What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis? A survey of surgeons and trainees

Convolutional Neural Network-Based Deep Learning Engine for Mastoidectomy Instrument Recognition and Movement Tracking

OBJECTIVE

To develop a convolutional neural network-based computer vision model to recognize and track 2 mastoidectomy surgical instruments-the drill and the suction-irrigator-from intraoperative video recordings of mastoidectomies.

STUDY DESIGN

Technological development and model validation.

SETTING

Academic center.

METHODS

Ten 1-minute videos of mastoidectomies done for cochlear implantation by varying levels of resident surgeons were collected. For each video, containing 900 frames, an open-access computer vision annotation tool was used to annotate the drill and suction-irrigator class images with bounding boxes. A mastoidectomy instrument tracking module, which extracts the center coordinates of bounding boxes, was developed using a feature pyramid network and layered with DETECTRON, an open-access faster-region-based convolutional neural network. Eight videos were used to train the model, and 2 videos were used for testing. Outcome measures included Intersection over Union (IoU) ratio, accuracy, and average precision.

RESULTS

For an IoU of 0.5, the mean average precision for the drill was 99% and 86% for the suction-irrigator. The model proved capable of generating maps of drill and suction-irrigator stroke direction and distance for the entirety of each video.

CONCLUSIONS

This computer vision model can identify and track the drill and suction-irrigator from videos of intraoperative mastoidectomies performed by residents with excellent precision. It can now be employed to retrospectively study objective mastoidectomy measures of expert and resident surgeons, such as drill and suction-irrigator stroke concentration, economy of motion, speed, and coordination, setting the stage for characterization of objective expectations for safe and efficient mastoidectomies.

Risk Management and Patient Safety in the Artificial Intelligence Era: A Systematic Review

BACKGROUND

Healthcare systems represent complex organizations within which multiple factors (physical environment, human factor, technological devices, quality of care) interconnect to form a dense network whose imbalance is potentially able to compromise patient safety. In this scenario, the need for hospitals to expand reactive and proactive clinical risk management programs is easily understood, and artificial intelligence fits well in this context. This systematic review aims to investigate the state of the art regarding the impact of AI on clinical risk management processes. To simplify the analysis of the review outcomes and to motivate future standardized comparisons with any subsequent studies, the findings of the present review will be grouped according to the possibility of applying AI in the prevention of the different incident type groups as defined by the ICPS.

MATERIALS AND METHODS

On 3 November 2023, a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was carried out using the SCOPUS and Medline (via PubMed) databases. A total of 297 articles were identified. After the selection process, 36 articles were included in the present systematic review.

RESULTS AND DISCUSSION

The studies included in this review allowed for the identification of three main "incident type" domains: clinical process, healthcare-associated infection, and medication. Another relevant application of AI in clinical risk management concerns the topic of incident reporting.

CONCLUSIONS

This review highlighted that AI can be applied transversely in various clinical contexts to enhance patient safety and facilitate the identification of errors. It appears to be a promising tool to improve clinical risk management, although its use requires human supervision and cannot completely replace human skills. To facilitate the analysis of the present review outcome and to enable comparison with future systematic reviews, it was deemed useful to refer to a pre-existing taxonomy for the identification of adverse events. However, the results of the present study highlighted the usefulness of AI not only for risk prevention in clinical practice, but also in improving the use of an essential risk identification tool, which is incident reporting. For this reason, the taxonomy of the areas of application of AI to clinical risk processes should include an additional class relating to risk identification and analysis tools. For this purpose, it was considered convenient to use ICPS classification.

A foundation for evaluating the surgical artificial intelligence literature

With increasing growth in applications of artificial intelligence (AI) in surgery, it has become essential for surgeons to gain a foundation of knowledge to critically appraise the scientific literature, commercial claims regarding products, and regulatory and legal frameworks that govern the development and use of AI. This guide offers surgeons a framework with which to evaluate manuscripts that incorporate the use of AI. It provides a glossary of common terms, an overview of prerequisite knowledge to maximize understanding of methodology, and recommendations on how to carefully consider each element of a manuscript to assess the quality of the data on which an algorithm was trained, the appropriateness of the methodological approach, the potential for reproducibility of the experiment, and the applicability to surgical practice, including considerations on generalizability and scalability.

Beyond the Scalpel: A Tapestry of Surgical Safety, Precision, and Patient Prosperity

In modern surgical practice, the focus extends beyond simply making and closing incisions. We aim to investigate the various complex aspects that redefine the criteria for achieving effective surgical outcomes. This narrative combines current knowledge, integrating practical experiences and academic viewpoints to comprehend the changing field of surgical care thoroughly. The tapestry explores the detailed aspects of surgical safety, examining the most recent progress in protocols, technology, and team dynamics that strive to reduce procedural risks. Examining precision in surgery, this narrative goes beyond conventional limits to explore the incorporation of advanced technologies, such as robotics and navigational systems. The complex interplay between the surgeon's proficiency and these technology aids is crucial in attaining unparalleled accuracy and favorable patient results. The focal point of this investigation is the patient's well-being, encompassing postoperative care, rehabilitation, and long-term health. Actual accounts from surgical procedures highlight the significant influence of comprehensive patient-centered methods, emphasizing the crucial need for empathy, communication, and individualized care plans in promoting healing and adaptability. As we explore this complex situation, the combination of real-life stories and academic discussions creates a clear and detailed image of a surgical environment that goes far beyond the boundaries of the operating room. "Beyond the Scalpel" seeks to engage practitioners, scholars, and stakeholders in a conversation that redefines the criteria for surgical success. It aims to establish a new benchmark that combines safety, precision, and patient well-being, ultimately shaping the future of surgical practice.

Innovative Approaches to Safe Surgery: A Narrative Synthesis of Best Practices

By encompassing a wide range of best practices within the ever-changing realm of modern surgical care, this exhaustive narrative compendium attempts to unravel the complex tapestry of novel approaches to safe surgery. Within the context of a dynamic surgical environment, this research endeavors to illuminate and integrate state-of-the-art methods that collectively methodically improve patient safety. The narrative elucidates a diverse array of practices that seek to revolutionize the paradigm of safe surgery, emphasizing technological progress, patient-centric approaches, and global viewpoints. The combined effectiveness of these methods in fostering an all-encompassing culture of safety, improving surgical precision, and decreasing complications is revealed by the results obtained from their implementation. The recognition of the dynamic interplay among multiple components, including the active participation of patients, the integration of cutting-edge technologies, and the establishment of comprehensive quality improvement programs, is fundamental to this narrative. By their collective composition, these components support the notion that secure surgical practices are intricate and interrelated. The present synthesis functions as a fundamental resource for healthcare professionals, policymakers, and researchers, providing an enlightening examination of the current condition of secure surgical practices. By emphasizing the promotion of innovation, continuous development, and the utmost quality of patient care, it offers a strategic guide for navigating the complex terrain of safe surgery. In the ever-evolving landscape of surgical care, this narrative synthesis serves as a guiding principle for stakeholders striving to understand better and implement safe surgical procedures in various healthcare environments.