These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
198 related articles for article (PubMed ID: 33384884)
1. Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning. Abdelmotaal H; Mostafa MM; Mostafa ANR; Mohamed AA; Abdelazeem K Transl Vis Sci Technol; 2020 Dec; 9(13):30. PubMed ID: 33384884 [TBL] [Abstract][Full Text] [Related]
2. Keratoconus Screening Based on Deep Learning Approach of Corneal Topography. Kuo BI; Chang WY; Liao TS; Liu FY; Liu HY; Chu HS; Chen WL; Hu FR; Yen JY; Wang IJ Transl Vis Sci Technol; 2020 Sep; 9(2):53. PubMed ID: 33062398 [TBL] [Abstract][Full Text] [Related]
3. Pix2pix Conditional Generative Adversarial Networks for Scheimpflug Camera Color-Coded Corneal Tomography Image Generation. Abdelmotaal H; Abdou AA; Omar AF; El-Sebaity DM; Abdelazeem K Transl Vis Sci Technol; 2021 Jun; 10(7):21. PubMed ID: 34132759 [TBL] [Abstract][Full Text] [Related]
4. Keratoconus detection of changes using deep learning of colour-coded maps. Chen X; Zhao J; Iselin KC; Borroni D; Romano D; Gokul A; McGhee CNJ; Zhao Y; Sedaghat MR; Momeni-Moghaddam H; Ziaei M; Kaye S; Romano V; Zheng Y BMJ Open Ophthalmol; 2021; 6(1):e000824. PubMed ID: 34337155 [TBL] [Abstract][Full Text] [Related]
5. Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities. Shi C; Wang M; Zhu T; Zhang Y; Ye Y; Jiang J; Chen S; Lu F; Shen M Eye Vis (Lond); 2020; 7():48. PubMed ID: 32974414 [TBL] [Abstract][Full Text] [Related]
6. Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study. Kamiya K; Ayatsuka Y; Kato Y; Fujimura F; Takahashi M; Shoji N; Mori Y; Miyata K BMJ Open; 2019 Sep; 9(9):e031313. PubMed ID: 31562158 [TBL] [Abstract][Full Text] [Related]
7. Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning. Xie Y; Zhao L; Yang X; Wu X; Yang Y; Huang X; Liu F; Xu J; Lin L; Lin H; Feng Q; Lin H; Liu Q JAMA Ophthalmol; 2020 May; 138(5):519-526. PubMed ID: 32215587 [TBL] [Abstract][Full Text] [Related]
8. Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps. Kamiya K; Ayatsuka Y; Kato Y; Shoji N; Miyai T; Ishii H; Mori Y; Miyata K Ann Transl Med; 2021 Aug; 9(16):1287. PubMed ID: 34532424 [TBL] [Abstract][Full Text] [Related]
9. Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography. Kamiya K; Ayatsuka Y; Kato Y; Shoji N; Mori Y; Miyata K Front Med (Lausanne); 2021; 8():724902. PubMed ID: 34671618 [No Abstract] [Full Text] [Related]
10. Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning. Agharezaei Z; Firouzi R; Hassanzadeh S; Zarei-Ghanavati S; Bahaadinbeigy K; Golabpour A; Akbarzadeh R; Agharezaei L; Bakhshali MA; Sedaghat MR; Eslami S Sci Rep; 2023 Nov; 13(1):20586. PubMed ID: 37996439 [TBL] [Abstract][Full Text] [Related]
11. A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps. Al-Timemy AH; Mosa ZM; Alyasseri Z; Lavric A; Lui MM; Hazarbassanov RM; Yousefi S Transl Vis Sci Technol; 2021 Dec; 10(14):16. PubMed ID: 34913952 [TBL] [Abstract][Full Text] [Related]
12. Keratoconus Progression Determined at the First Visit: A Deep Learning Approach With Fusion of Imaging and Numerical Clinical Data. Hartmann LM; Langhans DS; Eggarter V; Freisenich TJ; Hillenmayer A; König SF; Vounotrypidis E; Wolf A; Wertheimer CM Transl Vis Sci Technol; 2024 May; 13(5):7. PubMed ID: 38727695 [TBL] [Abstract][Full Text] [Related]
13. Use of a support vector machine for keratoconus and subclinical keratoconus detection by topographic and tomographic data. Arbelaez MC; Versaci F; Vestri G; Barboni P; Savini G Ophthalmology; 2012 Nov; 119(11):2231-8. PubMed ID: 22892148 [TBL] [Abstract][Full Text] [Related]
14. Comparative evaluation of Scheimpflug tomography parameters between thin non-keratoconic, subclinical keratoconic, and mild keratoconic corneas. Huseynli S; Salgado-Borges J; Alio JL Eur J Ophthalmol; 2018 Sep; 28(5):521-534. PubMed ID: 29566542 [TBL] [Abstract][Full Text] [Related]
15. Keratoconus Detection Based on a Single Scheimpflug Image. Consejo A; Solarski J; Karnowski K; Rozema JJ; Wojtkowski M; Iskander DR Transl Vis Sci Technol; 2020 Jun; 9(7):36. PubMed ID: 32832241 [TBL] [Abstract][Full Text] [Related]
16. Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning. Abdelmotaal H; Hazarbassanov RM; Salouti R; Nowroozzadeh MH; Taneri S; Al-Timemy AH; Lavric A; Yousefi S Ophthalmol Sci; 2024; 4(2):100380. PubMed ID: 37868800 [TBL] [Abstract][Full Text] [Related]
17. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. Kovács I; Miháltz K; Kránitz K; Juhász É; Takács Á; Dienes L; Gergely R; Nagy ZZ J Cataract Refract Surg; 2016 Feb; 42(2):275-83. PubMed ID: 27026453 [TBL] [Abstract][Full Text] [Related]
18. Central and peripheral corneal thickness measured with optical coherence tomography, Scheimpflug imaging, and ultrasound pachymetry in normal, keratoconus-suspect, and post-laser in situ keratomileusis eyes. Prospero Ponce CM; Rocha KM; Smith SD; Krueger RR J Cataract Refract Surg; 2009 Jun; 35(6):1055-62. PubMed ID: 19465292 [TBL] [Abstract][Full Text] [Related]
19. Corneal topographic analysis in patients with keratoconus using 3-dimensional anterior segment optical coherence tomography. Nakagawa T; Maeda N; Higashiura R; Hori Y; Inoue T; Nishida K J Cataract Refract Surg; 2011 Oct; 37(10):1871-8. PubMed ID: 21930048 [TBL] [Abstract][Full Text] [Related]
20. Sensitivity and specificity of posterior corneal elevation measured by Pentacam in discriminating keratoconus/subclinical keratoconus. de Sanctis U; Loiacono C; Richiardi L; Turco D; Mutani B; Grignolo FM Ophthalmology; 2008 Sep; 115(9):1534-9. PubMed ID: 18405974 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]