Digital image analysis of ultrasound images using machine learning to diagnose pediatric nonalcoholic fatty liver disease

Das, Amit; Connell, Mary J.; Khetarpal, Shailesh

doi:10.1016/j.clinimag.2021.02.038

Cited by 18 publications

(8 citation statements)

References 18 publications

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“…Features such as maximum probability [ 38 , 52 , 53 , 59 , 63 , 78 , 80 ], uniformity [ 38 , 52 , 53 , 59 , 60 , 63 , 70 , 80 ], entropy [ 37 , 38 , 49 , 52 , 59 , 63 , 66 , 70 , 71 , 73 , 74 , 76 , 80 , 82 ], contrast [ 38 , 49 , 52 , 59 , 63 , 65 , 66 , 70 , 71 , 73 , 74 , 76 , 78 , 80 ], run-length uniformity [ 38 , 52 , 70 , 80 ], attenuation [ 43 , 73 , ...…”

Section: Resultsmentioning

confidence: 99%

“…Even though not all studies provided information about the methods used to manage data, most studies used cross-validation. Twelve studies implemented 10-fold cross-validation on the testing and training datasets [ 36 , 38 , 39 , 42 , 43 , 52 , 58 , 60 , 62 , 67 , 68 , 70 ], four studies used five-fold cross-validation [ 59 , 63 , 66 , 69 ], one study used four-fold cross-validation [ 77 ], two studies used three-fold cross-validation [ 41 , 54 ], one study used two-fold cross-validation [ 40 ], and one study used one-fold cross-validation [ 74 ]. In addition, four studies mentioned that a leave-one-out cross-validation (LOOCV) method was used [ 55 , 56 , 57 , 79 ].…”

Section: Resultsmentioning

confidence: 99%

“…Two studies used ensembled models. One study used three different classifiers: SVM, multi-layered perceptron neural net, and extreme gradient boost [ 70 ]. The other study used two classifiers: LR and SVM [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

AlShagathrh

Househ

2022

Bioengineering

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Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

AlShagathrh

Househ

2022

Bioengineering

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“…In addition, as medical data are surging, various types of data are included in electronic health records (EHRs). ML has an unparalleled advantage in the analysis of unstructured data (Barber et al, 2021 ; De Silva et al, 2021 ), pictures (Das et al, 2021 ) and other data. However, numerous ML studies have shown that several problems related to model constructions still exist.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

et al. 2022

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Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models. Background As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Design Systematic review. Methods Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Results Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation. Conclusions ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. Relevance to Clinical Practice This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical practice.

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“…Then, our purpose was to develop a simple web app which permits to perform the diagnosis of absence of NALFD with high accuracy to reduce waiting list and costs for the National Health System, as. most studies on NAFLD diagnosis are based on images or laboratory parameters that are not always available 26 , 33 .…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a neural network for NAFLD diagnosis

Sorino

Campanella

Mirizzi

et al. 2021

Sci Rep

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Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases.

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Digital image analysis of ultrasound images using machine learning to diagnose pediatric nonalcoholic fatty liver disease

Cited by 18 publications

References 18 publications

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

Development and validation of a neural network for NAFLD diagnosis

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