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Deep learning for identification of gallbladder leakage during laparoscopic cholecystectomy

Gerkema, BSc. M.H. (2020) Deep learning for identification of gallbladder leakage during laparoscopic cholecystectomy.

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Abstract:This study aimed to develop a deep learning algorithm which is able to detect bile leakage in laparoscopic cholecystectomy video frames. More research into complications could be done if bile leakage is reported automatically, since studies showed that 13.0% till 73.8% of the bile leakages is not reported correctly. In total, 172 patients are included. The videos are derived from the Cholec80 dataset and from surgeries performed in the Meander Medical Centre. Video data is transformed to video frames and hereby 62380 bile and no bile leakage images are included in this study. Two CNNs and different parameters settings were used for creating an optimal bile leakage detection algorithm. Training of the deep learning algorithm and testing of the trained network, resulted in a trained model which showed 83% sensitivity, 80% specificity and an AUC score of 0.91 for the testing dataset. The most important outcome is that this trained model currently does not have clinical added value when compared to the standards of reporting bile leakage in surgery reports in the Netherlands. Although results should be improved, good results are achieved by this study and first insights are given into bile leakage detection by using a deep learning algorithm.
Item Type:Essay (Master)
Faculty:TNW: Science and Technology
Subject:44 medicine
Programme:Technical Medicine MSc (60033)
Link to this item:http://purl.utwente.nl/essays/81724
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