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Mitral valve regurgitation is one of the most common heart valve diseases that can occur when the structural composition of the mitral valve is affected. Mitral valve repair, typically performed in a minimally invasive setting, is a complex surgery that aims to reinstate the structural integrity of the valve and thereby restore normal valve function. The current practice lacks quantification of geometrical changes and interactive 3D visualization that could enhance the understanding of pathomorphological changes and impact of surgical correction. This work presents functionalities from smartMVR, a quantitative tool to measure, compare, and analyse changes in mitral valve geometry from data captured on-demand using an RGB-D camera. The feasibility of the approach is demonstrated on a silicone replica of a pathological mitral valve that is iteratively corrected using common surgical techniques.

Open in new tab Download slide OBJECTIVES Minimally invasive mitral valve repair (MVR) is considered one of the most challenging operations in cardiac surgery and requires much practice and experience. Simulation-based surgical training might be a method to support the learning process and help to flatten the steep learning curve of novices. The purpose of this study was to show the possible effects on learning of surgical training using a high-fidelity simulator with patient-specific mitral valve replicas. METHODS Twenty-five participants were recruited to perform MVR on anatomically realistic valve models during different training sessions. After every session their performance was evaluated by a surgical expert regarding accuracy and duration for each step. A second blinded rater similarly assessed the performance after the study. Through repeated documentation of those parameters, their progress in learning was analysed, and gains in proficiency were evaluated. RESULTS Participants showed significant performance enhancements in terms of both accuracy and time. Their surgical skills showed sizeable improvements after only 1 session. For example, the time to implant neo-chordae decreased by 24.64% (354 s-264 s, P  < 0.001) and the time for annuloplasty by 4.01% (54 s-50 s, P  = 0.165), whereas the number of irregular stitches for annuloplasty decreased from 52% to 24%. The significance of simulation-based surgical training as a tool for acquiring and training surgical skills was reviewed positively. CONCLUSIONS The results of this study indicate that simulation-based surgical training is a valuable and effective method for learning reconstructive techniques of minimally invasive MVR and overall general dexterity. The novel learning and training options should be implemented in the surgical traineeship for systematic teaching of various surgical skills. [ABSTRACT FROM AUTHOR]

The CycleGAN framework allows for unsupervised image-to-image translation of unpaired data. In a scenario of surgical training on a physical surgical simulator, this method can be used to transform endoscopic images of phantoms into images which more closely resemble the intra-operative appearance of the same surgical target structure. This can be viewed as a novel augmented reality approach, which we coined Hyperrealism in previous work. In this use case, it is of paramount importance to display objects like needles, sutures or instruments consistent in both domains while altering the style to a more tissue-like appearance. Segmentation of these objects would allow for a direct transfer, however, contouring of these, partly tiny and thin foreground objects is cumbersome and perhaps inaccurate. Instead, we propose to use landmark detection on the points when sutures pass into the tissue. This objective is directly incorporated into a CycleGAN framework by treating the performance of pre-trained detector models as an additional optimization goal. We show that a task defined on these sparse landmark labels improves consistency of synthesis by the generator network in both domains. Comparing a baseline CycleGAN architecture to our proposed extension (DetCycleGAN), mean precision (PPV) improved by $+61.32$ , mean sensitivity (TPR) by $+37.91$ , and mean $F_1$ score by $+0.4743$. Furthermore, it could be shown that by dataset fusion, generated intra-operative images can be leveraged as additional training data for the detection network itself. [ABSTRACT FROM AUTHOR]