Inteligencia artificial en neumología
DOI:
https://doi.org/10.56050/01205498.1646Palabras clave:
Inteligencia artificial, Neumología, Enfermedad Pulmonar obstructiva crónica, Asma, Hipertensión pulmonar, Enfermedad pulmonar intersticial, Apnea del sueñoResumen
Cada día encontramos con mayor frecuencia el término inteligencia artificial en todos los escenarios de la medicina incluyendo la neumología, siendo una necesidad el conocimiento en el tema y el acercamiento del profesional de salud al uso de estas herramientas. La aplicabilidad de la inteligencia artificial en neumología tuvo sus inicios en la interpretación de pruebas de función pulmonar y en ámbitos de diagnóstico. Sin embargo, rápidamente vemos la importancia del uso en soporte de decisiones, monitoreo y vigilancia de desenlaces en la práctica diaria y en el campo de investigación, modelos de predicción clínicos, solo para mencionar algunos. Este artículo es una revisión narrativa del papel de la inteligencia artificial en algunas de las patologías más frecuentemente vistas en nuestra práctica diaria. Describimos el uso de inteligencia artificial en el diagnóstico clínico, funcional e imagenológico de la EPOC y el asma así como en el monitoreo remoto de los pacientes, los avances en la interpretación de imágenes y de patología de las enfermedades intersticiales con el uso de Aprendizaje Automatizado (AA) y Aprendizaje Profundo (AP), la aplicación en tamización de hipertensión pulmonar a partir de pruebas diferentes al cateterismo cardiaco derecho, y finalmente la amplia gama de aplicaciones en medicina del sueño, en la que el avance es asombroso.
Biografía del autor/a
Leslie Vargas-Ramírez, Instituto Neumológico del Oriente, Bucaramanga
MD. Departamento Neumología, Instituto Neumológico del Oriente, Bucaramanga, Colombia.
Rafael Brango Ayazo, Asmetsalud, Bogotá
MD. Gerencia y Auditoría de la Calidad en Salud, Asmetsalud, Bogotá, Colombia.
Referencias bibliográficas
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2. Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respir J. 2019;54(1):1900829.
3. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019;53(4):1801660.
4. Topalovic M, Laval S, Aerts JM, Troosters T, Decramer M, Janssens W. Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints. Respiration. 2017;93(3):170–8.
5. Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: Current status and future potential. Curr Opin Pulm Med. 2018;24(2):117–23.
6. Bibault JE, Xing L. Screening for chronic obstructive pulmonary disease with artificial intelligence. Lancet Digit Heal. 2020;2(5):e216–7.
7. Tang LYW, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. Lancet Digit Heal. 2020;2(5):e259–67.
8. Isaac A, Nehemiah H, Isaac A, Kannan A. ComputerAided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms. Comput Biol Med. 2020;124:103940.
9. González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, et al. Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. Am J Respir Crit Care Med. 2018;197(2):193-203
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12. Agusti A, Calverley PM, Celli B, Coxson HO, Edwards LD, Lomas DA, et al. Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir Res. 2010;11(1):122. 13. Young AL, Bragman FJS, Rangelov B, Han MK, Galbán CJ, Galbán G, Lynch D, et al. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2020;201:294–302.
13. Ställberg B, Lisspers K, Larsson K, Janson C, Müller M, Łuczko M, et al. Predicting hospitalization due to copd exacerbations in swedish primary care patients using machine learning – based on the arctic study. Int J Chron Obstruct Pulmon Dis. 2021;16:677–88.
14. Moll M, Qiao D, Regan EA, Hunninghake GM, Make BJ, Tal-Singer R, et al. Machine Learning and Prediction of All-Cause Mortality in COPD. Chest. 2020;158(3):952– 64.
15. Matheson AM, Parraga G. Machine Learning Predictions of COPD Mortality: Computational Hide and Seek. Chest. 2020;158(3):846–7.
16. Li X, Zhou HP, Zhou ZJ, Du N, Zhong EH, Zhai K, et al. Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease. Chin Med J (Engl). 2021;134(13):1546–8.
17. Oletic D, Bilas V. Asthmatic Wheeze Detection from Compressively Sensed Respiratory Sound Spectra. IEEE J Biomed Health Inform. 2018;22(5):1406-1414.
18. Kaur H, Sohn S, Wi C-Il, Ryu E, Park MA, Bachman K, et al. Automated chart review utilizing natural language processing algorithm for asthma predictive index. BMC Pulm Med. 2018;18(1):1–9.
19. Amaral JLM, Lopes AJ, Veiga J, Faria ACD, Melo PL. High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements. Comput Methods Programs Biomed. 2017;144:113–25.
20. Krautenbacher N, Flach N, Böck A, Laubhahn K, Laimighofer M, Theis FJ, Ankerst DP, Fuchs C, Schaub B. A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors. Allergy. 2019 Jul;74(7):1364-1373.
21. Wu W, Bang S, Bleecker ER, Castro M, Denlinger L, Erzurum SC, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. Am J Respir Crit Care Med. 2019;199(11):1358–67.
22. Van Vliet D, Smolinska A, Jöbsis Q, Rosias PPR, Muris JWM, Dallinga JW, et al. Association between exhaled inflammatory markers and asthma control in children. J Breath Res. 2016;10(1):016014.
23. Khasha R, Sepehri MM, Mahdaviani SA. An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning. J Med Syst. 2019;43(6):158.
24. Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, et al. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. J Med Internet Res. 2020;22(7):e16981.
25. Huffaker MF, Carchia M, Harris BU, Kethman WC, Murphy TE, Sakarovitch CCD, et al. Passive nocturnal physiologic monitoring enables early detection of exacerbations in children with asthma a proof-of-concept study. Am J Respir Crit Care Med. 2018;198(3):320–8.
26. Horimasu Y, Ohshimo S, Yamaguchi K, Sakamoto S, Masuda T, Nakashima T, et al. A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study. Medicine. 2021;100(7):e24738.
27. Ohno Y, Aoyagi K, Takenaka D, Yoshikawa T, Ikezaki A, Fujisawa Y, Murayama K, Hattori H, Toyama H. Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases. Eur J Radiol. 2021;134:109410.
28. Li Q, Cai W, Feng DD. Lung image patch classification with automatic feature learning. Proc Annu Int Conf IEEE Eng Med Biol Soc. 2013;6079–82.
29. Zhao W, Xu R, Hirano Y, Tachibana R, Kido S. A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Comput Math Methods Med. 2015;2015:567932.
30. Bermejo-Peláez D, Ash SY, Washko GR, San José Estépar R, Ledesma-Carbayo MJ. Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks. Sci Rep. 2020;10(1):1–15.
31. Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on highresolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837–45.
32. Pankratz DG, Choi Y, Imtiaz U, Fedorowicz GM, Anderson JD, Colby T V., et al. Usual interstitial pneumonia can be detected in transbronchial biopsies using machine learning. Ann Am Thorac Soc. 2017;14(11):1646–54.
33. Wells AU, Antoniou KM. The genomic detection of usual interstitial pneumonia from transbronchial biopsy tissue: A dress rehearsal for the future? Ann Am Thorac Soc. 2017;14(11):1632–3.
34. Zou XL, Ren Y, Feng DY, He XQ, Guo YF, Yang HL, Li X, Fang J, Li Q, Ye JJ, Han LQ, Zhang TT. A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study. PLoS One. 2020;15(7):e0236378.
35. Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep. 2020;10(1):1–8.
36. Kwon J myoung, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH. Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Hear Lung Transplant. 2020;39(8):805–14.
37. Leha A, Hellenkamp K, Unsöld B, Mushemi-Blake S, Shah AM, Hasenfuß G, et al. A machine learning approach for the prediction of pulmonary hypertension. PLoS One. 2019;14(10):1–16.
38. Leopold JA, Maron BA. Precision Medicine in Pulmonary Arterial Hypertension. Circ Res. 2019;124(6):832-833.
39. Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy. Circulation. 2018;138(16):1623– 35.
40. Sweatt AJ, Hedlin HK, Balasubramanian V, Hsis A, Blum LK, Robinson WH, et al. Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension HHS Public Access. Circ Res. 2019;124(6):904–19.
41. Ong MS, Klann JG, Lin KJ, Maron BA, Murphy SN, Natter MD, et al. Claims-based algorithms for identifying patients with pulmonary hypertension: A comparison of decision rules and machine-learning approaches. J Am Heart Assoc. 2020;9(19):e016648.
42. Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al. Artificial intelligence in sleep medicine: Background and implications for clinicians. J Clin Sleep Med. 2020;16(4):609–18.
43. Biswal S, Sun H, Goparaju B, Brandon-Westover M, Sun J, Bianchi MT. Expert-level sleep scoring with deep neural networks. J Am Med Informatics Assoc. 2018;25(12):1643–50.
44. Bernardini A, Brunello A, Gigli GL, Montanari A, Saccomanno N. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning. Artif Intell Med. 2021;118:102133.
45. Mostafa SS, Mendonça F, Ravelo-García AG, MorgadoDias F. A systematic review of detecting sleep apnea using deep learning. Sensors. 2019;19(22):1–26.
46. Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al. Artificial intelligence in sleep medicine: An American Academy of Sleep Medicine position statement. J Clin Sleep Med. 2020;16(4):605–7.
47. Pépin JL, Bailly S, Tamisier R. Big Data in sleep apnoea: Opportunities and challenges. Respirology. 2020;25(5):486–94.
2. Delclaux C. No need for pulmonologists to interpret pulmonary function tests. Eur Respir J. 2019;54(1):1900829.
3. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J. 2019;53(4):1801660.
4. Topalovic M, Laval S, Aerts JM, Troosters T, Decramer M, Janssens W. Automated Interpretation of Pulmonary Function Tests in Adults with Respiratory Complaints. Respiration. 2017;93(3):170–8.
5. Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: Current status and future potential. Curr Opin Pulm Med. 2018;24(2):117–23.
6. Bibault JE, Xing L. Screening for chronic obstructive pulmonary disease with artificial intelligence. Lancet Digit Heal. 2020;2(5):e216–7.
7. Tang LYW, Coxson HO, Lam S, Leipsic J, Tam RC, Sin DD. Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT. Lancet Digit Heal. 2020;2(5):e259–67.
8. Isaac A, Nehemiah H, Isaac A, Kannan A. ComputerAided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms. Comput Biol Med. 2020;124:103940.
9. González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva Onieva J, Rahaghi FN, Ross JC, et al. Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. Am J Respir Crit Care Med. 2018;197(2):193-203
10. Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda. Respir Med. 2020;171:106093.
11. Agusti A, Faner R. When Harry Met Sally, or when machine learning met chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2020;201(3):263–75.
12. Agusti A, Calverley PM, Celli B, Coxson HO, Edwards LD, Lomas DA, et al. Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respir Res. 2010;11(1):122. 13. Young AL, Bragman FJS, Rangelov B, Han MK, Galbán CJ, Galbán G, Lynch D, et al. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2020;201:294–302.
13. Ställberg B, Lisspers K, Larsson K, Janson C, Müller M, Łuczko M, et al. Predicting hospitalization due to copd exacerbations in swedish primary care patients using machine learning – based on the arctic study. Int J Chron Obstruct Pulmon Dis. 2021;16:677–88.
14. Moll M, Qiao D, Regan EA, Hunninghake GM, Make BJ, Tal-Singer R, et al. Machine Learning and Prediction of All-Cause Mortality in COPD. Chest. 2020;158(3):952– 64.
15. Matheson AM, Parraga G. Machine Learning Predictions of COPD Mortality: Computational Hide and Seek. Chest. 2020;158(3):846–7.
16. Li X, Zhou HP, Zhou ZJ, Du N, Zhong EH, Zhai K, et al. Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease. Chin Med J (Engl). 2021;134(13):1546–8.
17. Oletic D, Bilas V. Asthmatic Wheeze Detection from Compressively Sensed Respiratory Sound Spectra. IEEE J Biomed Health Inform. 2018;22(5):1406-1414.
18. Kaur H, Sohn S, Wi C-Il, Ryu E, Park MA, Bachman K, et al. Automated chart review utilizing natural language processing algorithm for asthma predictive index. BMC Pulm Med. 2018;18(1):1–9.
19. Amaral JLM, Lopes AJ, Veiga J, Faria ACD, Melo PL. High-accuracy detection of airway obstruction in asthma using machine learning algorithms and forced oscillation measurements. Comput Methods Programs Biomed. 2017;144:113–25.
20. Krautenbacher N, Flach N, Böck A, Laubhahn K, Laimighofer M, Theis FJ, Ankerst DP, Fuchs C, Schaub B. A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors. Allergy. 2019 Jul;74(7):1364-1373.
21. Wu W, Bang S, Bleecker ER, Castro M, Denlinger L, Erzurum SC, et al. Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma. Am J Respir Crit Care Med. 2019;199(11):1358–67.
22. Van Vliet D, Smolinska A, Jöbsis Q, Rosias PPR, Muris JWM, Dallinga JW, et al. Association between exhaled inflammatory markers and asthma control in children. J Breath Res. 2016;10(1):016014.
23. Khasha R, Sepehri MM, Mahdaviani SA. An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning. J Med Syst. 2019;43(6):158.
24. Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, et al. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. J Med Internet Res. 2020;22(7):e16981.
25. Huffaker MF, Carchia M, Harris BU, Kethman WC, Murphy TE, Sakarovitch CCD, et al. Passive nocturnal physiologic monitoring enables early detection of exacerbations in children with asthma a proof-of-concept study. Am J Respir Crit Care Med. 2018;198(3):320–8.
26. Horimasu Y, Ohshimo S, Yamaguchi K, Sakamoto S, Masuda T, Nakashima T, et al. A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study. Medicine. 2021;100(7):e24738.
27. Ohno Y, Aoyagi K, Takenaka D, Yoshikawa T, Ikezaki A, Fujisawa Y, Murayama K, Hattori H, Toyama H. Machine learning for lung CT texture analysis: Improvement of inter-observer agreement for radiological finding classification in patients with pulmonary diseases. Eur J Radiol. 2021;134:109410.
28. Li Q, Cai W, Feng DD. Lung image patch classification with automatic feature learning. Proc Annu Int Conf IEEE Eng Med Biol Soc. 2013;6079–82.
29. Zhao W, Xu R, Hirano Y, Tachibana R, Kido S. A sparse representation based method to classify pulmonary patterns of diffuse lung diseases. Comput Math Methods Med. 2015;2015:567932.
30. Bermejo-Peláez D, Ash SY, Washko GR, San José Estépar R, Ledesma-Carbayo MJ. Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks. Sci Rep. 2020;10(1):1–15.
31. Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on highresolution computed tomography: a case-cohort study. Lancet Respir Med. 2018;6(11):837–45.
32. Pankratz DG, Choi Y, Imtiaz U, Fedorowicz GM, Anderson JD, Colby T V., et al. Usual interstitial pneumonia can be detected in transbronchial biopsies using machine learning. Ann Am Thorac Soc. 2017;14(11):1646–54.
33. Wells AU, Antoniou KM. The genomic detection of usual interstitial pneumonia from transbronchial biopsy tissue: A dress rehearsal for the future? Ann Am Thorac Soc. 2017;14(11):1632–3.
34. Zou XL, Ren Y, Feng DY, He XQ, Guo YF, Yang HL, Li X, Fang J, Li Q, Ye JJ, Han LQ, Zhang TT. A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: A retrospective study. PLoS One. 2020;15(7):e0236378.
35. Kusunose K, Hirata Y, Tsuji T, Kotoku J, Sata M. Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray. Sci Rep. 2020;10(1):1–8.
36. Kwon J myoung, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH. Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Hear Lung Transplant. 2020;39(8):805–14.
37. Leha A, Hellenkamp K, Unsöld B, Mushemi-Blake S, Shah AM, Hasenfuß G, et al. A machine learning approach for the prediction of pulmonary hypertension. PLoS One. 2019;14(10):1–16.
38. Leopold JA, Maron BA. Precision Medicine in Pulmonary Arterial Hypertension. Circ Res. 2019;124(6):832-833.
39. Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, et al. Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy. Circulation. 2018;138(16):1623– 35.
40. Sweatt AJ, Hedlin HK, Balasubramanian V, Hsis A, Blum LK, Robinson WH, et al. Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension HHS Public Access. Circ Res. 2019;124(6):904–19.
41. Ong MS, Klann JG, Lin KJ, Maron BA, Murphy SN, Natter MD, et al. Claims-based algorithms for identifying patients with pulmonary hypertension: A comparison of decision rules and machine-learning approaches. J Am Heart Assoc. 2020;9(19):e016648.
42. Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al. Artificial intelligence in sleep medicine: Background and implications for clinicians. J Clin Sleep Med. 2020;16(4):609–18.
43. Biswal S, Sun H, Goparaju B, Brandon-Westover M, Sun J, Bianchi MT. Expert-level sleep scoring with deep neural networks. J Am Med Informatics Assoc. 2018;25(12):1643–50.
44. Bernardini A, Brunello A, Gigli GL, Montanari A, Saccomanno N. AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning. Artif Intell Med. 2021;118:102133.
45. Mostafa SS, Mendonça F, Ravelo-García AG, MorgadoDias F. A systematic review of detecting sleep apnea using deep learning. Sensors. 2019;19(22):1–26.
46. Goldstein CA, Berry RB, Kent DT, Kristo DA, Seixas AA, Redline S, et al. Artificial intelligence in sleep medicine: An American Academy of Sleep Medicine position statement. J Clin Sleep Med. 2020;16(4):605–7.
47. Pépin JL, Bailly S, Tamisier R. Big Data in sleep apnoea: Opportunities and challenges. Respirology. 2020;25(5):486–94.
Cómo citar
[1]
Vargas-Ramírez, L. y Brango Ayazo, R. 2022. Inteligencia artificial en neumología. Medicina. 43, 4 (ene. 2022), 570–581. DOI:https://doi.org/10.56050/01205498.1646.
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2022-01-18
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