the mitral annulus, inter-trigonal distance is known to be relatively less dynamic during the cardiac cycle. Therefore, intertrigonal distance is considered a suitable benchmark for annuloplasty ring sizing during mitral valve (MV) surgery. The entire mitral annulus dilates and flattens in patients with ischemic mitral regurgitation (IMR). It is assumed that the fibrous trigone of the heart and the intertrigonal distance does not dilate. In this study, we sought to demonstrate the changes in mitral annular geometry in patients with IMR and specifically analyze the changes in intertrigonal distance during the cardiac cycle. Methods: Intraoperative three-dimensional transesophageal echocardiographic data obtained from 26 patients with normal MVs undergoing nonvalvular cardiac surgery and 36 patients with IMR undergoing valve repair were dynamically analyzed using Philips Qlab ® software. Results: Overall, regurgitant valves were larger in area and less dynamic than normal valves. Both normal and regurgitant groups displayed a significant change in annular area (AA) during the cardiac cycle (P 0.05). Conclusions: Annular dimensions in regurgitant valves are dynamic and can be measured feasibly and accurately using echocardiography. The echocardiographically identified inter-trigonal distance does not change significantly during the cardiac cycle

Making three-dimensional echocardiography more tangible: a workflow for three-dimensional printing with echocardiographic data

Abstract Three-dimensional (3D) printing is a rapidly evolving technology with several potential applications in the diagnosis and management of cardiac disease. Recently, 3D printing (i.e. rapid prototyping) derived from 3D transesophageal echocardiography (TEE) has become possible. Due to the multiple steps involved and the specific equipment required for each step, it might be difficult to start implementing echocardiography-derived 3D printing in a clinical setting. In this review, we provide an overview of this process, including its logistics and organization of tools and materials, 3D TEE image acquisition strategies, data export, format conversion, segmentation, and printing. Generation of patient-specific models of cardiac anatomy from echocardiographic data is a feasible, practical application of 3D printing technology

Echocardiography derived three-dimensional printing of normal and abnormal mitral annuli

Aims and Objectives: The objective of this study was to assess the clinical feasibility of using echocardiographic data to generate three-dimensional models of normal and pathologic mitral valve annuli before and after repair procedures. Materials and Methods: High-resolution transesophageal echocardiographic data from five patients was analyzed to delineate and track the mitral annulus (MA) using Tom Tec Image-Arena software. Coordinates representing the annulus were imported into Solidworks software for constructing solid models. These solid models were converted to stereolithographic (STL) file format and three-dimensionally printed by a commercially available Maker Bot Replicator 2 three-dimensional printer. Total time from image acquisition to printing was approximately 30 min. Results: Models created were highly reflective of known geometry, shape and size of normal and pathologic mitral annuli. Post-repair models also closely resembled shapes of the rings they were implanted with. Compared to echocardiographic images of annuli seen on a computer screen, physical models were able to convey clinical information more comprehensively, making them helpful in appreciating pathology, as well as post-repair changes. Conclusions: Three-dimensional printing of the MA is possible and clinically feasible using routinely obtained echocardiographic images. Given the short turn-around time and the lack of need for additional imaging, a technique we describe here has the potential for rapid integration into clinical practice to assist with surgical education, planning and decision-making

Artificial Intelligence in Mitral Valve Analysis

Background: Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variability in the measurements. Aim: The aim of this study is to determine the interobserver variability in an automated software that uses artificial intelligence for MV analysis. Settings and Design: Retrospective analysis of intraoperative three-dimensional transesophageal echocardiography data acquired from four patients with normal MV undergoing coronary artery bypass graft surgery in a tertiary hospital. Materials and Methods: Echocardiographic data were analyzed using the eSie Valve Software (Siemens Healthcare, Mountain View, CA, USA). Three examiners analyzed three end-systolic (ES) frames from each of the four patients. A total of 36 ES frames were analyzed and included in the study. Statistical Analysis: A multiple mixed-effects ANOVA model was constructed to determine if the examiner, the patient, and the loop had a significant effect on the average value of each parameter. A Bonferroni correction was used to correct for multiple comparisons, and P = 0.0083 was considered to be significant. Results: Examiners did not have an effect on any of the six parameters tested. Patient and loop had an effect on the average parameter value for each of the six parameters as expected (P < 0.0083 for both). Conclusion: We were able to conclude that using automated analysis, it is possible to obtain results with good reproducibility, which only requires minimal user intervention

Making three-dimensional echocardiography more tangible: a workflow for three-dimensional printing with echocardiographic data

Three-dimensional (3D) printing is a rapidly evolving technology with several potential applications in the diagnosis and management of cardiac disease. Recently, 3D printing (i.e. rapid prototyping) derived from 3D transesophageal echocardiography (TEE) has become possible. Due to the multiple steps involved and the specific equipment required for each step, it might be difficult to start implementing echocardiography-derived 3D printing in a clinical setting. In this review, we provide an overview of this process, including its logistics and organization of tools and materials, 3D TEE image acquisition strategies, data export, format conversion, segmentation, and printing. Generation of patient-specific models of cardiac anatomy from echocardiographic data is a feasible, practical application of 3D printing technology