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Diagnosis and Complementary Examinations

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Part of the book series: Stem Cell Biology and Regenerative Medicine ((STEMCELL))

Abstract

Retinal degenerative diseases cause relentless, progressive loss of vision through a variety of mechanisms, affecting photoreceptors, retinal pigment epithelial cells, and choroidal and/or retinal perfusion. The most sensitive measures of disease severity depend on the primary cell damaged in each retinal degeneration. Visual function measures provide information about how the patient experiences the world and therefore provide clinically meaningful outcome measures of disease activity. Unfortunately, some of the most commonly used tests of visual function, including visual acuity, are insensitive measures of disease severity or progression, while more sensitive functional tests are often variable in patients with retinal degenerative disease. Advanced imaging technology permits evaluation of retinal structure with cellular-level resolution in some cases and provides sensitive, objective measures that complement visual function tests to enable multimodal assessment of retinal degenerative diseases. This chapter outlines measures of visual function and retinal structure that are commonly used to diagnose patients with retinal degenerative disease and monitor disease severity during disease progression and in response to experimental therapies.

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References

  1. Ferris FL 3rd, Kassoff A, Bresnick GH, Bailey I. New visual acuity charts for clinical research. Am J Ophthalmol. 1982;94(1):91–6.

    Article  PubMed  Google Scholar 

  2. Beck RW, Maguire MG, Bressler NM, Glassman AR, Lindblad AS, Ferris FL. Visual acuity as an outcome measure in clinical trials of retinal diseases. Ophthalmology. 2007;114(10):1804–9.

    Article  PubMed  Google Scholar 

  3. Raasch TW, Bailey IL, Bullimore MA. Repeatability of visual acuity measurement. Optom Vis Sci. 1998;75(5):342–8.

    Article  CAS  PubMed  Google Scholar 

  4. Arditi A, Cagenello R. On the statistical reliability of letter-chart visual acuity measurements. Invest Ophthalmol Vis Sci. 1993;34(1):120–9.

    CAS  PubMed  Google Scholar 

  5. Rubin GS. Visual acuity and contrast sensitivity. In: Sadda SR, editor. Retinal imaging and diagnostics. Retina. 1. 5th ed. Amsterdam: Elsevier; 2013. p. 300–6.

    Google Scholar 

  6. Beck RW, Moke PS, Turpin AH, Ferris FL 3rd, SanGiovanni JP, Johnson CA, et al. A computerized method of visual acuity testing: adaptation of the early treatment of diabetic retinopathy study testing protocol. Am J Ophthalmol. 2003;135(2):194–205.

    Article  PubMed  Google Scholar 

  7. Sun JK, Aiello LP, Stockman M, Cavallerano JD, Kopple A, Eagan S, et al. Effects of dilation on electronic-ETDRS visual acuity in diabetic patients. Invest Ophthalmol Vis Sci. 2009;50(4):1580–4.

    Article  PubMed  Google Scholar 

  8. Sandberg MA, Rosner B, Weigel-DiFranco C, McGee TL, Dryja TP, Berson EL. Disease course in patients with autosomal recessive retinitis pigmentosa due to the USH2A gene. Invest Ophthalmol Vis Sci. 2008;49(12):5532–9.

    Article  PubMed  Google Scholar 

  9. Tzu JH, Arguello T, Berrocal AM, Berrocal M, Weisman AD, Liu M, et al. Clinical and electrophysiologic characteristics of a large kindred with X-linked retinitis pigmentosa associated with the RPGR locus. Ophthalmic Genet. 2015;36(4):321–6.

    Article  CAS  PubMed  Google Scholar 

  10. Fischer MD, Fleischhauer JC, Gillies MC, Sutter FK, Helbig H, Barthelmes D. A new method to monitor visual field defects caused by photoreceptor degeneration by quantitative optical coherence tomography. Invest Ophthalmol Vis Sci. 2008;49(8):3617–21.

    Article  PubMed  Google Scholar 

  11. Ross DF, Fishman GA, Gilbert LD, Anderson RJ. Variability of visual field measurements in normal subjects and patients with retinitis pigmentosa. Arch Ophthalmol. 1984;102(7):1004–10.

    Article  CAS  PubMed  Google Scholar 

  12. Berson EL, Sandberg MA, Rosner B, Birch DG, Hanson AH. Natural course of retinitis pigmentosa over a three-year interval. Am J Ophthalmol. 1985;99(3):240–51.

    Article  CAS  PubMed  Google Scholar 

  13. Bittner AK, Iftikhar MH, Dagnelie G. Test-retest, within-visit variability of Goldmann visual fields in retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2011;52(11):8042–6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gerth C, Wright T, Heon E, Westall CA. Assessment of central retinal function in patients with advanced retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2007;48(3):1312–8.

    Article  PubMed  Google Scholar 

  15. Lodha N, Westall CA, Brent M, Abdolell M, Heon E. A modified protocol for the assessment of visual function in patients with retinitis pigmentosa. Adv Exp Med Biol. 2003;533:49–57.

    Article  CAS  PubMed  Google Scholar 

  16. Weleber RG, Smith TB, Peters D, Chegarnov EN, Gillespie SP, Francis PJ, et al. VFMA: topographic analysis of sensitivity data from full-field static perimetry. Transl Vis Sci Technol. 2015;4(2):14.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Bainbridge JW, Mehat MS, Sundaram V, Robbie SJ, Barker SE, Ripamonti C, et al. Long-term effect of gene therapy on Leber’s congenital amaurosis. N Engl J Med. 2015;372(20):1887–97.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Weleber RG, Pennesi ME, Wilson DJ, Kaushal S, Erker LR, Jensen L, et al. Results at 2 years after gene therapy for RPE65-Deficient leber congenital amaurosis and severe early-childhood-onset retinal dystrophy. Ophthalmology. 2016;123(7):1606–20.

    Article  PubMed  Google Scholar 

  19. Subash M, Comyn O, Samy A, Qatarneh D, Antonakis S, Mehat M, et al. The effect of multispot laser panretinal photocoagulation on retinal sensitivity and driving eligibility in patients with diabetic retinopathy. JAMA Ophthalmol. 2016;134(6):666–72.

    Article  PubMed  Google Scholar 

  20. Smith TB, Parker M, Steinkamp PN, Weleber RG, Smith N, Wilson DJ, et al. Structure-function modeling of optical coherence tomography and standard automated perimetry in the retina of patients with autosomal dominant retinitis pigmentosa. PLoS One. 2016;11(2):e0148022.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rangaswamy NV, Patel HM, Locke KG, Hood DC, Birch DG. A comparison of visual field sensitivity to photoreceptor thickness in retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2010;51(8):4213–9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hood DC, Ramachandran R, Holopigian K, Lazow M, Birch DG, Greenstein VC. Method for deriving visual field boundaries from OCT scans of patients with retinitis pigmentosa. Biomed Opt Express. 2011;2(5):1106–14.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Birch DG, Locke KG, Felius J, Klein M, Wheaton DK, Hoffman DR, et al. Rates of decline in regions of the visual field defined by frequency-domain optical coherence tomography in patients with RPGR-mediated X-linked retinitis pigmentosa. Ophthalmology. 2015;122(4):833–9.

    Article  PubMed  Google Scholar 

  24. Rohrschneider K, Bultmann S, Springer C. Use of fundus perimetry (microperimetry) to quantify macular sensitivity. Prog Retin Eye Res. 2008;27(5):536–48.

    Article  PubMed  Google Scholar 

  25. Chen FK, Patel PJ, Xing W, Bunce C, Egan C, Tufail AT, et al. Test-retest variability of microperimetry using the Nidek MP1 in patients with macular disease. Invest Ophthalmol Vis Sci. 2009;50(7):3464–72.

    Article  PubMed  Google Scholar 

  26. Battu R, Khanna A, Hegde B, Berendschot TT, Grover S, Schouten JS. Correlation of structure and function of the macula in patients with retinitis pigmentosa. Eye (Lond). 2015;29(7):895–901.

    Article  CAS  Google Scholar 

  27. Wu Z, Cunefare D, Chiu E, Luu CD, Ayton LN, Toth CA, et al. Longitudinal associations between microstructural changes and microperimetry in the early stages of age-related macular degeneration. Invest Ophthalmol Vis Sci. 2016;57(8):3714–22.

    Article  CAS  PubMed  Google Scholar 

  28. Strauss RW, Ho A, Munoz B, Cideciyan AV, Sahel JA, Sunness JS, et al. The natural history of the progression of atrophy secondary to stargardt disease (ProgStar) studies: design and baseline characteristics: ProgStar Report No. 1. Ophthalmology. 2016;123(4):817–28.

    Article  PubMed  Google Scholar 

  29. Acton JH, Greenstein VC. Fundus-driven perimetry (microperimetry) compared to conventional static automated perimetry: similarities, differences, and clinical applications. Can J Ophthalmol. 2013;48(5):358–63.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Dimopoulos IS, Tseng C, MacDonald IM. Microperimetry as an outcome measure in choroideremia trials: reproducibility and beyond. Invest Ophthalmol Vis Sci. 2016;57(10):4151–61.

    Article  PubMed  Google Scholar 

  31. McGuigan DB 3rd, Roman AJ, Cideciyan AV, Matsui R, Gruzensky ML, Sheplock R, et al. Automated light- and dark-adapted perimetry for evaluating retinitis pigmentosa: filling a need to accommodate multicenter clinical trials. Invest Ophthalmol Vis Sci. 2016;57(7):3118–28.

    Article  PubMed  CAS  Google Scholar 

  32. Collison FT, Fishman GA, McAnany JJ, Zernant J, Allikmets R. Psychophysical measurement of rod and cone thresholds in stargardt disease with full-field stimuli. Retina. 2014;34(9):1888–95.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Roman AJ, Cideciyan AV, Aleman TS, Jacobson SG. Full-field stimulus testing (FST) to quantify visual perception in severely blind candidates for treatment trials. Physiol Meas. 2007;28(8):N51–6.

    Article  PubMed  Google Scholar 

  34. Roman AJ, Schwartz SB, Aleman TS, Cideciyan AV, Chico JD, Windsor EA, et al. Quantifying rod photoreceptor-mediated vision in retinal degenerations: dark-adapted thresholds as outcome measures. Exp Eye Res. 2005;80(2):259–72.

    Article  CAS  PubMed  Google Scholar 

  35. Messias K, Jagle H, Saran R, Ruppert AD, Siqueira R, Jorge R, et al. Psychophysically determined full-field stimulus thresholds (FST) in retinitis pigmentosa: relationships with electroretinography and visual field outcomes. Doc Ophthalmol. 2013;127(2):123–9.

    Article  PubMed  Google Scholar 

  36. Klein M, Birch DG. Psychophysical assessment of low visual function in patients with retinal degenerative diseases (RDDs) with the Diagnosys full-field stimulus threshold (D-FST). Doc Ophthalmol. 2009;119(3):217–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fishman GA, Birch DG, Holder GE, Brigell MG. In: Ophthalmology AAO, editor. Electrophysiologic testing in disorders of the retina, optic nerve and visual pathway. 2nd ed. USA: Oxford University Press; 2001.

    Google Scholar 

  38. McCulloch DL, Marmor MF, Brigell MG, Hamilton R, Holder GE, Tzekov R, et al. ISCEV Standard for full-field clinical electroretinography (2015 update). Documenta ophthalmologica Advances in ophthalmology, vol. 130; 2015. p. 1):1–12.

    Google Scholar 

  39. Peachey NS, Ball SL. Electrophysiological analysis of visual function in mutant mice. Doc Ophthalmol. 2003;107(1):13–36.

    Article  PubMed  Google Scholar 

  40. Miyake Y, Shinoda K. Clinical electrophysiology. In: Sadda SR, editor. Retinal imaging and diagnostics. Retina. 1. 5th ed. Amsterdam: Elsevier; 2013. p. 202–26.

    Google Scholar 

  41. Wu DM, Fawzi AA. Abnormalities of cone and rod function. In: Schachat AP, Sadda SR, editors. Medical retina. Retina. 2. 5th ed. Amsterdam: Elsevier; 2013. p. 899–906.

    Google Scholar 

  42. Renner AB, Kellner U, Cropp E, Foerster MH. Dysfunction of transmission in the inner retina: incidence and clinical causes of negative electroretinogram. Graefes Arch Clin Exp Ophthalmol. 2006;244(11):1467–73.

    Article  PubMed  Google Scholar 

  43. Birch DG, Anderson JL, Fish GE. Yearly rates of rod and cone functional loss in retinitis pigmentosa and cone-rod dystrophy. Ophthalmology. 1999;106(2):258–68.

    Article  CAS  PubMed  Google Scholar 

  44. Hood DC, Bach M, Brigell M, Keating D, Kondo M, Lyons JS, et al. ISCEV standard for clinical multifocal electroretinography (mfERG) (2011 edition). Doc Ophthalmol. 2012;124(1):1–13.

    Article  PubMed  Google Scholar 

  45. Hood DC, Frishman LJ, Saszik S, Viswanathan S. Retinal origins of the primate multifocal ERG: implications for the human response. Invest Ophthalmol Vis Sci. 2002;43(5):1673–85.

    PubMed  Google Scholar 

  46. Dettoraki M, Moschos MM. The role of multifocal electroretinography in the assessment of drug-induced retinopathy: a review of the literature. Ophthalmic Res. 2016;56(4):169–77.

    Article  CAS  PubMed  Google Scholar 

  47. Mkrtchyan M, Lujan BJ, Merino D, Thirkill CE, Roorda A, Duncan JL. Outer retinal structure in patients with acute zonal occult outer retinopathy. Am J Ophthalmol. 2012;153(4):757–68, 68 e1

    Article  PubMed  Google Scholar 

  48. Wen Y, Klein M, Hood DC, Birch DG. Relationships among multifocal electroretinogram amplitude, visual field sensitivity, and SD-OCT receptor layer thicknesses in patients with retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2012;53(2):833–40.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Moon CH, Park TK, Ohn YH. Association between multifocal electroretinograms, optical coherence tomography and central visual sensitivity in advanced retinitis pigmentosa. Doc Ophthalmol. 2012;125(2):113–22.

    Article  PubMed  Google Scholar 

  50. Constable PA, Bach M, Frishman LJ, Jeffrey BG, Robson AG. International society for clinical electrophysiology of V. ISCEV standard for clinical electro-oculography (2017 update). Doc Ophthalmol. 2017;134(1):1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Meunier I, Senechal A, Dhaenens CM, Arndt C, Puech B, Defoort-Dhellemmes S, et al. Systematic screening of BEST1 and PRPH2 in juvenile and adult vitelliform macular dystrophies: a rationale for molecular analysis. Ophthalmology. 2011;118(6):1130–6.

    Article  PubMed  Google Scholar 

  52. Lee CS, Jun I, Choi SI, Lee JH, Lee MG, Lee SC, et al. A Novel BEST1 mutation in autosomal recessive bestrophinopathy. Invest Ophthalmol Vis Sci. 2015;56(13):8141–50.

    Article  CAS  PubMed  Google Scholar 

  53. Toto L, Boon CJ, Di Antonio L, Battaglia Parodi M, Mastropasqua R, Antonucci I, et al. BESTROPHINOPATHY: a spectrum of ocular abnormalities caused by the c.614T>C mutation in the BEST1 gene. Retina. 2016;36(8):1586–95.

    Article  CAS  PubMed  Google Scholar 

  54. Boon CJ, Klevering BJ, Leroy BP, Hoyng CB, Keunen JE, den Hollander AI. The spectrum of ocular phenotypes caused by mutations in the BEST1 gene. Prog Retin Eye Res. 2009;28(3):187–205.

    Article  CAS  PubMed  Google Scholar 

  55. Leung CK, Ye C, Weinreb RN, Cheung CY, Qiu Q, Liu S, et al. Retinal nerve fiber layer imaging with spectral-domain optical coherence tomography a study on diagnostic agreement with Heidelberg Retinal Tomograph. Ophthalmology. 2010;117(2):267–74.

    Article  PubMed  Google Scholar 

  56. Lavinsky F, Lavinsky D. Novel perspectives on swept-source optical coherence tomography. Int J Retina Vitreous. 2016;2:25.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Spaide RF. Enhanced depth imaging optical coherence tomography of retinal pigment epithelial detachment in age-related macular degeneration. Am J Ophthalmol. 2009;147(4):644–52.

    Article  PubMed  Google Scholar 

  58. Yang Z, Tatham AJ, Zangwill LM, Weinreb RN, Zhang C, Medeiros FA. Diagnostic ability of retinal nerve fiber layer imaging by swept-source optical coherence tomography in glaucoma. Am J Ophthalmol. 2015;159(1):193–201.

    Article  PubMed  Google Scholar 

  59. Zhang C, Tatham AJ, Medeiros FA, Zangwill LM, Yang Z, Weinreb RN. Assessment of choroidal thickness in healthy and glaucomatous eyes using swept source optical coherence tomography. PLoS One. 2014;9(10):e109683.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Srinivasan VJ, Monson BK, Wojtkowski M, Bilonick RA, Gorczynska I, Chen R, et al. Characterization of outer retinal morphology with high-speed, ultrahigh-resolution optical coherence tomography. Invest Ophthalmol Vis Sci. 2008;49(4):1571–9.

    Article  PubMed  Google Scholar 

  61. Lujan BJ, Roorda A, Knighton RW, Carroll J. Revealing Henle’s fiber layer using spectral domain optical coherence tomography. Invest Ophthalmol Vis Sci. 2011;52(3):1486–92.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Spaide RF, Curcio CA. Anatomical correlates to the bands seen in the outer retina by optical coherence tomography: literature review and model. Retina. 2011;31(8):1609–19.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Hood DC, Lazow MA, Locke KG, Greenstein VC, Birch DG. The transition zone between healthy and diseased retina in patients with retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2011;52(1):101–8.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lazow MA, Hood DC, Ramachandran R, Burke TR, Wang YZ, Greenstein VC, et al. Transition zones between healthy and diseased retina in choroideremia (CHM) and Stargardt disease (STGD) as compared to retinitis pigmentosa (RP). Invest Ophthalmol Vis Sci. 2011;52(13):9581–90.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Menghini M, Lujan BJ, Zayit-Soudry S, Syed R, Porco TC, Bayabo K, et al. Correlation of outer nuclear layer thickness with cone density values in patients with retinitis pigmentosa and healthy subjects. Invest Ophthalmol Vis Sci. 2014;56(1):372–81.

    Article  PubMed  Google Scholar 

  66. Hariri AH, Zhang HY, Ho A, Francis P, Weleber RG, Birch DG, et al. Quantification of ellipsoid zone changes in retinitis pigmentosa using en face spectral domain-optical coherence tomography. JAMA Ophthalmol. 2016;134(6):628–35.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Wang Q, Tuten WS, Lujan BJ, Holland J, Bernstein PS, Schwartz SD, et al. Adaptive optics microperimetry and OCT images show preserved function and recovery of cone visibility in macular telangiectasia type 2 retinal lesions. Invest Ophthalmol Vis Sci. 2015;56(2):778–86.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Sparrow JR, Yoon KD, Wu Y, Yamamoto K. Interpretations of fundus autofluorescence from studies of the bisretinoids of the retina. Invest Ophthalmol Vis Sci. 2010;51(9):4351–7.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Yung M, Klufas MA, Sarraf D. Clinical applications of fundus autofluorescence in retinal disease. Int J Retina Vitreous. 2016;2:12.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Spaide R. Autofluorescence from the outer retina and subretinal space: hypothesis and review. Retina. 2008;28(1):5–35.

    Article  PubMed  Google Scholar 

  71. Park SP, Siringo FS, Pensec N, Hong IH, Sparrow J, Barile G, et al. Comparison of fundus autofluorescence between fundus camera and confocal scanning laser ophthalmoscope-based systems. Ophthalmic Surg Lasers Imaging Retina. 2013;44(6):536–43.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Deli A, Moetteli L, Ambresin A, Mantel I. Comparison of fundus autofluorescence images acquired by the confocal scanning laser ophthalmoscope (488 nm excitation) and the modified Topcon fundus camera (580 nm excitation). Int Ophthalmol. 2013;33(6):635–43.

    Article  CAS  PubMed  Google Scholar 

  73. Jorzik JJ, Bindewald A, Dithmar S, Holz FG. Digital simultaneous fluorescein and indocyanine green angiography, autofluorescence, and red-free imaging with a solid-state laser-based confocal scanning laser ophthalmoscope. Retina. 2005;25(4):405–16.

    Article  PubMed  Google Scholar 

  74. Sharp PF, Manivannan A, Xu H, Forrester JV. The scanning laser ophthalmoscope--a review of its role in bioscience and medicine. Phys Med Biol. 2004;49(7):1085–96.

    Article  CAS  PubMed  Google Scholar 

  75. Trieschmann M, Spital G, Lommatzsch A, van Kuijk E, Fitzke F, Bird AC, et al. Macular pigment: quantitative analysis on autofluorescence images. Graefe’s Arch Clin Exp Ophthalmol. 2003;241(12):1006–12.

    Article  CAS  Google Scholar 

  76. Chen Y, Roorda A, Duncan JL. Advances in imaging of Stargardt disease. Adv Exp Med Biol. 2010;664:333–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Lois N, Halfyard AS, Bird AC, Holder GE, Fitzke FW. Fundus autofluorescence in Stargardt macular dystrophy-fundus flavimaculatus. Am J Ophthalmol. 2004;138(1):55–63.

    Article  PubMed  Google Scholar 

  78. Robson AG, Tufail A, Fitzke F, Bird AC, Moore AT, Holder GE, et al. Serial imaging and structure-function correlates of high-density rings of fundus autofluorescence in retinitis pigmentosa. Retina. 2011;31(8):1670–9.

    Article  PubMed  Google Scholar 

  79. Duncker T, Tabacaru MR, Lee W, Tsang SH, Sparrow JR, Greenstein VC. Comparison of near-infrared and short-wavelength autofluorescence in retinitis pigmentosa. Invest Ophthalmol Vis Sci. 2013;54(1):585–91.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Johnson RN, Fu AD, McDonald HR, Jumper JM, Ai E, Cunningham ET Jr, et al. fluorescein angiography: basic principles and interpretation. In: Sadda SR, editor. Retinal imaging and diagnostics. Retina. 1. 5th ed. Amsterdam: Elsevier; 2013. p. 2–50.

    Google Scholar 

  81. Cideciyan AV, Jacobson SG, Aleman TS, Gu D, Pearce-Kelling SE, Sumaroka A, et al. In vivo dynamics of retinal injury and repair in the rhodopsin mutant dog model of human retinitis pigmentosa. Proc Natl Acad Sci U S A. 2005;102(14):5233–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Staurenghi G, Bottoni F, Giani A. Clinical applications of diagnostic indocyanine green angiography. In: Sadda SR, editor. Retinal imaging and diagnostics. Retina. 1. 5th ed. Amsterdam: Elsevier; 2013. p. 51–81.

    Google Scholar 

  83. Dell’omo R, Wong R, Marino M, Konstantopoulou K, Pavesio C. Relationship between different fluorescein and indocyanine green angiography features in multiple evanescent white dot syndrome. Br J Ophthalmol. 2010;94(1):59–63.

    Article  PubMed  Google Scholar 

  84. Slakter JS, Giovannini A, Yannuzzi LA, Scassellati-Sforzolini B, Guyer DR, Sorenson JA, et al. Indocyanine green angiography of multifocal choroiditis. Ophthalmology. 1997;104(11):1813–9.

    Article  CAS  PubMed  Google Scholar 

  85. Jia Y, Tan O, Tokayer J, Potsaid B, Wang Y, Liu JJ, et al. Split-spectrum amplitude-decorrelation angiography with optical coherence tomography. Opt Express. 2012;20(4):4710–25.

    Article  PubMed  PubMed Central  Google Scholar 

  86. Kuehlewein L, Bansal M, Lenis TL, Iafe NA, Sadda SR, Bonini Filho MA, et al. Optical coherence tomography angiography of type 1 neovascularization in age-related macular degeneration. Am J Ophthalmol. 2015;160(4):739–48 e2.

    Article  PubMed  Google Scholar 

  87. Iafe NA, Phasukkijwatana N, Chen X, Sarraf D. Retinal capillary density and foveal avascular zone area are age-dependent: quantitative analysis using optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2016;57(13):5780–7.

    Article  CAS  PubMed  Google Scholar 

  88. Kang JW, Yoo R, Jo YH, Kim HC. Correlation of microvascular structures on optical coherence tomography angiography with visual acuity in retinal vein occlusion. Retina. 2017;37(9):1700–9.

    Article  PubMed  Google Scholar 

  89. Li M, Yang Y, Jiang H, Gregori G, Roisman L, Zheng F, et al. Retinal microvascular network and microcirculation assessments in high myopia. Am J Ophthalmol. 2017;174:56–67.

    Article  PubMed  Google Scholar 

  90. Gao SS, Patel RC, Jain N, Zhang M, Weleber RG, Huang D, et al. Choriocapillaris evaluation in choroideremia using optical coherence tomography angiography. Biomed Opt Express. 2017;8(1):48–56.

    Article  PubMed  Google Scholar 

  91. Battaglia Parodi M, Cicinelli MV, Rabiolo A, Pierro L, Gagliardi M, Bolognesi G, et al. Vessel density analysis in patients with retinitis pigmentosa by means of optical coherence tomography angiography. Br J Ophthalmol. 2017;101(4):428–32.

    Article  PubMed  Google Scholar 

  92. Roorda A, Romero-Borja F, Donnelly W III, Queener H, Hebert T, Campbell M. Adaptive optics scanning laser ophthalmoscopy. Opt Express. 2002;10(9):405–12.

    Article  PubMed  Google Scholar 

  93. Godara P, Dubis AM, Roorda A, Duncan JL, Carroll J. Adaptive optics retinal imaging: emerging clinical applications. Optom Vis Sci. 2010;87(12):930–41.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Roorda A, Duncan JL. Adaptive optics ophthalmoscopy. Annu Rev Vis Sci. 2015;1:19–50.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Rha J, Jonnal RS, Thorn KE, Qu J, Zhang Y, Miller DT. Adaptive optics flood-illumination camera for high speed retinal imaging. Opt Express. 2006;14(10):4552–69.

    Article  PubMed  Google Scholar 

  96. Gale MJ, Feng S, Titus HE, Smith TB, Pennesi ME. Interpretation of flood-illuminated adaptive optics images in subjects with retinitis pigmentosa. Adv Exp Med Biol. 2016;854:291–7.

    Article  CAS  PubMed  Google Scholar 

  97. Tojo N, Nakamura T, Fuchizawa C, Oiwake T, Hayashi A. Adaptive optics fundus images of cone photoreceptors in the macula of patients with retinitis pigmentosa. Clin Ophthalmol. 2013;7:203–10.

    PubMed  PubMed Central  Google Scholar 

  98. Feng S, Gale MJ, Fay JD, Faridi A, Titus HE, Garg AK, et al. Assessment of different sampling methods for measuring and representing macular cone density using flood-illuminated adaptive optics. Invest Ophthalmol Vis Sci. 2015;56(10):5751–63.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Gocho K, Sarda V, Falah S, Sahel JA, Sennlaub F, Benchaboune M, et al. Adaptive optics imaging of geographic atrophy. Invest Ophthalmol Vis Sci. 2013;54(5):3673–80.

    Article  PubMed  Google Scholar 

  100. Duncan JL, Zhang Y, Gandhi J, Nakanishi C, Othman M, Branham KE, et al. High-resolution imaging with adaptive optics in patients with inherited retinal degeneration. Invest Ophthalmol Vis Sci. 2007;48(7):3283–91.

    Article  PubMed  Google Scholar 

  101. Genead MA, Fishman GA, Rha J, Dubis AM, Bonci DM, Dubra A, et al. Photoreceptor structure and function in patients with congenital achromatopsia. Invest Ophthalmol Vis Sci. 2011;52(10):7298–308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Choi SS, Doble N, Hardy JL, Jones SM, Keltner JL, Olivier SS, et al. In vivo imaging of the photoreceptor mosaic in retinal dystrophies and correlations with visual function. Invest Ophthalmol Vis Sci. 2006;47(5):2080–92.

    Article  PubMed  Google Scholar 

  103. Wolfing JI, Chung M, Carroll J, Roorda A, Williams DR. High-resolution retinal imaging of cone-rod dystrophy. Ophthalmology. 2006;113(6):1019.e1.

    Article  PubMed  Google Scholar 

  104. Talcott KE, Ratnam K, Sundquist SM, Lucero AS, Lujan BJ, Tao W, et al. Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment. Invest Ophthalmol Vis Sci. 2011;52(5):2219–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Scoles D, Flatter JA, Cooper RF, Langlo CS, Robison S, Neitz M, et al. Assessing photoreceptor structure associated with ellipsoid zone disruptions visualized with optical coherence tomography. Retina. 2016;36(1):91–103.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Morgan JI, Hunter JJ, Merigan WH, Williams DR. The reduction of retinal autofluorescence caused by light exposure. Invest Ophthalmol Vis Sci. 2009;50(12):6015–22.

    Article  PubMed  Google Scholar 

  107. Morgan JI, Hunter JJ, Masella B, Wolfe R, Gray DC, Merigan WH, et al. Light-induced retinal changes observed with high-resolution autofluorescence imaging of the retinal pigment epithelium. Invest Ophthalmol Vis Sci. 2008;49(8):3715–29.

    Article  PubMed  Google Scholar 

  108. Sharma R, Williams DR, Palczewska G, Palczewski K, Hunter JJ. Two-Photon Autofluorescence Imaging Reveals Cellular Structures Throughout the Retina of the Living Primate Eye. Invest Ophthalmol Vis Sci. 2016;57(2):632–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Scoles D, Sulai YN, Langlo CS, Fishman GA, Curcio CA, Carroll J, et al. In vivo imaging of human cone photoreceptor inner segments. Invest Ophthalmol Vis Sci. 2014;55(7):4244–51.

    Article  PubMed  PubMed Central  Google Scholar 

  110. Sulai YN, Scoles D, Harvey Z, Dubra A. Visualization of retinal vascular structure and perfusion with a nonconfocal adaptive optics scanning light ophthalmoscope. J Opt Soc Am A Opt Image Sci Vis. 2014;31(3):569–79.

    Article  PubMed  PubMed Central  Google Scholar 

  111. Morgan JI. The fundus photo has met its match: optical coherence tomography and adaptive optics ophthalmoscopy are here to stay. Ophthalmic Physiol Opt. 2016;36(3):218–39.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Scoles D, Sulai YN, Dubra A. In vivo dark-field imaging of the retinal pigment epithelium cell mosaic. Biomed Opt Express. 2013;4(9):1710–23.

    Article  PubMed  PubMed Central  Google Scholar 

  113. Langlo CS, Erker LR, Parker M, Patterson EJ, Higgins BP, Summerfelt P, et al. Repeatability and longitudinal assessment of foveal cone structure in Cngb3-associated achromatopsia. Retina. 2017;37(10):1956–66.

    Article  PubMed  PubMed Central  Google Scholar 

  114. Langlo CS, Patterson EJ, Higgins BP, Summerfelt P, Razeen MM, Erker LR, et al. Residual foveal cone structure in CNGB3-associated achromatopsia. Invest Ophthalmol Vis Sci. 2016;57(10):3984–95.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Abozaid MA, Langlo CS, Dubis AM, Michaelides M, Tarima S, Carroll J. Reliability and repeatability of cone density measurements in patients with congenital achromatopsia. Adv Exp Med Biol. 2016;854:277–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Scoles D, Sulai YN, Cooper RF, Higgins BP, Johnson RD, Carroll J, et al. Photoreceptor inner segment morphology in best vitelliform macular dystrophy. Retina. 2017;37(4):741–8.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Zayit-Soudry S, Sippl-Swezey N, Porco TC, Lynch SK, Syed R, Ratnam K, Menghini M, Roorda A, Duncan JL. Reliability of cone spacing measures in eyes with inherited retinal degenerations. Invest Ophthalmol Vis Sci 2015; 56:6179–89.

    Google Scholar 

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Correspondence to Jacque L. Duncan .

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Lew, Y.J., Duncan, J.L. (2019). Diagnosis and Complementary Examinations. In: Zarbin, M., Singh, M., Casaroli-Marano, R. (eds) Cell-Based Therapy for Degenerative Retinal Disease . Stem Cell Biology and Regenerative Medicine. Humana Press, Cham. https://doi.org/10.1007/978-3-030-05222-5_11

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