Artificial-intelligence in tomography for diagnosis of interstitial lung diseases
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1277Keywords:
Interstitial, Lung Diseases, Tomography, Artificial IntelligenceAbstract
Objective: Analyze the influence of Artificial Intelligence on the pathological diagnosis of Interstitial Lung Diseases (ILDs) through Tomography (CT) using Deep Learning (DL) in an integrative review. Methodology: We utilized English Mesh descriptors for the respective keywords, combined with the boolean operator "AND," on the MEDLINE and PubMed platforms. Results: Out of 36 articles from each database, 8 retrospective cohorts were analyzed, addressing the use of algorithms in quantifying parenchymal lesions, lung volume, image retrieval in databases, and performance comparison between technology and observer in the context of ILD diagnosis in CT scans. Conclusion: DL through algorithms in CT scans shows promise in aiding ILD diagnosis more efficiently, potentially streamlining this process in the future. However, further studies, particularly prospective ones with extensive databases, are necessary for even better results.
References
Wijsenbeek M, Suzuki A, Maher TM. Interstitial lung diseases. The Lancet. 2022 Sep;400(10354):769–86.
Exarchos KP, Gkrepi G, Kostikas K, Gogali A. Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics (Basel, Switzerland). 2023 Jul 6;13(13):2303.
Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, et al. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Academic Radiology. 2022 Feb;29:S226–35.
Dack E, Christe A, Fontanellaz M, Brigato L, Heverhagen JT, Peters AA, et al. Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis. Investigative radiology. 2023 Aug 1;58(8):602–9.
Rea G, Sverzellati N, Bocchino M, Lieto R, Milanese G, D’Alto M, et al. Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology.” Diagnostics (Basel, Switzerland). 2023 Jul 10;13(14):2333.
Furukawa T, Oyama S, Yokota H, Kondoh Y, Kataoka K, Johkoh T, et al. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Respirology. 27(9):739–46.
Exarchos KP, Gkrepi G, Kostikas K, Gogali A. Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics (Basel). 2023 Jan 1;
Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, et al. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Academic Radiology. 2022 Feb;29:S226–35.
Islam MN, Inan TT, Rafi S, Akter SS, Sarker IH, Islam AKMN. A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic. IEEE transactions on artificial intelligence. 2021 Mar 1;1(3):258–70.
Choe J, Hwang HJ, Seo JB, Lee SM, Yun J, Kim MJ, et al. Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology. 2022 Jan;302(1):187–97.
Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. The Lancet Respiratory Medicine. 2018 Nov;6(11):837–45.
Christe A, Peters AA, Drakopoulos D, Heverhagen JT, Geiser T, Stathopoulou T, et al. Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images. Investigative radiology. 2019 Oct;54(10):627–32.
Agarwala S, Kale M, Kumar D, Swaroop R, Kumar A, Kumar Dhara A, et al. Deep learning for screening of interstitial lung disease patterns in high-resolution CT images. Clinical Radiology. 2020 Jun;75(6):481.e1-481.e8.
Bratt A, Williams JM, Liu G, Panda A, Patel PP, Walkoff L, et al. Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning. Chest. 2022 Oct;162(4):815–23.
Handa T, Tanizawa K, Oguma T, Uozumi R, Watanabe K, Tanabe N, et al. Novel Artificial Intelligence-based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis. Ann Am Thorac Soc. 2022 Jan 1;399–406.
Yin N, Shen C, Dong F, Wang J, Guo Y, Bai L. Computer-aided identification of interstitial lung disease based on computed tomography. Journal of X-Ray Science and Technology. 2019 Sep 4;27(4):591–603.
Yu W, Zhou H, Choi Y, Goldin JG, Teng P, Wong WK, et al. Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images. Medical physics. 2023 Feb;50(2):894–905.
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