Key result
A deep learning algorithm objectively quantified liver steatosis from ultrasound images, which significantly correlated with body weight changes (R2 = 0.62, p < 0.001).
Why the study?
A deep learning algorithm to quantify steatosis from ultrasound images may convert a subjective diagnosis into objective quantification, warranting evaluation in patients undergoing weight changes.
Does a deep learning algorithm applied to ultrasound images objectively quantify liver steatosis in correlation with weight changes?
Observational (n=74)
Does a deep learning algorithm applied to ultrasound images objectively quantify liver steatosis in correlation with weight changes?
A deep learning algorithm can objectively quantify liver steatosis from ultrasound images, demonstrating significant correlation with body weight changes.
May aid objective steatosis monitoring during weight change; leaves open prospective validation before clinical use.
Introduction: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. Materials and Methods: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3–5 images in each group were used for the results and correlated against weight changes. Results: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). Conclusions: The best scanning conditions are 3–5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.
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Harrison et al. (2023) conducted an observational in Liver steatosis in patients undergoing weight changes (n=74). Deep learning algorithm for steatosis quantification on ultrasound images was evaluated on Correlation between body weight and steatosis scores (R2 = 0.62, p=<0.001). A deep learning algorithm objectively quantified liver steatosis from ultrasound images, which significantly correlated with body weight changes (R2 = 0.62, p < 0.001).
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