TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Beyersdorffer, Patrick
A1  - Kunert, Wolfgang
A1  - Jansen, Kai
A1  - Miller, Johanna
A1  - Wilhelm, Peter
A1  - Burgert, Oliver
A1  - Kirschniak, Andreas
A1  - Rolinger, Jens
T1  - Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
JF  - Biomedical Engineering / Biomedizinische Technik
N2  - Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
KW  - convolutional neural network
KW  - image data classification
KW  - inadvertent injury
KW  - laparoscopic surgery
KW  - selective data augmentation
KW  - surgical training
Y1  - 2021
SN  - 0013-5585
SS  - 0013-5585
U6  - https://doi.org/10.1515/bmt-2020-0106
DO  - https://doi.org/10.1515/bmt-2020-0106
VL  - 66
IS  - 4
SP  - 413
EP  - 421
S1  - 9
PB  - De Gruyter
CY  - Berlin
ER  -