Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease.
Aylin TahmasebiShuo WangCorinne E WessnerTrang VuJi-Bin LiuFlemming ForsbergJesse CivanFlavius F GuglielmoJohn R EisenbreyPublished in: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine (2023)
An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.
Keyphrases
- machine learning
- end stage renal disease
- magnetic resonance imaging
- ejection fraction
- newly diagnosed
- chronic kidney disease
- artificial intelligence
- prognostic factors
- peritoneal dialysis
- clinical trial
- study protocol
- randomized controlled trial
- ultrasound guided
- computed tomography
- patient reported outcomes
- phase iii
- quantum dots
- open label
- patient reported