138 related articles for article (PubMed ID: 38420613)
41. Progression of vascular changes in macular telangiectasia type 2: comparison between SD-OCT and OCT angiography.
Pauleikhoff D; Gunnemann F; Book M; Rothaus K
Graefes Arch Clin Exp Ophthalmol; 2019 Jul; 257(7):1381-1392. PubMed ID: 31093765
[TBL] [Abstract][Full Text] [Related]
42. Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.
Taylor AG; Mielke C; Mongan J
PLoS Med; 2018 Nov; 15(11):e1002697. PubMed ID: 30457991
[TBL] [Abstract][Full Text] [Related]
43. A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.
Shi D; Lin Z; Wang W; Tan Z; Shang X; Zhang X; Meng W; Ge Z; He M
Front Cardiovasc Med; 2022; 9():823436. PubMed ID: 35391847
[TBL] [Abstract][Full Text] [Related]
44. Detection of Diabetic Retinopathy from Ultra-Widefield Scanning Laser Ophthalmoscope Images: A Multicenter Deep Learning Analysis.
Tang F; Luenam P; Ran AR; Quadeer AA; Raman R; Sen P; Khan R; Giridhar A; Haridas S; Iglicki M; Zur D; Loewenstein A; Negri HP; Szeto S; Lam BKY; Tham CC; Sivaprasad S; Mckay M; Cheung CY
Ophthalmol Retina; 2021 Nov; 5(11):1097-1106. PubMed ID: 33540169
[TBL] [Abstract][Full Text] [Related]
45. Capillary density and caliber as assessed by optical coherence tomography angiography may be significant predictors of diabetic retinopathy severity.
Kushner-Lenhoff S; Kogachi K; Mert M; Chu Z; Shahidzadeh A; Palejwala NV; Wolfe J; Itty S; Drenser KA; Capone A; Dugel PU; Moshfeghi AA; Ameri H; Daskivich LP; Wang RK; Kashani AH
PLoS One; 2022; 17(1):e0262996. PubMed ID: 35081154
[TBL] [Abstract][Full Text] [Related]
46. Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm.
Li F; Liu Z; Chen H; Jiang M; Zhang X; Wu Z
Transl Vis Sci Technol; 2019 Nov; 8(6):4. PubMed ID: 31737428
[TBL] [Abstract][Full Text] [Related]
47. Quality assessment of colour fundus and fluorescein angiography images using deep learning.
König M; Seeböck P; Gerendas BS; Mylonas G; Winklhofer R; Dimakopoulou I; Schmidt-Erfurth UM
Br J Ophthalmol; 2023 Dec; 108(1):98-104. PubMed ID: 36418144
[TBL] [Abstract][Full Text] [Related]
48. Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.
Hassan M; Ali S; Kim JY; Saadia A; Sanaullah M; Alquhayz H; Safdar K
Interdiscip Sci; 2023 Jun; 15(2):273-292. PubMed ID: 36611082
[TBL] [Abstract][Full Text] [Related]
49. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet.
Bien N; Rajpurkar P; Ball RL; Irvin J; Park A; Jones E; Bereket M; Patel BN; Yeom KW; Shpanskaya K; Halabi S; Zucker E; Fanton G; Amanatullah DF; Beaulieu CF; Riley GM; Stewart RJ; Blankenberg FG; Larson DB; Jones RH; Langlotz CP; Ng AY; Lungren MP
PLoS Med; 2018 Nov; 15(11):e1002699. PubMed ID: 30481176
[TBL] [Abstract][Full Text] [Related]
50. Evaluating a 3D deep learning pipeline for cerebral vessel and intracranial aneurysm segmentation from computed tomography angiography-digital subtraction angiography image pairs.
Patel TR; Patel A; Veeturi SS; Shah M; Waqas M; Monteiro A; Baig AA; Pinter N; Levy EI; Siddiqui AH; Tutino VM
Neurosurg Focus; 2023 Jun; 54(6):E13. PubMed ID: 37552697
[TBL] [Abstract][Full Text] [Related]
51. Joint segmentation and classification of retinal arteries/veins from fundus images.
Girard F; Kavalec C; Cheriet F
Artif Intell Med; 2019 Mar; 94():96-109. PubMed ID: 30871687
[TBL] [Abstract][Full Text] [Related]
52. Automated Quantification of Nonperfusion Areas in 3 Vascular Plexuses With Optical Coherence Tomography Angiography in Eyes of Patients With Diabetes.
Hwang TS; Hagag AM; Wang J; Zhang M; Smith A; Wilson DJ; Huang D; Jia Y
JAMA Ophthalmol; 2018 Aug; 136(8):929-936. PubMed ID: 29902297
[TBL] [Abstract][Full Text] [Related]
53. Assessment of superficial and deep retinal vessel density in systemic lupus erythematosus patients using optical coherence tomography angiography.
Arfeen SA; Bahgat N; Adel N; Eissa M; Khafagy MM
Graefes Arch Clin Exp Ophthalmol; 2020 Jun; 258(6):1261-1268. PubMed ID: 32162113
[TBL] [Abstract][Full Text] [Related]
54. A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images.
Hu W; Yii FSL; Chen R; Zhang X; Shang X; Kiburg K; Woods E; Vingrys A; Zhang L; Zhu Z; He M
Transl Vis Sci Technol; 2023 Jul; 12(7):14. PubMed ID: 37440249
[TBL] [Abstract][Full Text] [Related]
55. Artificial Intelligence-Assisted Early Detection of Retinitis Pigmentosa - the Most Common Inherited Retinal Degeneration.
Chen TC; Lim WS; Wang VY; Ko ML; Chiu SI; Huang YS; Lai F; Yang CM; Hu FR; Jang JR; Yang CH
J Digit Imaging; 2021 Aug; 34(4):948-958. PubMed ID: 34244880
[TBL] [Abstract][Full Text] [Related]
56. A Deep-Learning Approach for Automated OCT En-Face Retinal Vessel Segmentation in Cases of Optic Disc Swelling Using Multiple En-Face Images as Input.
Islam MS; Wang JK; Johnson SS; Thurtell MJ; Kardon RH; Garvin MK
Transl Vis Sci Technol; 2020 Mar; 9(2):17. PubMed ID: 32821471
[TBL] [Abstract][Full Text] [Related]
57. Automated segmentation of retinal nonperfusion area in fluorescein angiography in retinal vein occlusion using convolutional neural networks.
Tang Z; Zhang X; Yang G; Zhang G; Gong Y; Zhao K; Xie J; Hou J; Hou J; Sun B; Wang Z
Med Phys; 2021 Feb; 48(2):648-658. PubMed ID: 33300143
[TBL] [Abstract][Full Text] [Related]
58. Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.
Phene S; Dunn RC; Hammel N; Liu Y; Krause J; Kitade N; Schaekermann M; Sayres R; Wu DJ; Bora A; Semturs C; Misra A; Huang AE; Spitze A; Medeiros FA; Maa AY; Gandhi M; Corrado GS; Peng L; Webster DR
Ophthalmology; 2019 Dec; 126(12):1627-1639. PubMed ID: 31561879
[TBL] [Abstract][Full Text] [Related]
59. A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model.
Gegundez-Arias ME; Marin-Santos D; Perez-Borrero I; Vasallo-Vazquez MJ
Comput Methods Programs Biomed; 2021 Jun; 205():106081. PubMed ID: 33882418
[TBL] [Abstract][Full Text] [Related]
60. Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.
Zhang G; Fu DJ; Liefers B; Faes L; Glinton S; Wagner S; Struyven R; Pontikos N; Keane PA; Balaskas K
Lancet Digit Health; 2021 Oct; 3(10):e665-e675. PubMed ID: 34509423
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
