Sonic Incytes Medical Corp., Vancouver, BC, Canada.
Sonic Incytes Medical Corp., Vancouver, BC, Canada.
Ultrasound Med Biol. 2024 Dec;50(12):1812-1820. doi: 10.1016/j.ultrasmedbio.2024.08.002. Epub 2024 Sep 6.
As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this study is to test the performance of a new software tool implemented in Velacur (Sonic Incytes), a liver stiffness and ultrasound attenuation measurement device, on patients with MASLD. This tool employs a deep learning-based method to detect and segment shear waves in the liver tissue for subsequent analysis to improve tissue characterization for patient diagnosis.
This new tool consists of a deep learning based algorithm, which was trained on 15,045 expert-segmented images from 103 patients, using a U-Net architecture. The algorithm was then tested on 4429 images from 36 volunteers and patients with MASLD. Test subjects were scanned at different clinics with different Velacur operators. Evaluation was performed on both individual images (image based) and averaged across all images collected from a patient (patient based). Ground truth was defined by expert segmentation of the shear waves within each image. For evaluation, sensitivity and specificity for correct wave detection in the image were calculated. For those images containing waves, the Dice coefficient was calculated. A prototype of the software tool was also implemented on Velacur and assessed by operators in real world settings.
The wave detection algorithm had a sensitivity of 81% and a specificity of 84%, with a Dice coefficient of 0.74 and 0.75 for image based and patient-based averages respectively. The implementation of this software tool as an overlay on the B-Mode ultrasound resulted in improved exam quality collected by operators.
The shear wave algorithm performed well on a test set of volunteers and patients with metabolic dysfunction-associated steatotic liver disease. The addition of this software tool, implemented on the Velacur system, improved the quality of the liver assessments performed in a real world, point of care setting.
随着代谢功能相关脂肪性肝病（MASLD）在全球范围内的日益流行，迫切需要开发更准确的技术，以便在即时护理环境中轻松评估肝脏。本研究旨在测试 Velacur（Sonic Incytes）中实施的新软件工具在 MASLD 患者中的性能。该工具采用基于深度学习的方法来检测和分割肝组织中的剪切波，以便进行后续分析，从而改善患者诊断的组织特征。
该新工具由基于深度学习的算法组成，该算法是使用 U-Net 架构在 103 名患者的 15,045 张专家分割图像上进行训练的。然后，该算法在 36 名志愿者和 MASLD 患者的 4429 张图像上进行了测试。测试对象在不同的诊所由不同的 Velacur 操作人员进行扫描。评估是在单个图像（基于图像）和从患者收集的所有图像的平均值（基于患者）上进行的。真实情况是通过对每张图像中的剪切波进行专家分割来定义的。对于评估，计算了图像中正确检测到的波的灵敏度和特异性。对于包含波的图像，计算了 Dice 系数。还在 Velacur 上实现了软件工具的原型，并在实际环境中由操作人员进行了评估。
波检测算法的灵敏度为 81%，特异性为 84%，基于图像和基于患者的平均值的 Dice 系数分别为 0.74 和 0.75。作为 B 模式超声的覆盖层实施此软件工具可提高操作人员收集的检查质量。
剪切波算法在志愿者和代谢功能相关脂肪性肝病患者的测试集中表现良好。在 Velacur 系统上实现此软件工具可提高即时护理环境中的肝脏评估质量。
