Efficacy of Ultrasound for the Detection of Possible Fatty Liver Disease in Children.
Sarah B LowryShelly JosephKevin J PsoterEmily DunnSana MansoorS Kathryn SmithWikrom W KarnsakulGihan NaguibKenneth NgAnn O ScheimannPublished in: Diagnostics (Basel, Switzerland) (2024)
Pediatric MASLD (previously referred to as NAFLD) incidence has continued to rise along with the obesity pandemic. Pediatric MASLD increases the risk of liver fibrosis and cirrhosis in adulthood. Early detection and intervention can prevent and reduce complications. Liver biopsy remains the gold standard for diagnosis, although imaging modalities are increasingly being used. We performed a retrospective study of 202 children seen in a pediatric gastroenterology clinic with a complaint of abdominal pain, elevated liver enzymes or MASLD, or a combination of the three to evaluate screening methods for MASLD. A total of 134 of the 202 patients included in the study underwent laboratory testing and abdominal ultrasound. Ultrasound images were reviewed with attention to liver size and echotexture by a fellowship-trained pediatric radiologist for liver size and echotexture. Overall, 76.2% of the initial radiology reports correctly identified hepatomegaly based on age and 75.4% of the initial radiology reports correctly described hepatic echogenicity that was consistent with increased hepatic fat deposition. Use of screening ultrasound in concert with other clinical evaluations can be helpful to identify children at risk of MASLD. Utilizing ranges for liver span according to age can help to diagnose hepatomegaly, and understanding how to identify hepatic echogenicity is important for identifying possible hepatic steatosis.
Keyphrases
- magnetic resonance imaging
- young adults
- liver fibrosis
- ultrasound guided
- randomized controlled trial
- end stage renal disease
- metabolic syndrome
- abdominal pain
- emergency department
- sars cov
- primary care
- type diabetes
- artificial intelligence
- coronavirus disease
- ejection fraction
- peritoneal dialysis
- deep learning
- newly diagnosed
- optical coherence tomography
- body mass index
- body composition
- convolutional neural network
- patient reported outcomes