EC Pulmonology and Respiratory Medicine

Artificial Intelligence (AI) is transforming pulmonary medicine by significantly enhancing diagnostic accuracy, prognostic capabilities, and treatment outcomes across various pulmonary diseases. Leveraging clinical data, imaging modalities, and advanced machine learning algorithms, AI enables precise disease management. Studies demonstrate AI's superiority in interpreting pulmonary function tests, accurately predicting mortality in chronic obstructive pulmonary disease (COPD), and efficiently identifying undiagnosed cases from low-dose CT scans. In asthma and interstitial lung disease (ILD), AI aids in diagnosis, phenotype classification, and exacerbation prediction, offering personalized management strategies tailored to individual patient needs. Moreover, in pulmonary infections like tuberculosis (TB) and COVID-19, AI-based systems facilitate early detection, prognosis, and management, guiding timely interventions to improve patient outcomes. Additionally, AI technologies enhance the detection and characterization of pulmonary nodules and lung malignancies, improving diagnostic accuracy and treatment decisions. This article will describe these AI methods and explore their effectiveness.

 Keywords: Lung Disease; Artificial Intelligence; Detection; Chronic Obstructive Pulmonary Disease; Interstitial Lung Disease; COVID-19; Tuberculosis

1. Frankenfield Jake. “How Artificial Intelligence Works”. Investopedia (2020).
2. Choudhury Saiara., et al. “Applications of Artificial Intelligence in Common Pulmonary Diseases”. Artificial Intelligence in Medical Imaging1 (2022): 1-7.
3. Feng Yinhe., et al. “Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease”. International Journal of Medical Sciences13 (2021): 2871-2889.
4. Kaplan Alan., 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 Practice6 (2021): 2255-2261.
5. Ali Issa., et al. “Lung Nodule Detection via Deep Reinforcement Learning”. Frontiers in Oncology 8 (2018): 108.
6. Crapo Robert O. “Pulmonary-Function Testing”. New England Journal of Medicine1 (1994): 25-30.
7. Topalovic Marko., et al. “Artificial Intelligence Outperforms Pulmonologists in the Interpretation of Pulmonary Function Tests”. European Respiratory Journal4 (2019): 1801660.
8. World Health Organization. “Chronic Obstructive Pulmonary Disease (COPD)”. World Health Organization: WHO (2023).
9. Mayo Clinic. “COPD”. Mayo Clinic, Mayo Clinic (2020).
10. Moll Matthew., et al. “Machine Learning and Prediction of All-Cause Mortality in Chronic Obstructive Pulmonary Disease”. Chest 3 (2020): 952-964.
11. Tang Lisa YW., 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 Health5 (2020): e259-e267.
12. Castaldi Peter J., et al. “Machine Learning Characterization of COPD Subtypes”. Chest5 (2020): 1147-1157.
13. Fischer Andreas M., 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 Imaging1 (2020): S28-S34.
14. Swaminathan Sumanth., et al. “A Machine Learning Approach to Triaging Patients with Chronic Obstructive Pulmonary Disease”. PloS One11 (2017): e0188532.
15. National Heart, Lung, and Blood Institute. “What Is Asthma?” (2022).
16. Aaron Shawn D., et al. “Underdiagnosis and Overdiagnosis of Asthma”. American Journal of Respiratory and Critical Care Medicine 8 (2018): 1012-1020.
17. Chung Il Wi., et al. “Natural Language Processing for Asthma Ascertainment in Different Practice Settings”. The Journal of Allergy and Clinical Immunology: In Practice 1 (2018): 126-131.
18. Wi Chung-Il., et al. “Application of a Natural Language Processing Algorithm to Asthma Ascertainment. An Automated Chart Review”. American Journal of Respiratory and Critical Care Medicine4 (2017): 430-437.
19. Kaur Harsheen., et al. “Automated Chart Review Utilizing Natural Language Processing Algorithm for Asthma Predictive Index”. BMC Pulmonary Medicine1 (2018): 34.
20. Alizadeh Behrouz., et al. “Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network”. Acta Informatica Medica4 (2015): 220-223.
21. Wu Wei., et al. “Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe Asthma”. American Journal of Respiratory and Critical Care Medicine11 (2019): 1358-1367.
22. Qin Yu., et al. “Deep Learning Algorithms-Based CT Images in Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction”. Journal of Healthcare Engineering (2021): 5317403.
23. Walsh Simon LF., et al. “Deep Learning for Classifying Fibrotic Lung Disease on High-Resolution Computed Tomography: A Case-Cohort Study”. The Lancet Respiratory Medicine11 (2018): 837-845.
24. Choe Jooae., et al. “Content-Based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT”. Radiology1 (2022): 187-197.
25. Handa Tomohiro., et al. “Novel Artificial Intelligence-Based Technology for Chest Computed Tomography Analysis of Idiopathic Pulmonary Fibrosis”. Annals of the American Thoracic Society3 (2022): 399-406.
26. Kulkarni Sagar and Saurabh Jha. “Artificial Intelligence, Radiology, and Tuberculosis: A Review”. Academic Radiology1 (2020): 71-75.
27. Chopra KK and VK Arora. “Artificial Intelligence and TB Management - the Way Forward”. Indian Journal of Tuberculosis1 (2020): 1-2.
28. Doshi Riddhi., et al. “Tuberculosis Control, and the Where and Why of Artificial Intelligence”. ERJ Open Research2 (2017): 00056-2017.
29. Garfein RS., et al. “Feasibility of Tuberculosis Treatment Monitoring by Video Directly Observed Therapy: A Binational Pilot Study”. The International Journal of Tuberculosis and Lung Disease 9 (2015): 1057-1064.
30. Heorhi Sinkou., et al. “Video-Observed Treatment for Tuberculosis Patients in Belarus: Findings from the First Programmatic Experience”. The European Respiratory Journal3 (2017): 1602049-49.
31. GLI QUICK GUIDE to TB DIAGNOSTICS CONNECTIVITY SOLUTIONS (2024).
32. “Global Storm: The Effects of the COVID-19 Pandemic and Responses around the World”. Fraser Institute (2021).
33. Li Lin., et al. “Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT”. Radiology 19 (2020): 200905.
34. Zhu Jocelyn., et al. “Deep Transfer Learning Artificial Intelligence Accurately Stages COVID-19 Lung Disease Severity on Portable Chest Radiographs”. PLOS ONE7 (2020): e0236621.
35. Burdick Hoyt., et al. “Prediction of Respiratory Decompensation in Covid-19 Patients Using Machine Learning: The READY Trial”. Computers in Biology and Medicine 124 (2020): 103949.
36. Khemasuwan Danai., et al. “Artificial Intelligence in Pulmonary Medicine: Computer Vision, Predictive Model and COVID-19”. European Respiratory Review157 (2020): 200181.
37. Binczyk Franciszek., et al. “Radiomics and Artificial Intelligence in Lung Cancer Screening”. Translational Lung Cancer Research2 (2021): 1186-1199.
38. Zhang Yuwen., et al. “Pulmonary Lobar Segmentation from Computed Tomography Scans Based on a Statistical Finite Element Analysis of Lobe Shape”. SPIE Digital Library (2019).
39. Chauvie Stéphane., et al. “Artificial Intelligence and Radiomics Enhance the Positive Predictive Value of Digital Chest Tomosynthesis for Lung Cancer Detection within SOS Clinical Trial”. European Radiology7 (2020): 4134-4140.
40. Lambin Philippe., et al. “Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis”. European Journal of Cancer4 (2012): 441-446.
41. Afshar Parnian., et al. “[Formula: see text]: Deep Learning-Based Radiomics for the Time-To-Event Outcome Prediction in Lung Cancer”. Scientific Reports1 (2020).