Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing

AJR. American Journal of Roentgenology
Andreas M FischerU Joseph Schoepf

Abstract

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared usi...Continue Reading

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Citations

Jan 28, 2021·Pattern Analysis and Applications : PAA·Tallha AkramRytis Maskeliūnas
Jan 1, 2021·BioMed Research International·Giuseppe MuscogiuriGianluca Pontone
Mar 4, 2021·International Journal of Chronic Obstructive Pulmonary Disease·Xian Wen SunQing Yun Li
May 11, 2021·Radiology. Cardiothoracic Imaging·Mannudeep K Kalra, Shadi Ebrahimian

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