用于血管医学血管造影计算机视觉的智能手机摄像头

Y. Rusinovich, V. Rusinovich, Markus Doss
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摘要

目的:本研究旨在开发一种用于血管造影分类的 TensorFlow Lite 算法,并将其部署到基本的移动智能手机设备上,从而验证为血管医学创建一个全面的端到端移动计算机视觉应用的概念验证。材料与方法:在获得当地伦理委员会的伦理批准后,我们收集了机构和开源的下肢外周血管造影。血管造影由一位在血管外科领域有 10 多年经验的研究人员进行标注。标注工作包括根据解剖模式将血管造影分为全球肢体解剖分期系统(GLASS)。该模型使用开源的 TensorFlow 框架开发,用于一般图像分类,并作为安卓应用程序部署。结果该模型使用了 700 张血管造影,按股骨关节 GLASS 疾病(fp)类别分布如下:fp0 - 187 张图像,fp1 - 136 张图像,fp2 - 128 张图像,fp3 - 97 张图像,fp4 - 152 张图像。参考数据集包括 372 张非血管造影图像(not_angio)。因此,整个模型包括 1,072 张图像。经过培训和部署后,该模型的性能如下:平均准确率为 0.72。每个类别的最佳自我报告准确率为 fp0 0.72、fp4 0.83 和 not_angio 1.0。结论我们发现,智能手机摄像头可以通过端到端应用程序用于血管造影计算机视觉,每个医疗保健专业人员都可以使用。然而,该模型的预测能力有限,需要改进。开发强大的血管造影计算机视觉智能手机应用程序应包含上传功能,通过人机头对头比较进行验证,可能包括分割,并采用前瞻性设计,明确同意在开发人工智能模型时使用收集的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smartphone Camera for Angiographic Computer Vision in Vascular Medicine
Aim: This study aimed to develop a TensorFlow Lite algorithm for angiography classification and to deploy it on a basic mobile smartphone device, thereby verifying the proof of concept for creating a comprehensive end-to-end mobile computer vision application for vascular medicine. Materials and Methods: After ethical approval by the local ethics committee, we collected institutional and open source peripheral angiograms of lower limbs. The angiograms were labeled by a researcher with more than 10 years of experience in vascular surgery. The labeling included dividing the angiograms according to their anatomical pattern into the Global Limb Anatomic Staging System (GLASS). The model was developed using the open-source TensorFlow framework for general image classification and deployed as an Android application. Results: The model utilized 700 angiograms, distributed as follows within the femoropoliteal GLASS disease (fp) categories: fp0 – 187 images, fp1 – 136 images, fp2 – 128 images, fp3 – 97 images, fp4 – 152 images. The reference dataset included 372 non-angiographic images (not_angio). Consequently, the entire model included 1,072 images. After training and deployment, the model demonstrated the following performance: a mean accuracy of 0.72. The best self-reported accuracy per class was for fp0 0.72, fp4 0.83 and not_angio 1.0 classes. Conclusion: We discovered that a smartphone camera could be utilized for angiographic computer vision through end-to-end applications accessible to every healthcare professional. However, the predictive abilities of the model are limited and require improvement. The development of a robust angiographic computer vision smartphone application should incorporate an upload function, undergo validation through head-to-head human-machine comparisons, potentially include segmentation, and feature a prospective design with explicit consent for using collected data in the development of AI models.
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