How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

Overview

Journal J Pers Med

Date 2022 Sep 23

PMID 36143214

Authors

Trieu-Nghi Hoang-Thi

Guillaume Chassagnon

Hai-Dang Tran

Nhat-Nam Le-Dong

Anh Tuan Dinh-Xuan

Marie-Pierre Revel

Affiliations

Soon will be listed here.

Abstract

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients' care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

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References

Mollura D, Azene E, Starikovsky A, Thelwell A, Iosifescu S, Kimble C . White Paper Report of the RAD-AID Conference on International Radiology for Developing Countries: identifying challenges, opportunities, and strategies for imaging services in the developing world. J Am Coll Radiol. 2010; 7(7):495-500. DOI: 10.1016/j.jacr.2010.01.018. View

Grydeland T, Thorsen E, Dirksen A, Jensen R, Coxson H, Pillai S . Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO. Respir Med. 2010; 105(3):343-51. DOI: 10.1016/j.rmed.2010.10.018. View

Ash S, Harmouche R, Vallejo D, Villalba J, Ostridge K, Gunville R . Densitometric and local histogram based analysis of computed tomography images in patients with idiopathic pulmonary fibrosis. Respir Res. 2017; 18(1):45. PMC: 5340000. DOI: 10.1186/s12931-017-0527-8. View

Best A, Lynch A, Bozic C, Miller D, Grunwald G, Lynch D . Quantitative CT indexes in idiopathic pulmonary fibrosis: relationship with physiologic impairment. Radiology. 2003; 228(2):407-14. DOI: 10.1148/radiol.2282020274. View

Gattinoni L, Caironi P, Cressoni M, Chiumello D, Ranieri V, Quintel M . Lung recruitment in patients with the acute respiratory distress syndrome. N Engl J Med. 2006; 354(17):1775-86. DOI: 10.1056/NEJMoa052052. View

Chong D, Kim H, Lo P, Young S, McNitt-Gray M, Abtin F . Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features. IEEE Trans Med Imaging. 2015; 35(1):144-57. DOI: 10.1109/TMI.2015.2459064. View

Sverzellati N, Zompatori M, De Luca G, Chetta A, Bna C, Ormitti F . Evaluation of quantitative CT indexes in idiopathic interstitial pneumonitis using a low-dose technique. Eur J Radiol. 2005; 56(3):370-5. DOI: 10.1016/j.ejrad.2005.05.012. View

Belfiore M, Urraro F, Grassi R, Giacobbe G, Patelli G, Cappabianca S . Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med. 2020; 125(5):500-504. PMC: 7197034. DOI: 10.1007/s11547-020-01195-x. View

Kieu S, Bade A, Ahmad Hijazi M, Kolivand H . A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions. J Imaging. 2021; 6(12). PMC: 8321202. DOI: 10.3390/jimaging6120131. View

10.

Chassagnon G, Vakalopoulou M, Regent A, Zacharaki E, Aviram G, Martin C . Deep Learning-based Approach for Automated Assessment of Interstitial Lung Disease in Systemic Sclerosis on CT Images. Radiol Artif Intell. 2021; 2(4):e190006. PMC: 8082359. DOI: 10.1148/ryai.2020190006. View

11.

Tunon-de-Lara J, Laurent F, Giraud V, Perez T, Aguilaniu B, Meziane H . Air trapping in mild and moderate asthma: effect of inhaled corticosteroids. J Allergy Clin Immunol. 2007; 119(3):583-90. DOI: 10.1016/j.jaci.2006.11.005. View

12.

Ariani A, Silva M, Seletti V, Bravi E, Saracco M, Parisi S . Quantitative chest computed tomography is associated with two prediction models of mortality in interstitial lung disease related to systemic sclerosis. Rheumatology (Oxford). 2017; 56(6):922-927. DOI: 10.1093/rheumatology/kew480. View

13.

Bharati S, Podder P, Mondal M . Hybrid deep learning for detecting lung diseases from X-ray images. Inform Med Unlocked. 2020; 20:100391. PMC: 7341954. DOI: 10.1016/j.imu.2020.100391. View

14.

Schroeder J, McKenzie A, Zach J, Wilson C, Curran-Everett D, Stinson D . Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. AJR Am J Roentgenol. 2013; 201(3):W460-70. PMC: 4067052. DOI: 10.2214/AJR.12.10102. View

15.

Marten K, Dicken V, Kneitz C, Hohmann M, Kenn W, Hahn D . Interstitial lung disease associated with collagen vascular disorders: disease quantification using a computer-aided diagnosis tool. Eur Radiol. 2008; 19(2):324-32. DOI: 10.1007/s00330-008-1152-1. View

16.

Kao E, Liu G, Lee L, Tsai H, Jaw T . Computer-aided detection system for chest radiography: reducing report turnaround times of examinations with abnormalities. Acta Radiol. 2014; 56(6):696-701. DOI: 10.1177/0284185114538017. View

17.

Hansell D, Goldin J, King Jr T, Lynch D, Richeldi L, Wells A . CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015; 3(6):483-96. DOI: 10.1016/S2213-2600(15)00096-X. View

18.

Gonzalez G, Ash S, Vegas-Sanchez-Ferrero G, Onieva J, Rahaghi F, Ross J . Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. Am J Respir Crit Care Med. 2017; 197(2):193-203. PMC: 5768902. DOI: 10.1164/rccm.201705-0860OC. View

19.

Walsh S, Nair A, Hansell D . Post-processing applications in thoracic computed tomography. Clin Radiol. 2013; 68(5):433-48. DOI: 10.1016/j.crad.2012.05.018. View

20.

Best A, Meng J, Lynch A, Bozic C, Miller D, Grunwald G . Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology. 2008; 246(3):935-40. DOI: 10.1148/radiol.2463062200. View