فصلنامه نفس

Turner CR, Fuggetta A, Lavazza L, Wolf AL. A conceptual basis for feature engineering. Journal of Systems and Software. 1999;49(1):3-15.

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444.

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012;25.

Sainath TN, Kingsbury B, Saon G, et al. Deep convolutional neural networks for large-scale speech tasks. Neural Networks. 2015;64:39-48.

Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V. Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling. 2015;55(2):263-274.

Xiong HY, Alipanahi B, Lee LJ, et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347(6218):1254806.

Feng Y, Wang Y, Zeng C, Mao H. Artificial intelligence and machine learning in chronic airway diseases: focus on asthma and chronic obstructive pulmonary disease. International Journal of Medical Sciences. 2021;18(13):2871-2889.

Kaplan A, Cao H, FitzGerald JM, et al. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. The Journal of Allergy and Clinical Immunology: In Practice. 2021;9(6):2255-2261.

Ali I, Hart GR, Gunabushanam G, et al. Lung nodule detection via deep reinforcement learning. Frontiers in Oncology. 2018;8:108.

FDA permits marketing of clinical decision support software for alerting providers of a potential stroke in patients [Internet]. 2018. Available from: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-clinical-decision-support-software-alerting-providers-potential-stroke

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New England Journal of Medicine. 2019;380(14):1347-1358.

Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ. PUFF: an expert system for interpretation of pulmonary function data. Computers and Biomedical Research. 1983;16(3):199-208.

Khemasuwan D, Sorensen JS, Colt HG. Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19. European Respiratory Review. 2020;29(157).

Topalovic M, Das N, Burgel PR, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. European Respiratory Journal. 2019;53(4).

Walsh SL, 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;6(11):837-845.

Gonem S, Janssens W, Das N, Topalovic M. Applications of artificial intelligence and machine learning in respiratory medicine. Thorax. 2020;75(8):695-701.

Hwang EJ, Park S, Jin K-N, et al. Development and validation of a deep learning–based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clinical Infectious Diseases. 2019;69(5):739-747.

Nam JG, Park S, Hwang EJ, et al. Development and validation of deep learning–based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 2019;290(1):218-228.

Seah JC, Tang JS, Kitchen A, Gaillard F, Dixon AF. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology. 2019;290(2):514-522.

Yates E, Yates L, Harvey H. Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification. Clinical Radiology. 2018;73(9):827-831.

Lu MT, Ivanov A, Mayrhofer T, et al. Deep learning to assess long-term mortality from chest radiographs. JAMA Network Open. 2019;2(7):e197416-e197416.

Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine. 2019;25(6):954-961.

Celli BR, Wedzicha JA. Update on clinical aspects of chronic obstructive pulmonary disease. New England Journal of Medicine. 2019;381(13):1257-1266.

Rodrigues SdO, Cunha CMCd, Soares GMV, et al. Mechanisms, pathophysiology and currently proposed treatments of chronic obstructive pulmonary disease. Pharmaceuticals. 2021;14(10):979.

Moll M, Qiao D, Regan EA, et al. Machine learning and prediction of all-cause mortality in COPD. Chest. 2020;158(3):952-964.

Tang LYW, Coxson HO, Lam S, et al. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. The Lancet Digital Health. 2020;2(5):e259-e267.

Castaldi PJ, Boueiz A, Yun J, et al. Machine learning characterization of copd subtypes: insights from the COPDGene study. Chest. 2020;157(5):1147-1157.

Fischer AM, Varga-Szemes A, Martin SS, et al. Artificial intelligence-based fully automated per lobe segmentation and emphysema-quantification based on chest computed tomography compared with global initiative for chronic obstructive lung disease severity of smokers. Journal of Thoracic Imaging. 2020;35 Suppl 1:S28-s34.

Swaminathan S, Qirko K, Smith T, et al. A machine learning approach to triaging patients with chronic obstructive pulmonary disease. PLoS ONE. 2017;12(11):e0188532.

Shirvani Farsani Z, Jahangiri S, Mohebi S, Charbgoo F, Behmanesh M. Vitamin D and asthma: a review of molecular mechanisms and clinical studies. Nafas Journal. 2015;1(4):1-7.

Qin Y, Wang J, Han Y, Lu L. Deep learning algorithms-based ct images in glucocorticoid therapy in asthma children with small airway obstruction. Journal of Healthcare Engineering. 2021;2021:5317403.

Wu W, Bang S, Bleecker ER, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. American Journal of Respiratory and Critical Care Medicine. 2019;199(11):1358-1367.

Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respiratory Medicine. 2018;6(11):837-845.

Choe J, Hwang HJ, Seo JB, et al. Content-based image retrieval by using deep learning for interstitial lung disease diagnosis with chest ct. Radiology. 2022;302(1):187-197.

Kulkarni S, Jha S. Artificial intelligence, radiology, and tuberculosis: a review. Academic Radiology. 2020;27(1):71-75.

36. Chopra KK, Arora VK. Artificial intelligence and TB management - the way forward. Indian Journal of Tuberculosis. 2020;67(1):1-2.

Doshi R, Falzon D, Thomas BV, et al. Tuberculosis control, and the where and why of artificial intelligence. ERJ Open Research. 2017;3(2).

Li L, Qin L, Xu Z, et al. Using artificial intelligence to detect covid-19 and community-acquired pneumonia based on pulmonary ct: evaluation of the diagnostic accuracy. Radiology. 2020;296(2):E65-e71.

Zhu J, Shen B, Abbasi A, et al. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS ONE. 2020;15(7):e0236621.

Burdick H, Lam C, Mataraso S, et al. Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine. 2020;124:103949.

41. Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Translational Lung Cancer Research. 2021;10(2):1186-1199.

42. Farrokhi M, Taheri F, Moeini A, et al. Artificial intelligence for drug development, personalized prescriptions, and adverse event prediction. Kindle. 2024;4(1):1-180.

Charbgoo F, Behmanesh M, Mohebbi S, Shirvani Farsani Z. RNA nanotechnology in gene therapy of lung cancer Nafas Journal. 2014;1(2):1-9.

Farrokhi M, Moeini A, Taheri F, et al. Artificial intelligence in cancer care: from diagnosis to prevention and beyond. Kindle. 2023;3(1):1-149.

Zhang Y, Osanlouy M, Clark AR, et al., Pulmonary lobar segmentation from computed tomography scans based on a statistical finite element analysis of lobe shape. Medical Imaging 2019: Image Processing; 2019: SPIE.

Chauvie S, De Maggi A, Baralis I, et al. Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial. European Radiology. 2020;30(7):4134-4140.

Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441-446.

Afshar P, Mohammadi A, Tyrrell PN, et al. Deep learning-based radiomics for the time-to-event outcome prediction in lung cancer. Scientific Reports. 2020;10(1):12366.

Schreuder A, Scholten ET, van Ginneken B, Jacobs C. Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice? Translational Lung Cancer Research. 2021;10(5):2378-2388.

Belfiore MP, Urraro F, Grassi R, et al. Artificial intelligence to codify lung CT in Covid-19 patients. La radiologia medica. 2020;125(5):500-504.