Artificial intelligence in cardiovascular imaging and intervention.

Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these novel technologies enables concurrent text and image analysis by so-called vision-language transformer models. They not only allow for automatic derivation of image reports, synthesis of novel images conditioned on certain textual properties, and visual questioning and answering in an oral or written dialogue style, but also for the retrieval of medical images from a large database based on a description of the pathology or specifics of the dataset of interest. Federated learning is an additional ingredient in these novel developments, facilitating multi-centric collaborative training of AI approaches and therefore access to large clinical cohorts. In this review paper, we provide an overview of the recent developments in the field of cardiovascular imaging and intervention and offer a future outlook.

Surgical phase and instrument recognition: how to identify appropriate dataset splits.

PURPOSE: Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances resulting from different length of phases and their potential erratic occurrences. Furthermore, sub-properties like instrument (co-)occurrence are usually not particularly considered when defining the split. METHODS: We present a publicly available data visualization tool that enables interactive exploration of dataset partitions for surgical phase and instrument recognition. The application focuses on the visualization of the occurrence of phases, phase transitions, instruments, and instrument combinations across sets. Particularly, it facilitates assessment of dataset splits, especially regarding identification of sub-optimal dataset splits. RESULTS: We performed analysis of the datasets Cholec80, CATARACTS, CaDIS, M2CAI-workflow, and M2CAI-tool using the proposed application. We were able to uncover phase transitions, individual instruments, and combinations of surgical instruments that were not represented in one of the sets. Addressing these issues, we identify possible improvements in the splits using our tool. A user study with ten participants demonstrated that the participants were able to successfully solve a selection of data exploration tasks. CONCLUSION: In highly unbalanced class distributions, special care should be taken with respect to the selection of an appropriate dataset split because it can greatly influence the assessments of machine learning approaches. Our interactive tool allows for determination of better splits to improve current practices in the field. The live application is available at https://cardio-ai.github.io/endovis-ml/ .

Surgical training of minimally invasive mitral valve repair on a patient-specific simulator improves surgical skills.

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.

Surgical Rehearsal for Mitral Valve Repair: Personalizing Surgical Simulation by 3D Printing.

PURPOSE: The goal of this study was to show possible effects of performing the actual procedure of mitral valve repair (MVR) on personalized silicone models 1 day before operation. DESCRIPTION: Based on preoperative 3-dimensional echocardiography recordings, flexible 3-dimensional replicas of the depicted pathologic mitral valves could be produced and used for a simulation of reconstructive techniques analogous to the upcoming MVR procedure. We integrated this step of personalized surgical planning into the clinical routine of 6 MVR cases with 3 different surgeons. This pilot study was assessed by evaluating questionnaires and by comparing isolated surgical steps with conventional MVRs. EVALUATION: This approach was considered a better preparation for MVRs with overall positive responses from the surgeons. Simulation helped reduce the time of initial inspection of the valve because of better understanding of the valve's pathomorphologic features. Annuloplasty benefited from preoperative sizing by reducing the number of sizing attempts. CONCLUSIONS: These initial findings suggest that simulation-based surgical planning can be implemented into patients' and physicians' clinical workflow as a major technologic advancement for future MVR preparation.

Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation.

PURPOSE: Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range. METHODS: This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12-20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings. RESULTS: The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures. CONCLUSION: If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Mutually Improved Endoscopic Image Synthesis and Landmark Detection in Unpaired Image-to-Image Translation.

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 F1 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.

Point detection through multi-instance deep heatmap regression for sutures in endoscopy.

PURPOSE: Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that provides quantitative information to analyse suturing patterns, assess prosthetic configurations and produce augmented reality visualisations. Facial or anatomical landmark detection tasks typically contain a fixed number of landmarks, and use regression or fixed heatmap-based approaches to localize the landmarks. However in endoscopy, there are a varying number of sutures in every image, and the sutures may occur at any location in the annulus, as they are not semantically unique. METHOD: In this work, we formulate the suture detection task as a multi-instance deep heatmap regression problem, to identify entry and exit points of sutures. We extend our previous work, and introduce the novel use of a 2D Gaussian layer followed by a differentiable 2D spatial Soft-Argmax layer to function as a local non-maximum suppression. RESULTS: We present extensive experiments with multiple heatmap distribution functions and two variants of the proposed model. In the intra-operative domain, Variant 1 showed a mean [Formula: see text] of [Formula: see text] over the baseline. Similarly, in the simulator domain, Variant 1 showed a mean [Formula: see text] of [Formula: see text] over the baseline. CONCLUSION: The proposed model shows an improvement over the baseline in the intra-operative and the simulator domains. The data is made publicly available within the scope of the MICCAI AdaptOR2021 Challenge https://adaptor2021.github.io/ , and the code at https://github.com/Cardio-AI/suture-detection-pytorch/ .