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      Generic surgical process model for minimally invasive liver treatment methods

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          Abstract

          Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced. The generic surgical process model was established at three different granularity levels. The generic process model, encompassing thirteen phases, was verified against videos of MILT procedures and interviews with surgeons. The established model covers all the surgical and interventional activities and the connections between them and provides a foundation for extensive quantitative analysis and simulations of MILT procedures for improving computer-assisted surgery systems, surgeon training and evaluation, surgeon guidance and planning systems and evaluation of new technologies.

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          EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos.

          Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase recognition has been studied in the context of several kinds of surgeries, such as cataract, neurological, and laparoscopic surgeries. In the literature, two types of features are typically used to perform this task: visual features and tool usage signals. However, the used visual features are mostly handcrafted. Furthermore, the tool usage signals are usually collected via a manual annotation process or by using additional equipment. In this paper, we propose a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information. In previous studies, it has been shown that the tool usage signals can provide valuable information in performing the phase recognition task. Thus, we present a novel CNN architecture, called EndoNet, that is designed to carry out the phase recognition and tool presence detection tasks in a multi-task manner. To the best of our knowledge, this is the first work proposing to use a CNN for multiple recognition tasks on laparoscopic videos. Experimental comparisons to other methods show that EndoNet yields state-of-the-art results for both tasks.
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            Laparoscopic Versus Open Resection for Colorectal Liver Metastases

            To perform the first randomized controlled trial to compare laparoscopic and open liver resection.
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              Machine Learning for Surgical Phase Recognition : A Systematic Review

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                Author and article information

                Contributors
                m.gholinejad-1@tudelft.nl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 October 2022
                6 October 2022
                2022
                : 12
                : 16684
                Affiliations
                [1 ]GRID grid.5292.c, ISNI 0000 0001 2097 4740, Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, , Delft University of Technology, ; Delft, The Netherlands
                [2 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, The Intervention Centre, , Oslo University Hospital, ; Oslo, Norway
                [3 ]GRID grid.5510.1, ISNI 0000 0004 1936 8921, Institute of Clinical Medicine, Medical Faculty, , University of Oslo, ; Oslo, Norway
                [4 ]GRID grid.427559.8, ISNI 0000 0004 0418 5743, Department of Surgery N1, , Yerevan State Medical University After M. Heratsi, ; Yerevan, Armenia
                [5 ]GRID grid.55325.34, ISNI 0000 0004 0389 8485, Department of HPB Surgery, , Oslo University Hospital, ; Oslo, Norway
                [6 ]GRID grid.5645.2, ISNI 000000040459992X, Department of Surgery, Division of HPB and Transplant Surgery, , Erasmus MC, University Medical Centre Rotterdam, ; Rotterdam, The Netherlands
                Article
                19891
                10.1038/s41598-022-19891-1
                9537522
                36202857
                f17be842-cfc8-422d-b764-fd23b41cc251
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 October 2021
                : 6 September 2022
                Funding
                Funded by: This work is part of the HiPerNav project that received funding from the European Union’s Horizon 2020 Research and Innovation program.
                Award ID: 722068
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                medical research,oncology,surgery,therapeutic endoscopy,therapeutics,biomedical engineering

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