RESUMEN
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.
RESUMEN
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/ .
