Application of Deep Learning for Real-Time Ablation Zone Measurement in Ultrasound Imaging

Simple Summary The manual measurement of ablation zones (AZs) in radiofrequency ablation (RFA) therapy is prone to inaccuracies, highlighting the need for automated methods. Our study investigated the effectiveness of an Artificial Intelligence (AI) model, Mask2Former, in automating AZ measurements from ultrasound images, comparing its performance against manual techniques. Conducted on chicken breast and liver samples, the study found the AI model to achieve high accuracy, particularly in chicken breast tissue, with no significant difference in measurements between AI and manual methods. These results suggest that the Mask2Former model can significantly reduce variability in manual measurements, marking a step forward in the automation of AZ measurement in RFA therapy research and potentially improving the precision of treatment assessments. 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. 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.