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Applied machine learning for liver surgery

Applied machine learning for liver surgery AbstractBackground and objectives: Both hepatic functional reserve and the underlying histology are important determinants in the preoperative risk evaluation before major hepatectomies. In this project we developed a new approach that implements cutting-edge research in machine learning and nevertheless is cheap and easily applicable in a routine clinical setting is needed. Methods: After splitting the study population into a training and test set we trained a convolutional neural network to predict the liver function as determined by hepatobiliary mebrofenin scintigraphy and single photon emission computer tomography (SPECT) imaging. Results: We developed a workflow for predicting liver function from routine CT imaging data using convolutional neural networks. We also evaluated in how far transfer learning and data augmentation can help to solve remaining manual data pre-processing steps and implemented the developed workflow in a clinical routine setting. Conclusion: We propose a robust semiautomatic end-to-end classification workflow for abdominal CT scans for the prediction of liver function based on a deep convolutional neural network model that shows reliable and accurate results even with limited computational resources. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Current Directions in Biomedical Engineering de Gruyter

Applied machine learning for liver surgery

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Publisher
de Gruyter
Copyright
© 2021 by Walter de Gruyter Berlin/Boston
eISSN
2364-5504
DOI
10.1515/cdbme-2021-1033
Publisher site
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Abstract

AbstractBackground and objectives: Both hepatic functional reserve and the underlying histology are important determinants in the preoperative risk evaluation before major hepatectomies. In this project we developed a new approach that implements cutting-edge research in machine learning and nevertheless is cheap and easily applicable in a routine clinical setting is needed. Methods: After splitting the study population into a training and test set we trained a convolutional neural network to predict the liver function as determined by hepatobiliary mebrofenin scintigraphy and single photon emission computer tomography (SPECT) imaging. Results: We developed a workflow for predicting liver function from routine CT imaging data using convolutional neural networks. We also evaluated in how far transfer learning and data augmentation can help to solve remaining manual data pre-processing steps and implemented the developed workflow in a clinical routine setting. Conclusion: We propose a robust semiautomatic end-to-end classification workflow for abdominal CT scans for the prediction of liver function based on a deep convolutional neural network model that shows reliable and accurate results even with limited computational resources.

Journal

Current Directions in Biomedical Engineering – de Gruyter

Published: Aug 1, 2021

Keywords: Machine learning; artificial neural network; convolutional neural network; hepatobiliary scintigraphy; liver function

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