A comparative study of CT-based volumetric assessment methods for total lung capacity with the development of an adjustment factor: incorporating VR imaging for improved accuracy

Physiological methods for measuring total lung capacity (TLC), including body-box plethysmography (BBP), are costly and require specialized expertise. Computed tomography (CT)-based TLC assessment is essential in clinical practice for candidates of lung transplantation and those unable to undergo st...

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Bibliographic Details
Published in:Virtual reality : the journal of the Virtual Reality Society 2024-03, Vol.28 (1), p.2, Article 2
Main Authors: Yogev, David, Chatarji, Sumit, Carl, Lawerence, Levy, Liran, Goldberg, Tomer, Feinberg, Omer, Illouz, Shay, Spector, Robert, Parmet, Yisrael, Tejman-Yarden, Shai
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Comparative studies
Computed tomography
Computer Graphics
Computer Science
Estimates
Evaluation
Image Processing and Computer Vision
Lungs
Measurement methods
Physiological effects
Plethysmography
S.I. : New Trends on Immersive Healthcare
Transplantation
User Interfaces and Human Computer Interaction
Virtual reality
Volumetric analysis
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Summary:Physiological methods for measuring total lung capacity (TLC), including body-box plethysmography (BBP), are costly and require specialized expertise. Computed tomography (CT)-based TLC assessment is essential in clinical practice for candidates of lung transplantation and those unable to undergo standard lung function testing. While CT-based algorithms were studied to estimate TLC, their accuracy should be further evaluated. This study aimed to compare the BBP measurement of TLC (TLCpleth) with three CT-based methods for measuring TLC, one of them is an innovative virtual reality (VR)-based method. Additionally, we aimed to develop an adjustment factor that will allow a new, non-invasive, cost-effective estimation of the TLCpleth. TLC was calculated for 24 adult patients using three different CT-based volumetric assessment methods: an older region-growing algorithm (TLCrg), a more recent convolutional neural network-based algorithm (TLCcnn), and a VR-based method (TLCvr). Agreement between each method and TLCpleth was evaluated, and an adjustment factor was developed using linear regression. The correlation between the three CT-based methods and TLCpleth ranged from 0.91 to 0.92 ( p  
ISSN:1359-4338
1434-9957
DOI:10.1007/s10055-023-00892-y