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Article summary:
1. Deep learning algorithms can accurately identify nonalcoholic fatty liver disease and quantify hepatic fat fraction using raw radiofrequency ultrasound data.
2. The proposed deep learning approach is robust to changes in system settings, including transmit focal range and time gain compensation.
3. MRI proton density fat fraction was used as the reference standard for noninvasive quantification of hepatic steatosis.
Article analysis:
The article provides a detailed overview of the use of one-dimensional convolutional neural networks (CNNs) for noninvasive diagnosis of nonalcoholic fatty liver disease (NAFLD) and quantification of liver fat with radiofrequency ultrasound data. The authors provide evidence that their proposed deep learning approach is accurate and robust to changes in system settings, including transmit focal range and time gain compensation. Furthermore, they use MRI proton density fat fraction as the reference standard for noninvasive quantification of hepatic steatosis, which has been shown to be an accurate method for this purpose.
The article appears to be reliable overall, however there are some potential biases that should be noted. First, the study only included 204 participants, which may not be sufficient to draw definitive conclusions about the accuracy of the proposed deep learning approach. Additionally, it is unclear if any potential risks associated with using this technique were discussed or considered by the authors. Finally, while the authors do provide evidence that their proposed approach is accurate and robust to changes in system settings, they do not explore any potential counterarguments or alternative approaches that could also be used for NAFLD diagnosis and quantification of liver fat with radiofrequency ultrasound data.