Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

Wong, Grace Lai Hung; Yuen, Pong C.; Andy, J.; Chan, Anthony W.H.; Leung, Howan; Wong, Vincent W. S.

doi:10.1111/jgh.15385

Cited by 46 publications

(32 citation statements)

References 32 publications

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“…These machine learning models allow researchers to include any number of clinical, laboratory, and demographic features to detect hidden patterns for disease classification. While an extensive revision of literature is beyond the scope of our current study, a recent publication [ 22 ] reviewed and discussed advantages and disadvantages of the most frequently used algorithms and other aspects of data (both EHR and imaging). Sowa et al [ 23 ] included EHR of 126 patients to develop a final model with an accuracy of 0.79.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

García-Carretero

Holgado-Cuadrado

Barquero-Pérez

2021

Entropy

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Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.

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Section: Introductionmentioning

confidence: 99%

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

García-Carretero

Holgado-Cuadrado

Barquero-Pérez

2021

Entropy

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“…In this study, for each patient, 28 fluence maps were measured, and there were 1120 fluence maps in total. After classification, there were less maps corresponding to each label, the size of the data set (40 patients) used as CNN input may have been insufficient for the network, as CNNs generally require large-scale datasets for training (43,44); as a result, the advantage of using the CNNs was not obvious. In this study, a method to identify position errors was developed by analyzing the EPID fluence maps, which can be combined with CBCT in clinical treatment.…”

Section: Discussionmentioning

confidence: 99%

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

et al. 2021

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PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.

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“…Besides, Wong comprehensively evaluated non-alcoholic fatty liver disease severity based on clinical information, including electronic health records, liver biopsies, and liver images [ 110 ]. In future, AI health assessment of patients may not be limited to cross-sectional studies.…”

Section: Overviewmentioning

confidence: 99%

A systematic review on application of deep learning in digestive system image processing

2021

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Artificial intelligence in prediction of non‐alcoholic fatty liver disease and fibrosis

Cited by 46 publications

References 32 publications

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest

Analysis of EPID Transmission Fluence Maps Using Machine Learning Models and CNN for Identifying Position Errors in the Treatment of GO Patients

A systematic review on application of deep learning in digestive system image processing

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