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Items: 1 to 50 of 70

1.

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Christopher M, Bowd C, Belghith A, Goldbaum MH, Weinreb RN, Fazio MA, Girkin CA, Liebmann JM, Zangwill LM.

Ophthalmology. 2019 Sep 30. pii: S0161-6420(19)32103-7. doi: 10.1016/j.ophtha.2019.09.036. [Epub ahead of print]

PMID:
31718841
2.

GNAQ and PMS1 Mutations Associated with Uveal Melanoma, Ocular Surface Melanosis, and Nevus of Ota.

Toomey CB, Fraser K, Thorson JA, Goldbaum MH, Lin JH.

Ocul Oncol Pathol. 2019 Jun;5(4):267-272. doi: 10.1159/000495508. Epub 2019 Jan 10.

PMID:
31367589
3.

Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs.

Christopher M, Belghith A, Bowd C, Proudfoot JA, Goldbaum MH, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM.

Sci Rep. 2018 Nov 12;8(1):16685. doi: 10.1038/s41598-018-35044-9.

4.

Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression.

Christopher M, Belghith A, Weinreb RN, Bowd C, Goldbaum MH, Saunders LJ, Medeiros FA, Zangwill LM.

Invest Ophthalmol Vis Sci. 2018 Jun 1;59(7):2748-2756. doi: 10.1167/iovs.17-23387.

5.

Optic nerve head problem.

Verma R, Chen KC, Ramkumar HL, Goldbaum MH, Shields CL.

Surv Ophthalmol. 2019 Jul - Aug;64(4):579-583. doi: 10.1016/j.survophthal.2017.10.003. Epub 2017 Oct 9.

PMID:
29024674
6.

Ophthalmic manifestations of tuberous sclerosis: a review.

Hodgson N, Kinori M, Goldbaum MH, Robbins SL.

Clin Exp Ophthalmol. 2017 Jan;45(1):81-86. doi: 10.1111/ceo.12806. Epub 2016 Sep 15. Review.

PMID:
27447981
7.

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

Yousefi S, Balasubramanian M, Goldbaum MH, Medeiros FA, Zangwill LM, Weinreb RN, Liebmann JM, Girkin CA, Bowd C.

Transl Vis Sci Technol. 2016 May 3;5(3):2. eCollection 2016 May.

8.

Detecting glaucomatous change in visual fields: Analysis with an optimization framework.

Yousefi S, Goldbaum MH, Varnousfaderani ES, Belghith A, Jung TP, Medeiros FA, Zangwill LM, Weinreb RN, Liebmann JM, Girkin CA, Bowd C.

J Biomed Inform. 2015 Dec;58:96-103. doi: 10.1016/j.jbi.2015.09.019. Epub 2015 Oct 9.

9.

Visual phenomena perceived during pars plana vitrectomy under peribulbar block and monitored anaesthesia care.

Ramkumar HL, Khatibi A, Freeman WR, Barteselli G, Ezon IC, Amini P, Sharpsten L, Arcinue CA, Nezgoda JT, Ferreyra HA, Goldbaum MH.

Br J Ophthalmol. 2016 Jun;100(6):777-81. doi: 10.1136/bjophthalmol-2015-306874. Epub 2015 Sep 17.

PMID:
26385093
10.

Recognizing patterns of visual field loss using unsupervised machine learning.

Yousefi S, Goldbaum MH, Zangwill LM, Medeiros FA, Bowd C.

Proc SPIE Int Soc Opt Eng. 2014 Mar 21;2014. pii: 90342M.

11.

Vessel Delineation in Retinal Images using Leung-Malik filters and Two Levels Hierarchical Learning.

Varnousfaderani ES, Yousefi S, Bowd C, Belghith A, Goldbaum MH.

AMIA Annu Symp Proc. 2015 Nov 5;2015:1140-7. eCollection 2015.

12.

Learning from data: recognizing glaucomatous defect patterns and detecting progression from visual field measurements.

Yousefi S, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Liebmann JM, Girkin CA, Weinreb RN, Bowd C.

IEEE Trans Biomed Eng. 2014 Jul;61(7):2112-24. doi: 10.1109/TBME.2014.2314714. Epub 2014 Apr 1.

13.

Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points.

Yousefi S, Goldbaum MH, Balasubramanian M, Jung TP, Weinreb RN, Medeiros FA, Zangwill LM, Liebmann JM, Girkin CA, Bowd C.

IEEE Trans Biomed Eng. 2014 Apr;61(4):1143-54. doi: 10.1109/TBME.2013.2295605.

14.

Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.

Bowd C, Weinreb RN, Balasubramanian M, Lee I, Jang G, Yousefi S, Zangwill LM, Medeiros FA, Girkin CA, Liebmann JM, Goldbaum MH.

PLoS One. 2014 Jan 30;9(1):e85941. doi: 10.1371/journal.pone.0085941. eCollection 2014.

15.

PARASITE IN A YOUNG GIRL FROM VIETNAM PRESENTING AS A GOLDEN FOVEAL LESION: Submacular Fly Larva or Nematode?

Khatibi A, Amini P, Goldbaum MH.

Retin Cases Brief Rep. 2013 Winter;7(1):32-4. doi: 10.1097/ICB.0b013e31827aeeac.

PMID:
25390517
16.

Progression of patterns (POP): a machine classifier algorithm to identify glaucoma progression in visual fields.

Goldbaum MH, Lee I, Jang G, Balasubramanian M, Sample PA, Weinreb RN, Liebmann JM, Girkin CA, Anderson DR, Zangwill LM, Fredette MJ, Jung TP, Medeiros FA, Bowd C.

Invest Ophthalmol Vis Sci. 2012 Sep 25;53(10):6557-67.

17.

Predicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurements.

Bowd C, Lee I, Goldbaum MH, Balasubramanian M, Medeiros FA, Zangwill LM, Girkin CA, Liebmann JM, Weinreb RN.

Invest Ophthalmol Vis Sci. 2012 Apr 30;53(4):2382-9. doi: 10.1167/iovs.11-7951.

18.

Pattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART.

Goldbaum MH, Kozak I, Hao J, Sample PA, Lee T, Grant I, Freeman WR.

Graefes Arch Clin Exp Ophthalmol. 2011 Apr;249(4):491-8. doi: 10.1007/s00417-010-1511-x. Epub 2010 Sep 24.

19.

Patterns of glaucomatous visual field loss in sita fields automatically identified using independent component analysis.

Goldbaum MH, Jang GJ, Bowd C, Hao J, Zangwill LM, Liebmann J, Girkin C, Jung TP, Weinreb RN, Sample PA.

Trans Am Ophthalmol Soc. 2009 Dec;107:136-44.

20.

Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers.

Racette L, Chiou CY, Hao J, Bowd C, Goldbaum MH, Zangwill LM, Lee TW, Weinreb RN, Sample PA.

J Glaucoma. 2010 Mar;19(3):167-75. doi: 10.1097/IJG.0b013e3181a98b85.

21.

Machine learning classifiers in glaucoma.

Bowd C, Goldbaum MH.

Optom Vis Sci. 2008 Jun;85(6):396-405. doi: 10.1097/OPX.0b013e3181783ab6. Review.

PMID:
18521021
22.

Machine learning classifiers detect subtle field defects in eyes of HIV individuals.

Kozak I, Sample PA, Hao J, Freeman WR, Weinreb RN, Lee TW, Goldbaum MH.

Trans Am Ophthalmol Soc. 2007;105:111-8; discussion 119-20.

23.

Comparison of 4 mg versus 20 mg intravitreal triamcinolone acetonide injections.

Tammewar AM, Cheng L, Kayikcioglu OR, Falkenstein IA, Kozak I, Goldbaum MH, Freeman WR.

Br J Ophthalmol. 2008 Jun;92(6):810-3. doi: 10.1136/bjo.2007.126227. Epub 2008 Apr 17.

24.

Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes.

Bowd C, Hao J, Tavares IM, Medeiros FA, Zangwill LM, Lee TW, Sample PA, Weinreb RN, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2008 Mar;49(3):945-53. doi: 10.1167/iovs.07-1083.

PMID:
18326717
25.

Endoresection of irradiated choroidal melanoma as a treatment for intractable vitreous hemorrhage and secondary blood-induced glaucoma.

Ferreyra HA, Goldbaum MH, Weinreb RN.

Semin Ophthalmol. 2008 Mar-Apr;23(2):135-8. doi: 10.1080/08820530801894467.

PMID:
18320480
26.

Analysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes.

Goldbaum MH, Falkenstein I, Kozak I, Hao J, Bartsch DU, Sejnowski T, Freeman WR.

Trans Am Ophthalmol Soc. 2008;106:196-204; discussion 204-5.

27.

Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.

Boden C, Chan K, Sample PA, Hao J, Lee TW, Zangwill LM, Weinreb RN, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2007 Dec;48(12):5582-90.

29.

Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.

Goldbaum MH, Sample PA, Zhang Z, Chan K, Hao J, Lee TW, Boden C, Bowd C, Bourne R, Zangwill L, Sejnowski T, Spinak D, Weinreb RN.

Invest Ophthalmol Vis Sci. 2005 Oct;46(10):3676-83.

30.

Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.

Bowd C, Medeiros FA, Zhang Z, Zangwill LM, Hao J, Lee TW, Sejnowski TJ, Weinreb RN, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2005 Apr;46(4):1322-9.

31.

Late intraocular pressure rise after repeat intravitreal triamcinolone acetonide injections.

Lee AC, Crowston JG, Goldbaum MH, Weinreb RN.

Semin Ophthalmol. 2004 Sep-Dec;19(3-4):119-21. No abstract available.

PMID:
15590552
32.

Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers.

Zangwill LM, Chan K, Bowd C, Hao J, Lee TW, Weinreb RN, Sejnowski TJ, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2004 Sep;45(9):3144-51.

33.

Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects.

Sample PA, Chan K, Boden C, Lee TW, Blumenthal EZ, Weinreb RN, Bernd A, Pascual J, Hao J, Sejnowski T, Goldbaum MH.

Invest Ophthalmol Vis Sci. 2004 Aug;45(8):2596-605.

34.

Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes.

Bowd C, Zangwill LM, Medeiros FA, Hao J, Chan K, Lee TW, Sejnowski TJ, Goldbaum MH, Sample PA, Crowston JG, Weinreb RN.

Invest Ophthalmol Vis Sci. 2004 Jul;45(7):2255-62.

35.

Foveal hypoplasia demonstrated in vivo with optical coherence tomography.

McGuire DE, Weinreb RN, Goldbaum MH.

Am J Ophthalmol. 2003 Jan;135(1):112-4.

PMID:
12504716
36.

Permanent postoperative vision loss associated with expansion of intraocular gas in the presence of a nitrous oxide-containing anesthetic.

Seaberg RR, Freeman WR, Goldbaum MH, Manecke GR Jr.

Anesthesiology. 2002 Nov;97(5):1309-10. No abstract available.

PMID:
12411820
37.

Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc.

Bowd C, Chan K, Zangwill LM, Goldbaum MH, Lee TW, Sejnowski TJ, Weinreb RN.

Invest Ophthalmol Vis Sci. 2002 Nov;43(11):3444-54.

PMID:
12407155
38.

Comparison of machine learning and traditional classifiers in glaucoma diagnosis.

Chan K, Lee TW, Sample PA, Goldbaum MH, Weinreb RN, Sejnowski TJ.

IEEE Trans Biomed Eng. 2002 Sep;49(9):963-74.

PMID:
12214886
39.

Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields.

Sample PA, Goldbaum MH, Chan K, Boden C, Lee TW, Vasile C, Boehm AG, Sejnowski T, Johnson CA, Weinreb RN.

Invest Ophthalmol Vis Sci. 2002 Aug;43(8):2660-5. Erratum in: Invest Ophthalmol Vis Sci. 2003 May;44(5):1813.

PMID:
12147600
40.

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

Goldbaum MH, Sample PA, Chan K, Williams J, Lee TW, Blumenthal E, Girkin CA, Zangwill LM, Bowd C, Sejnowski T, Weinreb RN.

Invest Ophthalmol Vis Sci. 2002 Jan;43(1):162-9.

PMID:
11773027
41.

Silicone oil tamponade to seal macular holes without position restrictions.

Goldbaum MH, McCuen BW, Hanneken AM, Burgess SK, Chen HH.

Ophthalmology. 1998 Nov;105(11):2140-7; discussion 2147-8.

PMID:
9818619
42.

Imaging the microvasculature of choroidal melanomas with confocal indocyanine green scanning laser ophthalmoscopy.

Mueller AJ, Bartsch DU, Folberg R, Mehaffey MG, Boldt HC, Meyer M, Gardner LM, Goldbaum MH, Pe'er J, Freeman WR.

Arch Ophthalmol. 1998 Jan;116(1):31-9.

PMID:
9445206
43.

Bilateral endogenous Escherichia coli endophthalmitis in a neonate with meningitis.

Friedlander SM, Raphaelian PV, Granet DB, Goldbaum MH.

Retina. 1996;16(4):341-2. No abstract available.

PMID:
8865397
44.

Interpretation of automated perimetry for glaucoma by neural network.

Goldbaum MH, Sample PA, White H, Côlt B, Raphaelian P, Fechtner RD, Weinreb RN.

Invest Ophthalmol Vis Sci. 1994 Aug;35(9):3362-73.

PMID:
8056511
45.

Peripheral proliferative retinopathies: an update on angiogenesis, etiologies and management.

Jampol LM, Ebroon DA, Goldbaum MH.

Surv Ophthalmol. 1994 May-Jun;38(6):519-40. Review.

PMID:
8066542
46.
47.

An inexpensive, pressure-regulated air pump for fluid-air exchange during pars plana vitrectomy.

Gross JG, Freeman WR, Goldbaum MH, Mendez TL.

Arch Ophthalmol. 1991 Nov;109(11):1492. No abstract available.

PMID:
1755718
48.

Retinal detachment following radial and astigmatic keratotomy.

Feldman RM, Crapotta JA, Feldman ST, Goldbaum MH.

Refract Corneal Surg. 1991 May-Jun;7(3):252-3.

PMID:
2069919
49.

Magnetic resonance imaging in the evaluation of vitreoretinal disease in eyes with intraocular silicone oil.

Gross JG, Hesselink JR, Press GA, Goldbaum MH, Freeman WR.

Am J Ophthalmol. 1990 Oct 15;110(4):366-70.

PMID:
2220970
50.

The discrimination of similarly colored objects in computer images of the ocular fundus.

Goldbaum MH, Katz NP, Nelson MR, Haff LR.

Invest Ophthalmol Vis Sci. 1990 Apr;31(4):617-23.

PMID:
2186008

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