101 related articles for article (PubMed ID: 36026617)
1. The role of artificial intelligence technology analysis of high-resolution computed tomography images in predicting the severity of COVID-19 pneumonia.
Chrzan R; Wojciechowska W; Terlecki M; Klocek M; Rajzer M; Popiela T
Pol Arch Intern Med; 2022 Dec; 132(12):. PubMed ID: 36026617
[TBL] [Abstract][Full Text] [Related]
2. Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters.
Chrzan R; Wizner B; Sydor W; Wojciechowska W; Popiela T; Bociąga-Jasik M; Olszanecka A; Strach M
BMC Infect Dis; 2023 May; 23(1):314. PubMed ID: 37165346
[TBL] [Abstract][Full Text] [Related]
3. The value of lung ultrasound in COVID-19 pneumonia, verified by high resolution computed tomography assessed by artificial intelligence.
Chrzan R; Polok K; Antczak J; Siwiec-Koźlik A; Jagiełło W; Popiela T
BMC Infect Dis; 2023 Mar; 23(1):195. PubMed ID: 37003997
[TBL] [Abstract][Full Text] [Related]
4. Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan.
Tanaka H; Maetani T; Chubachi S; Tanabe N; Shiraishi Y; Asakura T; Namkoong H; Shimada T; Azekawa S; Otake S; Nakagawara K; Fukushima T; Watase M; Terai H; Sasaki M; Ueda S; Kato Y; Harada N; Suzuki S; Yoshida S; Tateno H; Yamada Y; Jinzaki M; Hirai T; Okada Y; Koike R; Ishii M; Hasegawa N; Kimura A; Imoto S; Miyano S; Ogawa S; Kanai T; Fukunaga K
Respir Res; 2023 Oct; 24(1):241. PubMed ID: 37798709
[TBL] [Abstract][Full Text] [Related]
5. Primary SARS-CoV-2 Pneumonia Screening in Adults: Analysis of the Correlation Between High-Resolution Computed Tomography Pulmonary Patterns and Initial Oxygen Saturation Levels.
Alonazi B; Mostafa MA; Farghaly AM; Zindani SA; Al-Watban JA; Altaimi F; Almotairy AS; Fagiry MA; Mahmoud MZ
Curr Med Imaging; 2022; 19(5):486-493. PubMed ID: 35927895
[TBL] [Abstract][Full Text] [Related]
6. Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm.
Lu X; Cui Z; Pan F; Li L; Li L; Liang B; Yang L; Zheng C
Acta Diabetol; 2021 May; 58(5):575-586. PubMed ID: 33420614
[TBL] [Abstract][Full Text] [Related]
7. Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.
Grodecki K; Killekar A; Simon J; Lin A; Cadet S; McElhinney P; Chan C; Williams MC; Pressman BD; Julien P; Li D; Chen P; Gaibazzi N; Thakur U; Mancini E; Agalbato C; Munechika J; Matsumoto H; Menè R; Parati G; Cernigliaro F; Nerlekar N; Torlasco C; Pontone G; Maurovich-Horvat P; Slomka PJ; Dey D
Br J Radiol; 2023 Sep; 96(1149):20220180. PubMed ID: 37310152
[TBL] [Abstract][Full Text] [Related]
8. Artificial intelligence-based CT metrics used in predicting clinical outcome of COVID-19 in young and middle-aged adults.
Xudong Y; Weihong L; Feng X; Yanli L; Weishun L; Fengjun Z; Jiao G; Jiawei L; Xiaolu H; Huailiang H; Jianye L; Sihui Z; Chuanmiao X; Hanhui L; Liang M
Med Phys; 2022 Aug; 49(8):5604-5615. PubMed ID: 35689830
[TBL] [Abstract][Full Text] [Related]
9. AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.
Ardali Duzgun S; Durhan G; Basaran Demirkazik F; Irmak I; Karakaya J; Akpinar E; Gulsun Akpinar M; Inkaya AC; Ocal S; Topeli A; Ariyurek OM
J Comput Assist Tomogr; 2021 Nov-Dec 01; 45(6):970-978. PubMed ID: 34581706
[TBL] [Abstract][Full Text] [Related]
10. Differences among COVID-19, Bronchopneumonia and Atypical Pneumonia in Chest High Resolution Computed Tomography Assessed by Artificial Intelligence Technology.
Chrzan R; Bociąga-Jasik M; Bryll A; Grochowska A; Popiela T
J Pers Med; 2021 May; 11(5):. PubMed ID: 34068751
[TBL] [Abstract][Full Text] [Related]
11. Quantitative CT for detecting COVID‑19 pneumonia in suspected cases.
Lu W; Wei J; Xu T; Ding M; Li X; He M; Chen K; Yang X; She H; Huang B
BMC Infect Dis; 2021 Aug; 21(1):836. PubMed ID: 34412614
[TBL] [Abstract][Full Text] [Related]
12. Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.
Saba L; Agarwal M; Patrick A; Puvvula A; Gupta SK; Carriero A; Laird JR; Kitas GD; Johri AM; Balestrieri A; Falaschi Z; Paschè A; Viswanathan V; El-Baz A; Alam I; Jain A; Naidu S; Oberleitner R; Khanna NN; Bit A; Fatemi M; Alizad A; Suri JS
Int J Comput Assist Radiol Surg; 2021 Mar; 16(3):423-434. PubMed ID: 33532975
[TBL] [Abstract][Full Text] [Related]
13. Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.
Okuma T; Hamamoto S; Maebayashi T; Taniguchi A; Hirakawa K; Matsushita S; Matsushita K; Murata K; Manabe T; Miki Y
Jpn J Radiol; 2021 Oct; 39(10):956-965. PubMed ID: 33988788
[TBL] [Abstract][Full Text] [Related]
14. Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.
Ippolito D; Ragusi M; Gandola D; Maino C; Pecorelli A; Terrani S; Peroni M; Giandola T; Porta M; Talei Franzesi C; Sironi S
Eur Radiol; 2021 May; 31(5):2726-2736. PubMed ID: 33125559
[TBL] [Abstract][Full Text] [Related]
15. From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.
Li Z; Zhong Z; Li Y; Zhang T; Gao L; Jin D; Sun Y; Ye X; Yu L; Hu Z; Xiao J; Huang L; Tang Y
Eur Radiol; 2020 Dec; 30(12):6828-6837. PubMed ID: 32683550
[TBL] [Abstract][Full Text] [Related]
16. Analysis of clinical features and imaging signs of COVID-19 with the assistance of artificial intelligence.
Ren HW; Wu Y; Dong JH; An WM; Yan T; Liu Y; Liu CC
Eur Rev Med Pharmacol Sci; 2020 Aug; 24(15):8210-8218. PubMed ID: 32767351
[TBL] [Abstract][Full Text] [Related]
17. Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method.
Leonardi A; Scipione R; Alfieri G; Petrillo R; Dolciami M; Ciccarelli F; Perotti S; Cartocci G; Scala A; Imperiale C; Iafrate F; Francone M; Catalano C; Ricci P
Eur J Radiol; 2020 Sep; 130():109202. PubMed ID: 32745895
[TBL] [Abstract][Full Text] [Related]
18. Computed Tomography scanning in patients with COVID-19: artificial intelligence analysis of lesions volume and outcome.
Zuo YH; Chen Y; Chen LH; Zhang Q; Qiu B
Eur Rev Med Pharmacol Sci; 2023 Jun; 27(12):5869-5877. PubMed ID: 37401324
[TBL] [Abstract][Full Text] [Related]
19. Inter-reader agreement of high-resolution computed tomography findings in patients with COVID-19 pneumonia: A multi-reader study.
Cereser L; Girometti R; Da Re J; Marchesini F; Como G; Zuiani C
Radiol Med; 2021 Apr; 126(4):577-584. PubMed ID: 33389557
[TBL] [Abstract][Full Text] [Related]
20. The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia.
Szabó IV; Simon J; Nardocci C; Kardos AS; Nagy N; Abdelrahman RH; Zsarnóczay E; Fejér B; Futácsi B; Müller V; Merkely B; Maurovich-Horvat P
Tomography; 2021 Nov; 7(4):697-710. PubMed ID: 34842822
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]