Purpose Idiopathic interstitial pneumonia (IIP) is a condition comprising specific patterns of pulmonary fibrosis and emphysema. The exact prevalence is not fully known, due to differences between studies, lack of key features when examined, or international lack of reporting [1,2]. Because fibrosis and emphysema are often comorbid, and as combined pulmonary fibrosis and emphysema (CPFE) markedly decrease survival rate, accurate diagnosis and management of both conditions is critical to informing clinical decisions and treatment routes [3-5]. High-Resolution Computed Tomography (HRCT) is widely accepted for investigating...

Methods and Materials Data Retrospective data from IIP subjects representing a range of disease severity from mild to severe including CPFE were used from RISE-IIP study [12] and NHS data sets through strategic research agreements and acquired with multi-detector HRCT (different types/vendors and kernel settings, collimation ≈1,25 mm). The training dataset consisted of 850 annotated HRCT slices from 82 volumes. The independent test dataset contained 132 annotated slices from 130 volumes. These were annotated by an image analyst, specialising in ground truth annotation for medical...

Results Table 1 illustrates the correlation between the Analyst, Baseline, and our DL approach in fibrosis and emphysema quantification. Quantification using DL approach shows higher correlation with the ground truth compared to baseline approach. [Fig 1] Figure 2 shows the correlation plots for DL vs. ground truth (132 slices). Close clustering around the regression line suggests agreement between DL and ground truth. [Fig 2] Figure 3-6 show the comparison of each approach to the raw image and ground truth, highlighting the qualitative performance of DL...

Conclusion Manual delineation of emphysema and fibrosis is challenging. Our deep learning approach shows promising results in the automatic quantification of pulmonary fibrosis and emphysema. Automatic delineation of fibrotic and emphysematous regions for the entire volume in less than a minute represents a key advantage of our approach. Such techniques can be used to better understand the extent and progression of disease through HRCT, to help identify patient population in clinical trials and to better inform treatment decisions, thus improving patient outcomes. Future studies in...

C. H. Leow: Employee: Senysne Health V. Corona: Employee: Bayer PLC P. Yousefi: Employee: Senysne Health M. Purtorab: Employee: Bayer AG K. Tait: Employee: Senysne Health S. Mohammadi: Employee: Bayer AG B. Irving: Employee: Senysne Health G. Brüggenwerth: Employee: Bayer AG

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