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For: 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? Transl Lung Cancer Res 2021;10:2378-88. [PMID: 34164285 DOI: 10.21037/tlcr-2020-lcs-06] [Cited by in Crossref: 11] [Cited by in F6Publishing: 15] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Zarinshenas R, Amini A, Mambetsariev I, Abuali T, Fricke J, Ladbury C, Salgia R. Assessment of Barriers and Challenges to Screening, Diagnosis, and Biomarker Testing in Early-Stage Lung Cancer. Cancers (Basel) 2023;15. [PMID: 36900386 DOI: 10.3390/cancers15051595] [Reference Citation Analysis]
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3 Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS Digit Health 2023;2:e0000189. [PMID: 36812620 DOI: 10.1371/journal.pdig.0000189] [Reference Citation Analysis]
4 Mușetescu AE, Gherghina FL, Florescu LM, Streba L, Ciurea PL, Florescu A, Gheonea IA. Computer-Aided Diagnosis of Pulmonary Nodules in Rheumatoid Arthritis. Life (Basel) 2022;12. [PMID: 36431070 DOI: 10.3390/life12111935] [Reference Citation Analysis]
5 Palm V, Norajitra T, von Stackelberg O, Heussel CP, Skornitzke S, Weinheimer O, Kopytova T, Klein A, Almeida SD, Baumgartner M, Bounias D, Scherer J, Kades K, Gao H, Jäger P, Nolden M, Tong E, Eckl K, Nattenmüller J, Nonnenmacher T, Naas O, Reuter J, Bischoff A, Kroschke J, Rengier F, Schlamp K, Debic M, Kauczor H, Maier-hein K, Wielpütz MO. AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine. Healthcare 2022;10:2166. [DOI: 10.3390/healthcare10112166] [Reference Citation Analysis]
6 Koh DM, Papanikolaou N, Bick U, Illing R, Kahn CE Jr, Kalpathi-Cramer J, Matos C, Martí-Bonmatí L, Miles A, Mun SK, Napel S, Rockall A, Sala E, Strickland N, Prior F. Artificial intelligence and machine learning in cancer imaging. Commun Med (Lond) 2022;2:133. [PMID: 36310650 DOI: 10.1038/s43856-022-00199-0] [Reference Citation Analysis]
7 Deng Y, Chen Y, Xie L, Wang L, Zhan J. The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.1001840] [Reference Citation Analysis]
8 Wang L, Zhang M, Pan X, Zhao M, Huang L, Hu X, Wang X, Qiao L, Guo Q, Xu W, Qian W, Xue T, Ye X, Li M, Su H, Kuang Y, Lu X, Ye X, Qian K, Lou J. Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification. Adv Sci (Weinh) 2022;9:e2203786. [PMID: 36257825 DOI: 10.1002/advs.202203786] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Grenier PA, Brun AL, Mellot F. The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography. Diagnostics (Basel) 2022;12:2435. [PMID: 36292124 DOI: 10.3390/diagnostics12102435] [Reference Citation Analysis]
10 Kuo PL, Wu YJ, Wu FZ. Pros and Cons of Applying Deep Learning Automatic Scan-Range Adjustment to Low-Dose Chest CT in Lung Cancer Screening Programs. Acad Radiol 2022;29:1552-4. [PMID: 35410801 DOI: 10.1016/j.acra.2022.02.017] [Reference Citation Analysis]
11 Chen MM, Terzic A, Becker AS, Johnson JM, Wu CC, Wintermark M, Wald C, Wu J. Artificial intelligence in oncologic imaging. Eur J Radiol Open 2022;9:100441. [PMID: 36193451 DOI: 10.1016/j.ejro.2022.100441] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Vliegenthart R, Fouras A, Jacobs C, Papanikolaou N. Innovations in thoracic imaging: CT, radiomics, AI and x-ray velocimetry. Respirology 2022. [PMID: 35965430 DOI: 10.1111/resp.14344] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Ajmera P, Pant R, Seth J, Ghuwalewala S, Kathuria S, Rathi S, Patil S, Edara M, Saini M, Raj P, Duddalwar V, Kulkarni V, Patil P, Kulkarni V, Kharat A. Deep-learning-based automatic detection of pulmonary nodules from chest radiographs.. [DOI: 10.1101/2022.06.21.22276691] [Reference Citation Analysis]
14 Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022;14:1370. [PMID: 35326521 DOI: 10.3390/cancers14061370] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
16 Dyer DS, Sandler KL. Lung Cancer Screening Results and Tracking. Lung Cancer Screening 2022. [DOI: 10.1007/978-3-031-10662-0_6] [Reference Citation Analysis]
17 Prazuch W, Jelitto-gorska M, Durawa A, Dziadziuszko K, Polanska J. Radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography. Bioinformatics and Biomedical Engineering 2022. [DOI: 10.1007/978-3-031-07704-3_29] [Reference Citation Analysis]
18 Zareian F, Rezaei N. Application of Artificial Intelligence in Lung Cancer Detection: The Integration of Computational Power and Clinical Decision-Making. Interdisciplinary Cancer Research 2022. [DOI: 10.1007/16833_2022_46] [Reference Citation Analysis]