Background: The accurate delineation of ablation zones (AZs) is crucial for assessing radiofrequency ablation (RFA) therapy’s efficacy. Manual measurement, the current standard, is subject to variability and potential inaccuracies. Aim: This study aims to assess the effectiveness of Artificial Intelligence (AI) in automating AZ measurements in ultrasound images and compare its accuracy with manual measurements in ultrasound images. Methods: An in vitro study was conducted using chicken breast and liver samples subjected to bipolar RFA. Ultrasound images were captured every 15 s, with the AI model Mask2Former trained for AZ segmentation. The measurements were compared across all methods, focusing on short-axis (SA) metrics. Results: We performed 308 RFA procedures, generating 7275 ultrasound images across liver and chicken breast tissues. Manual and AI measurement comparisons for ablation zone diameters revealed no significant differences, with correlation coefficients exceeding 0.96 in both tissues (p< 0.001). Bland–Altman plots and a Deming regression analysis demonstrated a very close alignment between AI predictions and manual measurements, with the average difference between the two methods being −0.259 and −0.243 mm, for bovine liver and chicken breast tissue, respectively. Conclusion: The study validates the Mask2Former model as a promising tool for automating AZ measurement in RFA research, offering a significant step towards reducing manual measurement variability.
背景：准确描绘消融区（AZs）对于评估射频消融（RFA）治疗效果至关重要。目前标准的手动测量方法存在变异性和潜在不准确性。目的：本研究旨在评估人工智能（AI）在超声图像中自动测量消融区的有效性，并将其准确性与超声图像中的手动测量进行比较。方法：采用鸡胸肉和肝脏样本进行体外研究，实施双极射频消融。每15秒采集一次超声图像，并训练Mask2Former AI模型进行消融区分割。比较所有测量方法，重点关注短轴（SA）指标。结果：我们进行了308次射频消融操作，在肝脏和鸡胸肉组织中生成了7275张超声图像。消融区直径的手动与AI测量比较显示无显著差异，两种组织的相关系数均超过0.96（p<0.001）。Bland-Altman图和Deming回归分析表明AI预测与手动测量高度一致，两种方法的平均差异在牛肝组织和鸡胸肉组织中分别为-0.259毫米和-0.243毫米。结论：本研究验证了Mask2Former模型作为射频消融研究中自动测量消融区的有效工具，为减少手动测量变异性迈出了重要一步。
Application of Deep Learning for Real-Time Ablation Zone Measurement in Ultrasound Imaging
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