Changes in ventilation and perfusion following lower lobe endoscopic lung volume reduction (ELVR) with endobronchial valves in severe COPD

The Past, Present, and Future Role of Artificial Intelligence in Ventilation/Perfusion Scintigraphy: A Systematic Review

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.

Interventional bronchoscopy for chronic obstructive pulmonary disease: more than a pipe dream

Endoscopic lung volume reduction (ELVR) is recognised in both national and international expert guidelines as one of the few additive treatments to benefit patients with advanced chronic obstructive pulmonary disease (COPD) who are otherwise receiving optimal medical and supportive care. Despite these recommendations and a growing evidence base, these procedures are not widely offered across Australia and New Zealand, and general practitioner and physician awareness of this therapy can be improved. ELVR aims to mitigate the impact of hyperinflation and gas trapping on dyspnoea and exercise intolerance in COPD. Effective ELVR is of proven benefit in improving symptoms, quality of life, lung function and survival. Several endoscopic techniques to achieve ELVR have been developed, with endobronchial valve placement to collapse a single lobe being the most widely studied and commonly practised. This review describes the physiological rationale underpinning lung volume reduction, highlights the challenges of patient selection, and provides an overview of the evidence for current and investigational endoscopic interventions for COPD.