Department of Physics, Carleton University, Ottawa, Ontario, Canada.
Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada.
Semin Nucl Med. 2023 Nov;53(6):752-765. doi: 10.1053/j.semnuclmed.2023.03.002. Epub 2023 Apr 18.
Ventilation-perfusion (V/Q) lung scans constitute one of the oldest nuclear medicine procedures, remain one of the few studies performed in the acute setting, and are amongst the few performed in the emergency setting. V/Q studies have witnessed a long fluctuation in adoption rates in parallel to continuous advances in image processing and computer vision techniques. This review provides an overview on the status of artificial intelligence (AI) in V/Q scintigraphy. To clearly assess the past, current, and future role of AI in V/Q scans, we conducted a systematic Ovid MEDLINE(R) literature search from 1946 to August 5, 2022 in addition to a manual search. The literature was reviewed and summarized in terms of methodologies and results for the various applications of AI to V/Q scans. The PRISMA guidelines were followed. Thirty-one publications fulfilled our search criteria and were grouped into two distinct categories: (1) disease diagnosis/detection (N = 22, 71.0%) and (2) cross-modality image translation into V/Q images (N = 9, 29.0%). Studies on disease diagnosis and detection relied heavily on shallow artificial neural networks for acute pulmonary embolism (PE) diagnosis and were primarily published between the mid-1990s and early 2000s. Recent applications almost exclusively regard image translation tasks from CT to ventilation or perfusion images with modern algorithms, such as convolutional neural networks, and were published between 2019 and 2022. AI research in V/Q scintigraphy for acute PE diagnosis in the mid-90s to early 2000s yielded promising results but has since been largely neglected and thus have yet to benefit from today's state-of-the art machine-learning techniques, such as deep neural networks. Recently, the main application of AI for V/Q has shifted towards generating synthetic ventilation and perfusion images from CT. There is therefore considerable potential to expand and modernize the use of real V/Q studies with state-of-the-art deep learning approaches, especially for workflow optimization and PE detection at both acute and chronic stages. We discuss future challenges and potential directions to compensate for the lag in this domain and enhance the value of this traditional nuclear medicine scan.
通气-灌注（V/Q）肺扫描是核医学中最古老的程序之一，仍然是在急性情况下进行的为数不多的研究之一，也是在紧急情况下进行的为数不多的研究之一。V/Q 研究的采用率与图像处理和计算机视觉技术的不断进步密切相关，经历了长期的波动。本综述提供了人工智能（AI）在 V/Q 闪烁中的应用概述。为了清楚地评估 AI 在 V/Q 扫描中的过去、现在和未来作用，我们除了手动搜索外，还从 1946 年至 2022 年 8 月 5 日，在 Ovid MEDLINE(R)文献库中进行了系统的文献检索。根据 AI 应用于 V/Q 扫描的各种方法和结果，对文献进行了回顾和总结。本研究遵循 PRISMA 指南。31 篇文献符合我们的检索标准，分为两类：（1）疾病诊断/检测（N=22，71.0%）和（2）跨模态图像转换为 V/Q 图像（N=9，29.0%）。疾病诊断和检测研究主要依赖于浅层人工神经网络进行急性肺栓塞（PE）诊断，主要发表于 20 世纪 90 年代中期至 21 世纪初。最近的应用几乎完全是关于使用现代算法（如卷积神经网络）将 CT 图像转换为通气或灌注图像的图像翻译任务，发表于 2019 年至 2022 年期间。20 世纪 90 年代至 21 世纪初，V/Q 闪烁用于急性 PE 诊断的 AI 研究取得了有希望的结果，但此后很大程度上被忽视，因此尚未受益于当今最先进的机器学习技术，如深度神经网络。最近，AI 用于 V/Q 的主要应用是从 CT 生成合成通气和灌注图像。因此，通过使用最先进的深度学习方法扩展和现代化真实 V/Q 研究具有很大的潜力，特别是对于急性和慢性阶段的工作流程优化和 PE 检测。我们讨论了未来的挑战和潜在方向，以弥补该领域的滞后并提高这种传统核医学扫描的价值。
