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      Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty

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          Abstract

          Background

          We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques.

          Methods

          We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores.

          Results

          We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72.

          Conclusion

          This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.

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          Most cited references27

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          SMOTE: Synthetic Minority Over-sampling Technique

          An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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            Is Open Access

            A survey on Image Data Augmentation for Deep Learning

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              Principal component analysis: a review and recent developments.

              Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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                Author and article information

                Contributors
                ddemirel@floridapoly.edu
                Journal
                Surg Endosc
                Surg Endosc
                Surgical Endoscopy
                Springer US (New York )
                0930-2794
                1432-2218
                10 March 2023
                : 1-12
                Affiliations
                [1 ]GRID grid.462208.a, ISNI 0000 0004 0414 1628, Department of Computer Science, , Florida Polytechnic University, ; Lakeland, FL USA
                [2 ]GRID grid.462208.a, ISNI 0000 0004 0414 1628, Department of Data Science and Business Analytics, , Florida Polytechnic University, ; Lakeland, FL USA
                [3 ]GRID grid.420371.3, ISNI 0000 0004 0417 4585, Intuitive Surgical, ; Peachtree Corners, GA USA
                [4 ]GRID grid.33647.35, ISNI 0000 0001 2160 9198, Department of Biomedical Engineering, , Rensselaer Polytechnic Institute, ; Troy, NY USA
                [5 ]College of Engineering, Florida A&M University - Florida State University, Tallahassee, FL USA
                [6 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Division of Gastroenterology and Hepatology, , Indiana University School of Medicine, ; Indianapolis, IN USA
                Author information
                http://orcid.org/0000-0002-8270-1163
                Article
                9955
                10.1007/s00464-023-09955-2
                10000349
                36897405
                20cf31dc-4926-4cf7-9c9e-99e35933b22b
                © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 30 October 2022
                : 12 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000070, National Institute of Biomedical Imaging and Bioengineering;
                Award ID: 1R01EB033674-01A1
                Award ID: 3R01EB005807-09A1S1
                Award Recipient :
                Categories
                Original Article

                Surgery
                endoscopic simulator,endoscopic sleeve gastroplasty,non-linear constraint optimization,synthetic data generation,machine learning classification

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