Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.
Yang YangJing LiuChangxuan SunYuwei ShiJulianna C HsingAya KamyaCody Auston KellerNeha AntilDaniel RubinHongxia WangHaochao YingXueyin ZhaoYi-Hsuan WuMindie NguyenYing LuFei YangPinton HuangAnn W HsingJian WuShan-Kuan ZhuPublished in: European radiology (2023)
• Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.
Keyphrases
- dual energy
- deep learning
- artificial intelligence
- computed tomography
- skeletal muscle
- body mass index
- image quality
- convolutional neural network
- type diabetes
- machine learning
- contrast enhanced
- adipose tissue
- cardiovascular disease
- insulin resistance
- fatty acid
- risk factors
- magnetic resonance imaging
- physical activity
- glycemic control
- real time pcr
- clinical practice
- mass spectrometry
- bone mineral density
- sensitive detection