Ultrasound‐Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease

Abstract

Objectives-Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD.Methods-One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated.Results-A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%.Conclusions-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.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease

“…Liver ultrasound scans are a standard non-invasive diagnostic tool for chronic liver diseases, including MASLD, but are influenced by examiner subjectivity and exhibit reduced sensitivity when the liver contains less than 20–30% fat [43] . Limited studies on AI's application for predicting and classifying MASLD patients indicate promising results with excellent AUROC scores [44] , [45] , [46] . Additionally, ML algorithms integrated with transient elastography (TE) have been employed to predict liver fibrosis and MASLD in large clinical trial/cohort studies [47] , [48] , [49] .…”

Pathophysiology of Cystic Fibrosis Liver Disease

“…One way to overcome this is through implementation of AI for analysis of routine ultrasound imaging in CF which could provide a way to improve standardization and decrease heterogeneity in the interpretation of results. Indeed, the application of AI for ultrasound images of the liver is under intense study at this time and was recently demonstrated to be excellent for detection of steatotic liver disease 59 …”

“…Indeed, the application of AI for ultrasound images of the liver is under intense study at this time and was recently demonstrated to be excellent for detection of steatotic liver disease. 59…”

The Prediction and Treatment of Bleeding Esophageal Varices in the Artificial Intelligence Era: A Review

“…AI has been commonly used in clinical practice in many other ways nowadays. For instance, in a recent prospective study conducted in the United States in 2023, an ultrasound-based machine learning model using AI was used to detect metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease {NAFLD}) [ 14 , 15 ]. This machine learning device using ultrasound and AI reported high positive predictive value and specificity for detecting MASLD in high-risk patients [ 14 ].…”