31 research outputs found
Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery
Understanding and anticipating intraoperative events and actions is critical
for intraoperative assistance and decision-making during minimally invasive
surgery. Automated prediction of events, actions, and the following
consequences is addressed through various computational approaches with the
objective of augmenting surgeons' perception and decision-making capabilities.
We propose a predictive neural network that is capable of understanding and
predicting critical interactive aspects of surgical workflow from
intra-abdominal video, while flexibly leveraging surgical knowledge graphs. The
approach incorporates a hypergraph-transformer (HGT) structure that encodes
expert knowledge into the network design and predicts the hidden embedding of
the graph. We verify our approach on established surgical datasets and
applications, including the detection and prediction of action triplets, and
the achievement of the Critical View of Safety (CVS). Moreover, we address
specific, safety-related tasks, such as predicting the clipping of cystic duct
or artery without prior achievement of the CVS. Our results demonstrate the
superiority of our approach compared to unstructured alternatives
Concept Graph Neural Networks for Surgical Video Understanding
We constantly integrate our knowledge and understanding of the world to
enhance our interpretation of what we see.
This ability is crucial in application domains which entail reasoning about
multiple entities and concepts, such as AI-augmented surgery. In this paper, we
propose a novel way of integrating conceptual knowledge into temporal analysis
tasks via temporal concept graph networks. In the proposed networks, a global
knowledge graph is incorporated into the temporal analysis of surgical
instances, learning the meaning of concepts and relations as they apply to the
data. We demonstrate our results in surgical video data for tasks such as
verification of critical view of safety, as well as estimation of Parkland
grading scale. The results show that our method improves the recognition and
detection of complex benchmarks as well as enables other analytic applications
of interest
SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education)
BACKGROUND: Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. METHODS: Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. RESULTS: The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. CONCLUSION: This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow
Artificial Intelligence in Surgery: Promises and Perils
Objective: The aim of this review was to summarize major topics in artificial intelligence (AI), including their applications and limitations in surgery. This paper reviews the key capabilities of AI to help surgeons understand and critically evaluate new AI applications and to contribute to new developments. Summary Background Data: AI is composed of various subfields that each provide potential solutions to clinical problems. Each of the core subfields of AI reviewed in this piece has also been used in other industries such as the autonomous car, social networks, and deep learning computers. Methods: A review of AI papers across computer science, statistics, and medical sources was conducted to identify key concepts and techniques within AI that are driving innovation across industries, including surgery. Limitations and challenges of working with AI were also reviewed. Results: Four main subfields of AI were defined: (1) machine learning, (2) artificial neural networks, (3) natural language processing, and (4) computer vision. Their current and future applications to surgical practice were introduced, including big data analytics and clinical decision support systems. The implications of AI for surgeons and the role of surgeons in advancing the technology to optimize clinical effectiveness were discussed. Conclusions: Surgeons are well positioned to help integrate AI into modern practice. Surgeons should partner with data scientists to capture data across phases of care and to provide clinical context, for AI has the potential to revolutionize the way surgery is taught and practiced with the promise of a future optimized for the highest quality patient care
Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery
© 2017 IEEE. Context-aware segmentation of laparoscopic and robot assisted surgical video has been shown to improve performance and perioperative workflow efficiency, and can be used for education and time-critical consultation. Modern pressures on productivity preclude manual video analysis, and hospital policies and legacy infrastructure are often prohibitive of recording and storing large amounts of data. In this paper we present a system that automatically generates a video segmentation of laparoscopic and robot-assisted procedures according to their underlying surgical phases using minimal computational resources, and low amounts of training data. Our system uses an SVM and HMM in combination with an augmented feature space that captures the variability of these video streams without requiring analysis of the nonrigid and variable environment. By using the data reduction capabilities of online k-segment coreset algorithms we can efficiently produce results of approximately equal quality, in realtime. We evaluate our system in cross-validation experiments and propose a blueprint for piloting such a system in a real operating room environment with minimal risk factors