Terapevticheskii arkhivTerapevticheskii arkhiv0040-36602309-5342LLC Obyedinennaya Redaktsiya10646210.26442/00403660.2022.03.201407Research ArticleThe role of artificial intelligence in assessing the progression of fibrosing lung diseasesSperanskaiaAleksandra A.a.spera@mail.ruhttps://orcid.org/0000-0001-8322-4509Pavlov First Saint Petersburg State Medical University150320229434094121904202219042022Copyright © 2022, Consilium Medicum2022<p><strong>Introduction. </strong>The widespread use of artificial intelligence (AI) programs during the COVID-19 pandemic to assess the exact volume of lung tissue damage has allowed them to train a large number of radiologists. The simplicity of the program for determining the volume of the affected lung tissue in acute interstitial pneumonia, which has density indicators in the range from -200 HU to -730 HU, which includes the density indicators of "ground glass" and reticulation (the main radiation patterns in COVID-19) allows you to accurately determine the degree of prevalence process. The characteristics of chronic interstitial pneumonia, which are progressive in nature, fit into the same density framework.</p>
<p><strong>А</strong><strong>im. </strong>To аssess AI's ability to assess the progression of fibrosing lung disease using lung volume counting programs used for COVID-19 and chronic obstructive pulmonary disease.</p>
<p><strong>Results. </strong>Retrospective analysis of computed tomography data during follow-up of 75 patients with progressive fibrosing lung disease made it possible to assess the prevalence and growth of interstitial lesions.</p>
<p><strong>Conclusion. </strong>Using the experience of using AI programs to assess acute interstitial pneumonia in COVID-19 can be applied to chronic interstitial pneumonia.</p>computer tomographyartificial intelligenceprogressive fibrosing interstitial lung diseasesкомпьютерная томографияискусственный интеллектпрогрессирующие фиброзирующие интерстициальные заболевания легких[Inui S. Radiology: Cardiothoracic Imaging, 8 April 2020. Available at: https://www.researchgate.net/journal/Radiology-Cardiothoracic-Imaging-2638-6135. Accessed at: 24.02.2022.][Han X, Fan Y, Alwalid O, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021;299(1):E177-86. DOI:10.1148/radiol.2021203153][Flaherty KR, Wells AU, Cottin V, et al. Nintedanib in Progressive Fibrosing Interstitial Lung Diseases. N Engl J Med. 2019;381(18):1718-27. DOI:10.1056/nejmoa1908681][Cottin V, Wollin L, Fischer A, et al. Fibrosing interstitial lung diseases: knowns and unknowns. Eur Respir Rev. 2019;28(151). DOI:10.1183/16000617.0100-2018][Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmona ry fibrosis; evidence based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183:788-824.][Сперанская А.А., Новикова Л.Н., Двораковская И.В., и др. Лучевая и морфологическая картина фиброзирующих болезней легких: от ранних признаков до исхода. Лучевая диагностика и терапия. 2020;11(2):89-98 [Speranskaya AA, Novikova LN, Dvorakovskaya IV, et al. Radiation and morphological picture of fibrosing lung diseases: from early signs to outcome. Diagnostic Radiology and Radiotherapy. 2020;11(2):89-98 (in Russian)]. DOI:10.22328/2079-5343-2020-11-2-89-98][Maldonado F, Moua T, Rajagopalan S, et al. Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis. Eur Respir J. 2014;43(1):204-12. DOI:10.1183/09031936.00071812][Jacob J, Bartholmai B, Rajagopalan S, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of automated computer-based CT analysis with conventional severity measures. Eur Respir J. 2017;49:1601011. DOI:10.1183/13993003.01011-2016]