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

Hameed, M. Saif; Laplante, Simon; Masino, Caterina; Khalid, Muhammad Uzair; Zhang, Haochi; Protserov, Sergey; Hunter, Jaryd; Mashouri, Pouria; Fecso, Andras B.; Brudno, Michael; Madani, Amin

doi:10.1007/s00464-023-10377-3

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Consequently, it is intuitive that the use of AI, in a clinical risk management perspective, concerns mainly these aspects of the Clinical process. By applying the ICPS classification, the use of AI in studies aimed at preventing the misinterpretation of radiographic investigations [ 28 , 29 , 56 ] or the operating field [ 30 ], translates into the potential advantage of avoiding inadequate treatments and procedures other than incorrect interventions.…”

Section: Resultsmentioning

confidence: 99%

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

Ferrara,

Bertozzi,

Di Fazio

et al. 2024

Healthcare

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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.

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Section: Resultsmentioning

confidence: 99%

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

Ferrara,

Bertozzi,

Di Fazio

et al. 2024

Healthcare

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“…Additionally, while video‐based review and coaching have been shown to be useful for supplementing intraoperative mastoidectomy education, 29 these tasks remain time‐consuming and subjective, issues which computer vision analysis has the potential to overcome. A recent survey of general surgeon experts and residents interfacing with a validated artificial intelligence model for laparoscopic cholecystectomy analysis found that most participants agreed that the tool was an effective adjunct to surgical training and was effective at providing postoperative feedback and improving confidence 30 . Currently, it is unknown whether these perceptions correlate to any changes in surgical behavior or skills improvements.…”

Section: Discussionmentioning

confidence: 99%

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

Raymond,

Biswal,

Pipaliya

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveTo 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 DesignTechnological development and model validation.SettingAcademic center.MethodsTen 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.ResultsFor 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.ConclusionsThis 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.

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“…Human factors, including stress, weariness, and situational awareness, can influence decision-making and overall performance. Hence, it is crucial to prioritize enhancing surgical safety by effectively addressing these variables through appropriate training, consistent team building, and a dedicated focus on the human dynamics within the operating room [13].…”

Section: Human Factors and Communicationmentioning

confidence: 99%

“…Surgical checklists and defined protocols have become essential to ensure patient safety in the operating room. The WHO Surgical Safety Checklist, implemented in 2009, has gained international recognition and widespread adoption as a standard [13]. The checklist comprises pre-anesthesia induction, pre-skin incision, and pre-patient departure from the operating theater.…”

Section: Surgical Checklists and Protocolsmentioning

confidence: 99%

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

Hussain,

Kakakhel,

Ashraf

et al. 2023

Cureus

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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.

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

Cited by 7 publications

References 18 publications

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

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

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

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

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