Andreas M Fischer1,2, Akos Varga-Szemes1, Simon S Martin1,3, Jonathan I Sperl4, Pooyan Sahbaee5, Dominik Neumann6, Joshua Gawlitza7, Thomas Henzler2,8, Colin M Johnson1, John W Nance9, Stefan O Schoenberg2, U Joseph Schoepf1. 1. Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, Charleston, SC. 2. Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Heidelberg. 3. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt. 4. Siemens Healthineers, Forchheim, Germany. 5. Siemens Medical Solutions. 6. Siemens Healthineers, Erlangen. 7. Department for Cardiothoracic Imaging, Clinic for Diagnostic and Interventional Radiology, Saarland University Medical Center, Homburg, Germany. 8. Conradia Germany, München. 9. Department of Radiology, Houston Methodist Hospital, Houston, TX.
Abstract
OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. METHODS: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. RESULTS: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001). CONCLUSIONS: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.
OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. METHODS:Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. RESULTS: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001). CONCLUSIONS: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPDpatients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.
Authors: Andrej Romanov; Michael Bach; Shan Yang; Fabian C Franzeck; Gregor Sommer; Constantin Anastasopoulos; Jens Bremerich; Bram Stieltjes; Thomas Weikert; Alexander Walter Sauter Journal: Diagnostics (Basel) Date: 2021-04-21
Authors: Jordan Chamberlin; Madison R Kocher; Jeffrey Waltz; Madalyn Snoddy; Natalie F C Stringer; Joseph Stephenson; Pooyan Sahbaee; Puneet Sharma; Saikiran Rapaka; U Joseph Schoepf; Andres F Abadia; Jonathan Sperl; Phillip Hoelzer; Megan Mercer; Nayana Somayaji; Gilberto Aquino; Jeremy R Burt Journal: BMC Med Date: 2021-03-04 Impact factor: 8.775
Authors: Madison R Kocher; Jordan Chamberlin; Jeffrey Waltz; Madalyn Snoddy; Natalie Stringer; Joseph Stephenson; Jacob Kahn; Megan Mercer; Dhiraj Baruah; Gilberto Aquino; Ismail Kabakus; Philipp Hoelzer; Pooyan Sahbaee; U Joseph Schoepf; Jeremy R Burt Journal: Heliyon Date: 2022-02-15
Authors: Amir H Sadeghi; Alexander P W M Maat; Yannick J H J Taverne; Robin Cornelissen; Anne-Marie C Dingemans; Ad J J C Bogers; Edris A F Mahtab Journal: JTCVS Tech Date: 2021-03-16