Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy

Guérin, Sonia; Huaulmé, Arnaud; Lavoué, Vincent; Jannin, Pierre; Timoh, Krystel Nyangoh

doi:10.1007/s00464-021-08792-5

Cited by 13 publications

(17 citation statements)

References 54 publications

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“…However, the variability and complexity of the surgery can play a role in time stamping and in assessing the quality of the performance [ 16 ]. Although some of the platforms have an additive difficulty score based on the surgeon’s input, developing a machine-based classification will help to universally quantify the surgery according to its difficulty [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Gendia¹

2022

Cureus

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Aims: To outline the use of cloud-based artificial intelligence (AI)-driven video analytics (CAVs) in minimally invasive surgery and to propose their potential as a virtual portfolio for trainee and established surgeons.Methods: An independent online demonstration was requested from three platforms, namely Theator (Palo Alto, California, USA), Touch Surgery™ (Medtronic, London, England, UK), and C-SATS® (Seattle, Washington, USA). The assessed domains were online and app-based accessibility, the ability for timely trainee feedback, and AI integration for operation-specific steps and critical views.Results: The CAVs enable users to record surgeries with the advantage of limitless video storage through clouding and smart integration into theatre settings. This can be used to view surgeries and review trainee videos through a medium of communication and sharing with the ability to provide feedback. Theator and C-SATS® provide their users with surgical skills scoring systems with customizable options that can be used to provide structured feedback to trainees. Additionally, AI plays an important role in all three platforms by providing time-based analysis of steps and highlighting critical milestones. Conclusion: Cloud-based AI-driven video analytics is an emerging new technology that enables users to store, analyze, and review videos. This technology has the potential to improve training, governance, and standardization procedures. Moreover, with the future adaptation of the technology, CAVs can be integrated into the trainees' portfolios as part of their virtual curriculum. This can enable a structured assessment of a surgeon's progression and degree of experience throughout their surgical career.

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Section: Discussionmentioning

confidence: 99%

Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Gendia¹

2022

Cureus

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“…Artificial intelligence holds significant promise for the implementation of such surgical adverse event detection models into clinical practice. 78 For example, a machine learning approach for the detection of surgical site infections from electronic health records demonstrated its applicability in aiding data abstraction for complication reporting. 81,82 Surgical Quality Improvement Cycle Precise reporting of procedure-specific standardized surgical outcomes is essential to stimulate and monitor surgical improvement.…”

Section: Digitalization In Prediction Prevention and Recording Of Com...mentioning

confidence: 99%

“…With the advent of surgical robots and advanced laparoscopic systems, automated surgical performance metrics-objective measures related to instrument movements-are now available. 77,78 Further, through realtime data processing using machine learning, new possibilities of pre-or even intraoperative clinical decision making are evolving. The MySurgeryRisk online calculator uses artificial intelligence to estimate probabilistic risk scores for eight postoperative QIs and death with high discriminatory ability using data from the available preoperative electronic health records.…”

Section: Digitalization In Prediction Prevention and Recording Of Com...mentioning

confidence: 99%

Classification of Adverse Events in Adult Surgery

2023

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Successful surgery combines quality (achievement of a positive outcome) with safety (avoidance of a negative outcome). Outcome assessment serves the purpose of quality improvement in health care by establishing performance indicators and allowing the identification of performance gaps. Novel surgical quality metric tools (benchmark cutoffs and textbook outcomes) provide procedure-specific ideal surgical outcomes in a subgroup of well-defined low-risk patients, with the aim of setting realistic and best achievable goals for surgeons and centers, as well as supporting unbiased comparison of surgical quality between centers and periods of time. Validated classification systems have been deployed to grade adverse events during the surgical journey: (1) the ClassIntra classification for the intraoperative period; (2) the Clavien–Dindo classification for the gravity of single adverse events; and the (3) Comprehensive Complication Index (CCI) for the sum of adverse events over a defined postoperative period. The failure to rescue rate refers to the death of a patient following one or more potentially treatable postoperative adverse event(s) and is a reliable proxy of the institutional safety culture and infrastructure. Complication assessment is undergoing digital transformation to decrease resource-intensity and provide surgeons with real-time pre- or intraoperative decision support. Standardized reporting of complications informs patients on their chances to realize favorable postoperative outcomes and assists surgical centers in the prioritization of quality improvement initiatives, multidisciplinary teamwork, surgical education, and ultimately, in the enhancement of clinical standards.

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“…Another example is prostate or uterine biopsy, where the main metric is the reached position [83,84]. It is important to note that new automatic metric extraction methods are increasingly developed to address this issue [85].…”

Section: Gesture Evaluationmentioning

confidence: 99%

Review on Needle Insertion Haptic Simulation

et al. 2022

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This short review updates an exhaustive one written by Correa et al in 2019 about haptic training simulation on needle insertion in the medical field. b) Recent findings: Latest works refine wellknown models and enhance setups and methods to facilitate generically getting experimental data. c) Summary:We provide a complementary focus on device specifications and recent models to render this specific haptic feedback on Computer-Based Simulators. Assessment approaches and the issues encountered when introducing such simulators into curricula are also discussed. FEM-based approaches still do not permit real-time computation but hybrid approaches as proposed by [1] may become a good compromise. Nonetheless, psychophysical studies should be performed to determine the haptic fidelity of the various approaches found in the literature, and embed them efficiently in medical curricula. This would permit to delay the necessary final hands-on training on patients that raises ethical issues.

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Review of automated performance metrics to assess surgical technical skills in robot-assisted laparoscopy

Cited by 13 publications

References 54 publications

Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Classification of Adverse Events in Adult Surgery

Review on Needle Insertion Haptic Simulation

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