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Everything you need to know about Computed Tomography (CT) & CT Scanning

Liver: Artificial Intelligence Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Liver ❯ Artificial Intelligence

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  • OBJECTIVE To examine whether deep learning recurrent neural network(RNN )models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC).
    CONCLUSIONS AND RELEVANCE In this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
  • “This study has limitations related to lack of external validation and the computational cost of running the analyses. To reduce computational cost, we only performed optimal search for some of the hyperparameters. Even so, the RNN model outperformed conventional LR models. Health care systems are now investing in the infrastructure to construct some of these complex models. For example, the VHA has collaborated with Google’s DeepMind to develop an RNN model for predicting acute kidney injury using national VHA data. All deep learning neural network models including ours, have limited interpretability due to their black-box nature, which may limit acceptability by clinicians. However, recent innovations allow for interpretable deep learning models by determining the proportion of the prediction attributed to each feature.”
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
  • "In this study, we demonstrated that RNN models that use raw longitudinal EHR data are superior to conventional LR models in estimating the risk of HCC in patients with HCV-related cirrhosis. RNN models such as ours could have multiple applications in clinical practice, provided they can be incorporated within EHR software systems.”
    Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis
    George N. Ioannou et al.
    JAMA Network Open. 2020;3(9):e2015626. Sept 2020
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