PMID- 36897405
OWN - NLM
STAT- MEDLINE
DCOM- 20230605
LR  - 20230608
IS  - 1432-2218 (Electronic)
IS  - 0930-2794 (Print)
IS  - 0930-2794 (Linking)
VI  - 37
IP  - 6
DP  - 2023 Jun
TI  - Skill-level classification and performance evaluation for endoscopic sleeve 
      gastroplasty.
PG  - 4754-4765
LID - 10.1007/s00464-023-09955-2 [doi]
AB  - 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.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer Science+Business 
      Media, LLC, part of Springer Nature.
FAU - Dials, James
AU  - Dials J
AD  - Department of Computer Science, Florida Polytechnic University, Lakeland, FL, 
      USA.
FAU - Demirel, Doga
AU  - Demirel D
AUID- ORCID: 0000-0002-8270-1163
AD  - Department of Computer Science, Florida Polytechnic University, Lakeland, FL, 
      USA. ddemirel@floridapoly.edu.
FAU - Sanchez-Arias, Reinaldo
AU  - Sanchez-Arias R
AD  - Department of Data Science and Business Analytics, Florida Polytechnic 
      University, Lakeland, FL, USA.
FAU - Halic, Tansel
AU  - Halic T
AD  - Intuitive Surgical, Peachtree Corners, GA, USA.
FAU - Kruger, Uwe
AU  - Kruger U
AD  - Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 
      USA.
FAU - De, Suvranu
AU  - De S
AD  - College of Engineering, Florida A&M University - Florida State University, 
      Tallahassee, FL, USA.
FAU - Gromski, Mark A
AU  - Gromski MA
AD  - Division of Gastroenterology and Hepatology, Indiana University School of 
      Medicine, Indianapolis, IN, USA.
LA  - eng
GR  - R01 EB005807/EB/NIBIB NIH HHS/United States
GR  - R01 EB025241/EB/NIBIB NIH HHS/United States
GR  - R01 EB033674/EB/NIBIB NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20230310
PL  - Germany
TA  - Surg Endosc
JT  - Surgical endoscopy
JID - 8806653
SB  - IM
MH  - Humans
MH  - *Gastroplasty
MH  - Algorithms
MH  - Machine Learning
MH  - Random Forest
MH  - Support Vector Machine
PMC - PMC10000349
OTO - NOTNLM
OT  - Endoscopic simulator
OT  - Endoscopic sleeve gastroplasty
OT  - Machine learning classification
OT  - Non-linear constraint optimization
OT  - Synthetic data generation
COIS- James Dials, Drs. Doga Demirel, Reinaldo Sanchez-Arias, Tansel Halic, Uwe Kruger, 
      Suvranu De, and Mark A. Gromski have no conflict of interest or financial ties to 
      disclose.
EDAT- 2023/03/11 06:00
MHDA- 2023/06/05 06:42
PMCR- 2023/03/10
CRDT- 2023/03/10 11:15
PHST- 2022/10/30 00:00 [received]
PHST- 2023/02/12 00:00 [accepted]
PHST- 2023/06/05 06:42 [medline]
PHST- 2023/03/11 06:00 [pubmed]
PHST- 2023/03/10 11:15 [entrez]
PHST- 2023/03/10 00:00 [pmc-release]
AID - 10.1007/s00464-023-09955-2 [pii]
AID - 9955 [pii]
AID - 10.1007/s00464-023-09955-2 [doi]
PST - ppublish
SO  - Surg Endosc. 2023 Jun;37(6):4754-4765. doi: 10.1007/s00464-023-09955-2. Epub 2023 
      Mar 10.