PDF(Pubmed)
Abstract：
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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
摘要：
背景：消融区（AZs）的准确描绘对于评估射频消融（RFA）治疗的疗效至关重要。手动测量,现行标准,受到可变性和潜在的不准确性的影响。
目的：本研究旨在评估人工智能（AI）在超声图像中自动进行AZ测量的有效性，并将其准确性与超声图像中的手动测量进行比较。
方法:使用经受双极RFA的鸡胸肉和肝脏样品进行体外研究。每15秒拍摄一次超声图像，使用AI模型Mask2Former进行AZ分割训练。对所有方法的测量结果进行了比较，关注短轴(SA)指标。
结果：我们执行了308RFA程序，在肝脏和鸡胸组织中生成7275张超声图像。消融区直径的手动和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预测与手动测量之间非常接近。两种方法之间的平均差为-0.259和-0.243mm，用于牛肝和鸡胸组织，分别。
结论：该研究验证了Mask2Former模型是RFA研究中自动化AZ测量的有前途的工具，为减少手动测量可变性提供了重要的一步。
目的：本研究旨在评估人工智能（AI）在超声图像中自动进行AZ测量的有效性，并将其准确性与超声图像中的手动测量进行比较。
方法:使用经受双极RFA的鸡胸肉和肝脏样品进行体外研究。每15秒拍摄一次超声图像，使用AI模型Mask2Former进行AZ分割训练。对所有方法的测量结果进行了比较，关注短轴(SA)指标。
结果：我们执行了308RFA程序，在肝脏和鸡胸组织中生成7275张超声图像。消融区直径的手动和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预测与手动测量之间非常接近。两种方法之间的平均差为-0.259和-0.243mm，用于牛肝和鸡胸组织，分别。
结论：该研究验证了Mask2Former模型是RFA研究中自动化AZ测量的有前途的工具，为减少手动测量可变性提供了重要的一步。
