To develop a ConvNeXt deep learning model based on hepatic ultrasound radiofrequency (RF) signals and the ultrasound attenuation parameter (UAP), using liver biopsy as the reference standard, and to evaluate its diagnostic performance for significant hepatic steatosis in liver diseases.

A total of 1222 patients with liver diseases who underwent evaluation at Beijing Ditan Hospital, Capital Medical University between January 2020 and February 2025 were retrospectively analyzed. RF signals, UAP measurements, and liver biopsy data were collected. The dataset was divided into a training set (n=851), a validation set (n=192), and a test set (n=179). RF signal acquisition and UAP measurement were performed prior to liver biopsy, and corresponding histopathological findings were obtained. Hepatic steatosis graded as F0–F1 was defined as negative (label 0), while ≥F2 was defined as positive (label 1). To construct an artificial intelligence model for assessing significant hepatic steatosis, each column of ultrasound RF signal data was normalized using Z-score standardization. ConvNeXt-Tiny was used as the backbone network, initialized with pretrained weights from the ImageNet dataset, and the mean of the first-layer three-channel weights was used to initialize single-channel weights. UAP was incorporated as a scalar feature and concatenated with image features extracted by the ConvNeXt backbone at the final linear layer. The model prediction score was generated through a fully connected layer followed by a Sigmoid function, and final classification was determined using a threshold. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of the ConvNeXt model and UAP for detecting significant hepatic steatosis, and their performance was compared.

In the test set, the area under the ROC curve (AUC) of the ConvNeXt model for diagnosing significant hepatic steatosis was 0.838 (95% confidence interval [CI]: 0.769–0.906). Using the cutoff value (0.225) determined in the validation set, the confusion matrix [[True Negative (TN) False Positive (FP) ][False Negative (FN)True Positive (TP)]] in the test set was [[108 36][6 29]], yielding a sensitivity of 82.86% and specificity of 75.00%. In addition, based on the ROC curve in the test set, the optimal cutoff value was 0.272, at which the confusion matrix [[TN FP ][FN TP]] was [[115 29][6 29]], yielding a sensitivity of 82.86% and specificity of 79.86%. For UAP, the AUC for diagnosing significant hepatic steatosis was 0.802 (95%CI: 0.723–0.881), with a threshold of 269 dB/m, and the sensitivity and specificity were 74.29% and 72.22%, respectively.

The ConvNeXt deep learning model based on RF signals and UAP demonstrates favorable performance in identifying significant hepatic steatosis (≥F2) in liver diseases, and may serve as a useful tool for clinical screening and follow-up of patients with steatotic liver disease.