REFERENCES
2. McCarthy C, Lara Gallego B, Trapnell BC, et al. Epidemiology of rare lung diseases: The challenges and opportunities to improve research and knowledge. In: Posada de la Paz M, Taruscio D, Groft SC, Editors. Rare diseases epidemiology: update and overview. Cham: Springer International Publishing; 2017. pp. 419-42.
3. Willemink MJ, Noël PB. The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol. 2019;29:2185-95.
4. Chen H, Zhang Y, Zhang W, et al. Low-dose CT via convolutional neural network. Biomed Opt Express. 2017;8:679-94.
5. Chen H, Zhang Y, Kalra MK, et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans Med Imaging. 2017;36:2524-35.
6. Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. arXiv. 2014;arXiv:1406.2661. Available from: https://arxiv.org/abs/1406.2661 [Last accessed on 20 Jul 2026].
7. Pan J, Zhang H, Wu W, Gao Z, Wu W. Multi-domain integrative swin transformer network for sparse-view tomographic reconstruction. Patterns. 2022;3:100498.
8. Hu J, Zheng S, Wang B, Luo G, Huang W, Zhang J. Super-resolution swin transformer and attention network for medical CT imaging. Biomed Res Int. 2022;2022:4431536.
9. Tan Z, Li J, Tao H, Li S, Hu Y. XctNet: Reconstruction network of volumetric images from a single X-ray image. Comput Med Imaging Graph. 2022;98:102067.
10. Ying X, Guo H, Ma K, et al. X2CT-GAN: reconstructing CT from biplanar X-rays with generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019 Jun 15-20; Long Beach, CA, USA. IEEE; 2019. pp. 10611-20.
11. Jiang J, Hu YC, Tyagi N, et al. Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets. Med Phys. 2019;46:4392-404.
12. Jue J, Jason H, Neelam T, et al. Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation. In: Shen D, Liu T, Peters TM, et al. Editors. Medical image computing and computer assisted intervention-MICCAI 2019. Cham: Springer International Publishing; 2019. pp. 221-9.
13. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. In: Proceedings of the 34th International Conference on Neural Information Processing Systems; Red Hook, NY, USA. Curran Associates Inc.; 2020. pp. 6840-51. Available from: https://dl.acm.org/doi/abs/10.5555/3495724.3496298 [Last accessed on 20 Jul 2026].
14. Kazerouni A, Aghdam EK, Heidari M, et al. Diffusion models in medical imaging: a comprehensive survey. Med Image Anal. 2023;88:102846.
15. Jiang Y, Chen H, Loew M, Ko H. COVID-19 CT image synthesis with a conditional generative adversarial network. IEEE J Biomed Health Inform. 2021;25:441-52.
16. Onishi Y, Teramoto A, Tsujimoto M, et al. Automated pulmonary nodule classification in computed tomography images using a deep convolutional neural network trained by generative adversarial networks. Biomed Res Int. 2019;2019:6051939.
17. Apostolopoulos ID, Papathanasiou ND, Panayiotakis GS. Classification of lung nodule malignancy in computed tomography imaging utilising generative adversarial networks and semi-supervised transfer learning. Biocybern Biomed Eng. 2021;41:1243-57.
18. Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, et al. Editors. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham: Springer International Publishing; 2015. pp. 234-41.
19. Milletari F, Navab N, Ahmadi S-A. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV); 2016 Oct 25-28; Stanford, CA, USA. IEEE; 2016. pp. 565-71.
20. Hu H, Li Q, Zhao Y, Zhang Y. Parallel deep learning algorithms with hybrid attention mechanism for image segmentation of lung tumors. IEEE Trans Ind Inf. 2021;17:2880-9.
21. Nan Y, Ser JD, Tang Z, et al. Fuzzy attention neural network to tackle discontinuity in airway segmentation. IEEE Trans Neural Netw Learn Syst. 2024;35:7391-404.
22. Abdalaoui Slimani F, Bentourkia M. Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging. Phys Eng Sci Med. 2025;48:59-73.
23. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18:203-11.
24. Wasserthal J, Breit HC, Meyer MT, et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell. 2023;5:e230024.
25. Diaz-Pinto A, Alle S, Nath V, et al. MONAI label: a framework for AI-assisted interactive labeling of 3D medical images. Med Image Anal. 2024;95:103207.
26. Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640-51.
27. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv. 2021;arXiv:2010.11929. Available from: http://arxiv.org/abs/2010.11929 [Last accessed on 20 Jul 2026].
28. Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct 10-17; Montreal, QC, Canada. IEEE; 2021. pp. 9992-10002.
29. Chen J, Lu Y, Yu Q, et al. TransUNet: transformers make strong encoders for medical image segmentation. arXiv. 2021;arXiv:2102.04306. Available from: http://arxiv.org/abs/2102.04306 [Last accessed on 20 Jul 2026].
30. Raza R, Zulfiqar F, Khan MO, et al. Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images. Eng Appl Artif Intell. 2023;126:106902.
31. Huang ML, Liao YC. A lightweight CNN-based network on COVID-19 detection using X-ray and CT images. Comput Biol Med. 2022;146:105604.
32. Kathamuthu ND, Subramaniam S, Le QH, et al. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. Adv Eng Softw. 2023;175:103317.
33. Jiang H, Tang S, Liu W, Zhang Y. Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer. Comput Struct Biotechnol J. 2021;19:1391-9.
34. Sabir M, Farhan M, Almalki NS, Alnfiai MM, Sampedro GA. FibroVit-Vision transformer-based framework for detection and classification of pulmonary fibrosis from chest CT images. Front Med. 2023;10:1282200.
35. UrRehman Z, Qiang Y, Wang L, et al. Effective lung nodule detection using deep CNN with dual attention mechanisms. Sci Rep. 2024;14:3934.
36. Sangeetha SKB, Mathivanan SK, Karthikeyan P, et al. An enhanced multimodal fusion deep learning neural network for lung cancer classification. Syst Soft Comput. 2024;6:200068.
37. Huang SC, Pareek A, Zamanian R, Banerjee I, Lungren MP. Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection. Sci Rep. 2020;10:22147.
38. Hasenstab KA, Yuan N, Retson T, et al. Automated CT staging of chronic obstructive pulmonary disease severity for predicting disease progression and mortality with a deep learning convolutional neural network. Radiol Cardiothorac Imaging. 2021;3:e200477.
39. González G, Ash SY, Vegas-Sánchez-Ferrero G, et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am J Respir Crit Care Med. 2018;197:193-203.
40. Mukherjee P, Zhou M, Lee E, et al. A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional CT-image data. Nat Mach Intell. 2020;2:274-82.
41. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018;18:24.
42. Mei X, Liu Z, Singh A, et al. Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data. Nat Commun. 2023;14:2272.
43. Han J, Xiao N, Yang W, et al. MS-ResNet: disease-specific survival prediction using longitudinal CT images and clinical data. Int J Comput Assist Radiol Surg. 2022;17:1049-57.
44. Aslani S, Alluri P, Gudmundsson E, et al. Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning. Comput Med Imaging Graph. 2024;116:102399.
45. Zhang S, Liu X, Zhou L, et al. Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study. EClinicalMedicine. 2023;65:102270.
46. Gong J, Fu F, Ma X, et al. Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images. Phys Med Biol. 2023;68:245021.
47. Ye G, Wu G, Qi Y, et al. Non-invasive multimodal CT deep learning biomarker to predict pathological complete response of non-small cell lung cancer following neoadjuvant immunochemotherapy: a multicenter study. J Immunother Cancer. 2024;12:e009348.
48. Niu Y, Jia HB, Li XM, et al. Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative brain metastasis risk in non-small cell lung cancer. BMC Cancer. 2025;25:1133.
49. Ortiz A, Trivedi A, Desbiens J, et al. Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes. Sci Rep. 2022;12:1716.
50. She Y, He B, Wang F, et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicentre study. EBioMedicine. 2022;86:104364.
51. Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2022;205:e18-47.
52. Refaee T, Salahuddin Z, Frix AN, et al. Diagnosis of idiopathic pulmonary fibrosis in high-resolution computed tomography scans using a combination of handcrafted radiomics and deep learning. Front Med. 2022;9:915243.
53. Bratt A, Williams JM, Liu G, et al. Predicting usual interstitial pneumonia histopathology from chest CT imaging with deep learning. Chest. 2022;162:815-23.
54. Huang X, Si W, Ye X, et al. Novel 3D-based deep learning for classification of acute exacerbation of idiopathic pulmonary fibrosis using high-resolution CT. BMJ Open Respir Res. 2024;11:e002226.
55. Sun H, Liu M, Kang H, et al. Quantitative analysis of high-resolution computed tomography features of idiopathic pulmonary fibrosis: a structure-function correlation study. Quant Imaging Med Surg. 2022;12:3655-65.
56. Thillai M, Oldham JM, Ruggiero A, et al. Deep learning-based segmentation of computed tomography scans predicts disease progression and mortality in idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2024;210:465-72.
57. Maetani T, Tanabe N, Tanizawa K, et al. Computed tomography morphological assessments of central airways in interstitial lung abnormalities and idiopathic pulmonary fibrosis. Respir Res. 2024;25:404.
58. Nan Y, Felder FN, Humphries S, et al. Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study. Eur Respir J. 2025;66:2500981.
59. Humphries SM, Mackintosh JA, Jo HE, et al. Quantitative computed tomography predicts outcomes in idiopathic pulmonary fibrosis. Respirology. 2022;27:1045-53.
60. Humphries SM, Thieke D, Baraghoshi D, et al. Deep learning classification of usual interstitial pneumonia predicts outcomes. Am J Respir Crit Care Med. 2024;209:1121-31.
62. Nezamabadi K, Naseri Z, Moghaddam HA, Modarresi M, Pak N, Mahdizade M. Lung HRCT pattern classification for cystic fibrosis using convolutional neural network. SIViP. 2019;13:1225-32.
63. Zucker EJ, Barnes ZA, Lungren MP, et al. Deep learning to automate brasfield chest radiographic scoring for cystic fibrosis. J Cyst Fibros. 2020;19:131-8.
64. Dournes G, Hall CS, Willmering MM, et al. Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators. Eur Respir J. 2022;59:2100844.
65. Hadj Bouzid AI, Bui S, Benlala I, et al. Artificial intelligence-driven volumetric CT outcome score in cystic fibrosis: longitudinal and multicenter validation with/without modulators treatment. Eur Radiol. 2025;35:815-27.
66. Hadj Bouzid AI, Pasche D, Benlala I, et al. Quantitative CT evaluation of bronchiectasis improvement in cystic fibrosis after CFTR-modulator therapy. Radiol Cardiothorac Imaging. 2025;7:e250116.
67. McCormack FX, Gupta N, Finlay GR, et al. Official American Thoracic Society/Japanese Respiratory Society clinical practice guidelines: lymphangioleiomyomatosis diagnosis and management. Am J Respir Crit Care Med. 2016;194:748-61.
68. Jonas A, Muelly M, Gupta N, Reicher JJ. Machine learning to distinguish lymphangioleiomyomatosis from other diffuse cystic lung diseases. Respir Investig. 2022;60:430-3.
69. Golbus AE, Steveson C, Schuzer JL, et al. Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM). Eur Radiol. 2024;34:5613-20.
70. Zhang L, Gopalakrishnan V, Lu L, et al. Self-learning to detect and segment cysts in lung CT images without manual annotation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); 2018 Apr 4-7; Washington, DC, USA. IEEE; 2018. pp. 1100-3.
71. McCarthy C, Bonella F, O'Callaghan M, et al. European Respiratory Society guidelines for the diagnosis and management of pulmonary alveolar proteinosis. Eur Respir J. 2024;64:2400725.
72. Shi S, Zou R, Chen L, et al. Quantitative chest CT assessment of pulmonary alveolar proteinosis with deep learning: a real-world longitudinal study. Quant Imaging Med Surg. 2022;12:5394-403.
73. Shi S, Gui X, Ding J, et al. Assessment of statin treatment for pulmonary alveolar proteinosis without hypercholesterolemia: a 12-month prospective, longitudinal, and observational study. Biomed Res Int. 2022;2022:1589660.
74. Gao Z, Zhang G, Liang H, et al. A lung CT vision foundation model facilitating disease diagnosis and medical imaging. Nat Commun. 2025;17:35.
