Scholarlink

Purpose <p>This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventional B-mode ultrasound images, with Fibroscan-derived measurement as reference standard.</p> Methods <p>We utilized a multi-source approach comprising three distinct ultrasound datasets of patients with MASLD: a private clinical dataset (DS1, <i>n</i> = 111 patients, 1131 images), and two subsets from the public BEHSOF repository—Behbood Clinic subset (DS2, <i>n</i> = 95 patients, 1328 images) for training/validation, and Taleghani Hospital subset (DS3, <i>n</i> = 18 patients, 185 images) for external testing. Additionally, we validated on a temporally independent test set (DS4, <i>n</i> = 23 patients, 155 images). Deep learning models, including EfficientNet-B4 and Vision Transformers, were trained to classify steatosis (S0-S1 vs. S2-S3) and fibrosis (F0-F1 vs. F2-F4), respectively, using 5-fold cross-validation. Performance was assessed and compared with expert radiologists.</p> Results <p>For steatosis classification, our models achieved an AUROC of 0.83 (95% CI: 0.78–0.88) in cross-validation and 0.70 (95% CI: 0.43–0.93) on the external test set. For fibrosis, AUROC reached 0.86 (95% CI: 0.81–0.91) in cross-validation and 0.88 (95% CI: 0.71–1.00) on the external test set. On the temporally independent test set, performance was better with AUROCs of 0.82 (95% CI: 0.66–0.98) for steatosis and 0.91 (95% CI: 0.77–1.00) for fibrosis. The AI models consistently outperformed expert radiologists, whose interobserver agreement was moderate (κ = 0.58–0.65).</p> Conclusion <p>This study demonstrates that deep learning models can accurately identify steatosis and fibrosis from ultrasound images across diverse populations and clinical settings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset

  • 期刊论文

  • 发表日期：

    2025年11月24日

查看全文
  • Kartik Bose,
  • Priya Mudgil,
  • Pankaj Gupta,
  • Samonee Ralmilay,
  • Niharika Dutta,
  • Ajay Gulati,
  • Naveen Kalra,
  • Madhumita Premkumar,
  • Sunil Taneja,
  • Nipun Verma,
  • Arka De,
  • Ajay Duseja

摘要

Purpose

This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventional B-mode ultrasound images, with Fibroscan-derived measurement as reference standard.

Methods

We utilized a multi-source approach comprising three distinct ultrasound datasets of patients with MASLD: a private clinical dataset (DS1, n = 111 patients, 1131 images), and two subsets from the public BEHSOF repository—Behbood Clinic subset (DS2, n = 95 patients, 1328 images) for training/validation, and Taleghani Hospital subset (DS3, n = 18 patients, 185 images) for external testing. Additionally, we validated on a temporally independent test set (DS4, n = 23 patients, 155 images). Deep learning models, including EfficientNet-B4 and Vision Transformers, were trained to classify steatosis (S0-S1 vs. S2-S3) and fibrosis (F0-F1 vs. F2-F4), respectively, using 5-fold cross-validation. Performance was assessed and compared with expert radiologists.

Results

For steatosis classification, our models achieved an AUROC of 0.83 (95% CI: 0.78–0.88) in cross-validation and 0.70 (95% CI: 0.43–0.93) on the external test set. For fibrosis, AUROC reached 0.86 (95% CI: 0.81–0.91) in cross-validation and 0.88 (95% CI: 0.71–1.00) on the external test set. On the temporally independent test set, performance was better with AUROCs of 0.82 (95% CI: 0.66–0.98) for steatosis and 0.91 (95% CI: 0.77–1.00) for fibrosis. The AI models consistently outperformed expert radiologists, whose interobserver agreement was moderate (κ = 0.58–0.65).

Conclusion

This study demonstrates that deep learning models can accurately identify steatosis and fibrosis from ultrasound images across diverse populations and clinical settings.