Non-contiguous computed tomography lung scans can be manipulated to permit artificial intelligence analyses for interstitial lung disease in systemic sclerosis

Abstract Artificial intelligence (AI) can analyse high-resolution CT lung scans (HRCT) in various interstitial lung diseases (ILDs), including systemic sclerosis (SSc). Older HRCT lung scans may have been saved as small DICOM file sets consisting of non-contiguous slices. These are not amenable to AI analyses. Our aim was to develop and test a method of rebuilding small non-contiguous sets of HRCT lung slices into larger sets of contiguous slices that could be analysed by AI programs. We deleted sets of DICOM files from 14 large DICOM file set scans from SSc patients and were left with a scan with about 30 equidistant non-contiguous slices. We then inserted copies of scans between each pair of slices to create a large DICOM file set similar in size to the original large file set scan. Both the original scan and the rebuilt large DICOM file set scan were analysed by Contextflow ADVANCE Chest CT. We recorded the values for honeycombing (HC), reticular pattern (RP), ground glass opacities (GGO), and total ILD. We analysed agreement between the original scan and the rebuilt large DICOM file set scan using intraclass correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), Bland–Altman limits-of-agreement (LOA) plots and the Bradley–Blackwood P-value. ICC, CCC, Bradly–Blackwood P-values and Bland–Altman plots showed excellent agreement between scans for HC, RC, GGO, and total ILD except for the Bradley–Blackwood P-value for RP. Non-contiguous HRCT small DICOM file set lung scans in SSc can be manipulated to allow analysis by AI.