Surgical phase modelling in minimal invasive surgery

Meeuwsen, F.C.; Luyn, F. van; Blikkendaal, Mathijs D.; Jansen, Frank-Willem; Dobbelsteen, John J. van den

doi:10.1007/s00464-018-6417-4

Cited by 25 publications

(14 citation statements)

References 22 publications

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“…Inspired by this intuition in procedural skill development, the SDS community has firstly focused on developing computer vision systems for automatic phase recognition and instrument detection, fundamental elements of surgical workflows, 39 with the aim to then provide context‐aware assistance in the OR 39,40 . Starting from fairly standardized procedures such as cholecystectomy and hysterectomy, early algorithms made use of hand‐crafted signals such as surgical instrument usage and time dependencies to model and visualize surgical workflow using classical machine learning techniques such as dynamic time warping and hidden Markov models 41,42 . More recently, breakthroughs in deep neural networks have boosted computer vision performance and revived the field of surgical workflow analysis 43 …”

Section: Introductionmentioning

confidence: 99%

Surgical data science and artificial intelligence for surgical education

Ward

Mascagni

Madani

et al. 2021

Journal of Surgical Oncology

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Surgical data science (SDS) aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis, and modeling of procedural data. As data capture has increased and artificial intelligence (AI) has advanced, SDS can help to unlock augmented and automated coaching, feedback, assessment, and decision support in surgery. We review major concepts in SDS and AI as applied to surgical education and surgical oncology.

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Section: Introductionmentioning

confidence: 99%

Surgical data science and artificial intelligence for surgical education

Ward

Mascagni

Madani

et al. 2021

Journal of Surgical Oncology

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“…Thus far, most studies related to surgical step recognition modeling have focused on laparoscopic cholecystectomy because of its standard and frequent execution [16,[29][30][31]. However, recently, to improve step recognition systems and extend their range of applications, increasingly diverse and complex procedures have been subjected to step recognition modeling, including laparoscopic total hysterectomy [32], robot-assisted partial nephrectomy [17], laparoscopic sleeve gastrectomy [18], and laparoscopic colorectal surgery [19]. Nevertheless, to the best of our knowledge, this is the first study based on the automatic surgical step classification task for TaTME.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

et al. 2021

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Background Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require intraoperative video analysis, such as transanal total mesorectal excision (TaTME); however, manual video indexing is time-consuming. Thus, in this study, we constructed an annotated video dataset for TaTME with surgical step information and evaluated the performance of a deep learning model in recognizing the surgical steps in TaTME. Methods This was a single-institutional retrospective feasibility study. All TaTME intraoperative videos were divided into frames. Each frame was manually annotated as one of the following major steps: (1) purse-string closure; (2) full thickness transection of the rectal wall; (3) down-to-up dissection; (4) dissection after rendezvous; and (5) purse-string suture for stapled anastomosis. Steps 3 and 4 were each further classified into four sub-steps, specifically, for dissection of the anterior, posterior, right, and left planes. A convolutional neural network-based deep learning model, Xception, was utilized for the surgical step classification task. Results Our dataset containing 50 TaTME videos was randomly divided into two subsets for training and testing with 40 and 10 videos, respectively. The overall accuracy obtained for all classification steps was 93.2%. By contrast, when sub-step classification was included in the performance analysis, a mean accuracy (± standard deviation) of 78% (± 5%), with a maximum accuracy of 85%, was obtained. Conclusions To the best of our knowledge, this is the first study based on automatic surgical step classification for TaTME. Our deep learning model self-learned and recognized the classification steps in TaTME videos with high accuracy after training. Thus, our model can be applied to a system for intraoperative guidance or for postoperative video indexing and analysis in TaTME procedures.

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“…Model representations can be categorized as descriptive or as numerical. In descriptive representations, the behaviour of a system is described using plain text as a list of encountered activities, e.g., [2,20,48], surgical milestones, e.g., [49], etc. In numeric representation the behaviour of a system is modelled using numbers, mathematical relations or programming languages.…”

Section: Model Representationmentioning

confidence: 99%

“…They also provide input for human designers to clearly visualize workflows, making easy-to-interpret models and visualizing relations and patterns between extensive sets of actions and decisions. So far, different studies have aimed at the investigation of employing surgical procedure models for various purposes, such as surgeon skills evaluation and training [4][5][6][7][8], analysing clinical team workload [9,10] optimization of operating room (OR) management [11][12][13], introduction of new technologies [14][15][16], predicting next surgical task [17,18], and predicting surgery duration [19,20].…”

Section: Introductionmentioning

confidence: 99%

Surgical process modelling strategies: which method to choose for determining workflow?

Gholinejad

Loeve

Dankelman

2019

Minimally Invasive Therapy & Allied Technologies

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The vital role of surgeries in healthcare requires a constant attention to improvement. Surgical process modelling is an innovative and rather recently introduced approach for tackling the issues in today's complex surgeries. This modelling field is very challenging and still under development, therefore, it is not always clear which modelling strategy would best fit the needs in which situations. The aim of this study was to provide a guide for matching the choice of modelling strategies for determining surgical workflows. In this work, the concepts associated with surgical process modelling are described, aiming to clarify them and to promote their use in future studies. The relationship of these concepts and the possible combinations of the suitable approaches for modelling strategies are elaborated and the criteria for opting for the proper modelling strategy are discussed.

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Surgical phase modelling in minimal invasive surgery

Abstract: This study demonstrates an intra-operative approach to recognise surgical phases in 40 laparoscopic hysterectomy cases based on instrument usage data. The model is capable of automatic detection of surgical phases for generation of a solid prediction of the surgical end-time.

Cited by 25 publications

References 22 publications

Surgical data science and artificial intelligence for surgical education

Surgical data science and artificial intelligence for surgical education

Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision

Surgical process modelling strategies: which method to choose for determining workflow?

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