Next Article in Journal
Maternal Right Ventricular and Left Atrial Function in Uncomplicated Twin Pregnancies: A Longitudinal Study
Next Article in Special Issue
Assessment of Corneal Angiography Filling Patterns in Corneal Neovascularization
Previous Article in Journal
Social Deprivation, Healthcare Access and Diabetic Foot Ulcer: A Narrative Review
Previous Article in Special Issue
OCT Analysis of Retinal Pigment Epithelium in Myopic Choroidal Neovascularization: Correlation Analysis with Different Treatments
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Editorial: Imaging in Ophthalmology

Manchester Royal Eye Hospital, Oxford Road, Manchester M13 9WL, UK
Department of Eye and Vision Science, University of Liverpool, Liverpool L69 3BX, UK
St Paul’s Eye Unit, Royal Liverpool University Hospital, Liverpool L69 3BX, UK
Eye Clinic, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25121 Brescia, Italy
ASST Civil Hospital of Brescia, 25123 Brescia, Italy
Author to whom correspondence should be addressed.
J. Clin. Med. 2022, 11(18), 5433;
Submission received: 5 September 2022 / Accepted: 13 September 2022 / Published: 15 September 2022
(This article belongs to the Special Issue Imaging in Ophthalmology—Volume I)
Over the last decade, ophthalmology has significantly benefited from advances in vivo non-invasive ophthalmic imaging techniques that play currently a fundamental role in the clinical assessment, diagnosis, management, and monitoring of a wide variety of conditions involving both the anterior and posterior segment [1,2,3,4,5,6]. Imaging technologies, including anterior and posterior segment optical coherence tomography (OCT), OCT angiography, wide-field retinal imaging, specular and confocal microscopy, corneal topography, and ocular ultrasound, have dramatically improved the morphological and functional evaluation of ocular structures, both in healthy and pathological eyes [1,2,3,4,5,6,7,8,9,10,11].
The detection of tissue microstructural changes, even at the subclinical level, can improve our ability to not only make an appropriate diagnosis, but also to elucidate pathogenetic mechanisms and to plan an appropriate management strategy for several pathological conditions. In this regard, for instance, an analysis of the retinal and corneal changes associated with SO tamponade provided important information on the potential effects of this compound on ocular tissues and facilitated the early detection of complications [12,13,14,15]. This aspect is of great clinical relevance, especially when considering that SO-related complications can be severe and potentially sight-threatening [16]. Furthermore, imaging techniques allow the identification of new biomarkers with different potential applications, including the detection and prediction of progression or responses to the treatment of common ocular diseases (e.g., age-related macular degeneration, AMD, diabetic retinopathy, DR, and myopic choroidal neovascularization) [17,18,19]; the early detection of systemic diseases, including hypertension [20] and multiple sclerosis [21]; the prediction of functional outcomes after surgical procedures [22]; or the detection of potential complications associated with systemic dugs [23].
With regard to the anterior segment, corneal topography and tomography have an established role in the accurate evaluation of the corneal shape as well as in the preoperative assessment for refractive and cataract surgery [24]. They are also relevant in the diagnosis, surgical planning, and long-term monitoring of various corneal pathologies, including keratoconus [25,26,27], ectatic corneal diseases, and pterygium or corneal scars [28,29]. It worth noting that keratometry measurements may significantly differ on the basis of the methodology used (e.g., anterior segment-OCT vs. Pentacam) [30]. Specular microscopy is a fundamental tool in the assessment of corneal and diagnosis and in the management of corneal endothelial disorders [31]. This technique can be also used to assess corneas stored in cold storage or in organoculture using an active storage machine [32]. Confocal microscopy allows for the detailed analysis of corneal nerves as well as for understanding their important role in the corneal structure and function in common corneal diseases such as keratoconus [33] but also as early markers of ocular involvement in systemic diseases, such as type 2 diabetes [34].
With regard to posterior segment, the advent of OCT and OCTA and their recent developments has dramatically improved the assessment of retinal and choroidal disorders. The diagnosis and the management of medical retinal diseases, including AMD, DR, and retinal vein occlusion, has been optimized by the use of these techniques, and the need for more invasive investigations, such as fluorescein angiography, has decreased [35,36,37]. This shift has also been seen in the anterior segment [38,39,40,41,42]. The evaluation and management of vitreoretinal interface diseases have particularly benefited from these imaging techniques, which allow for detailed structural analysis of the retinal tissues and the identification of multiple anatomical findings for classification [43,44], differential diagnosis [45,46,47], surgical planning [48,49,50], prognosis [45,49,51], and long-term monitoring [50,52]. It has been recently suggested that retromode imaging modalities, which rely on confocal scanning laser ophthalmoscopic technology, may be a promising additional tool for the assessment of ERMs [53].
The possibility of combining different imaging modalities can optimize the processes of differential diagnosis, particularly in diseases sharing multiple common clinical aspects, including macular oedema of different etiologies [54] or inflammatory pathologies [55,56,57], as shown for chorioretinal lesions associated with Mycobacterium (M.) chimaera, M. tuberculosis, and other ocular granulomatous infectious diseases [58].
Finally, there is a growing interest in the use of artificial intelligence (AI) and deep learning in ophthalmology due to the promising results achieved in the detection of common ocular diseases such as AMD, diabetic retinopathy, and glaucoma, and the potential applications for screening, diagnosis, and monitoring of these conditions [59]. The high accuracy of a computer-aided diagnosis algorithm using deep convolutional neural networks in recognizing and classifying high levels of myopia through fundus images has also been reported [60]. Interestingly, an AI system based on transfer learning and deep learning has been successfully applied for meibography analysis [61].
In this issue, we aimed to highlight the multiple potential applications of imaging techniques in ophthalmology, and we hope that this will be appreciated by readers.


This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Fogel-Levin, M.; Sadda, S.R.; Rosenfeld, P.J.; Waheed, N.; Querques, G.; Freund, B.K.; Sarraf, D. Advanced retinal imaging and applications for clinical practice: A consensus review. Surv. Ophthalmol. 2022, 67, 1373–1390, Online ahead of print. [Google Scholar] [CrossRef] [PubMed]
  2. Romano, V.; Steger, B.; Ahmad, M.; Coco, G.; Pagano, L.; Ahmad, S.; Zhao, Y.; Zheng, Y.; Kaye, S.B. Imaging of vascular abnormalities in ocular surface disease. Surv. Ophthalmol. 2021, 67, 31–51. [Google Scholar] [CrossRef] [PubMed]
  3. Hood, D.C.; La Bruna, S.; Tsamis, E.; Thakoor, K.A.; Rai, A.; Leshno, A.; de Moraes, C.G.V.; Cioffi, G.A.; Liebmann, J.M. Detecting glaucoma with only OCT: Implications for the clinic, research, screening and AI development. Prog. Retin. Eye Res. 2022, 90, 101052. [Google Scholar] [CrossRef] [PubMed]
  4. Romano, V.; Tey, A.; Hill, N.M.; Ahmad, S.; Britten, C.; Batterbury, M.; Willoughby, C.; Kaye, S.B. Influence of graft size on graft survival following Descemet stripping automated endothelial keratoplasty. Br. J. Ophthalmol. 2015, 99, 784–788. [Google Scholar] [CrossRef]
  5. Borroni, D.; Romano, V.; Kaye, S.B.; Somerville, T.; Napoli, L.; Fasolo, A.; Gallon, P.; Ponzin, D.; Esposito, A.; Ferrari, S. Metagenomics in ophthalmology: Current findings and future prospectives. BMJ Open Ophthalmol. 2019, 4, e000248. [Google Scholar] [CrossRef]
  6. Liu, S.; Romano, V.; Steger, B.; Kaye, S.B.; Hamill, K.J.; Willoughby, C.E. Gene-based antiangiogenic applications for corneal neovascularization. Surv. Ophthalmol. 2018, 63, 193–213. [Google Scholar] [CrossRef]
  7. Ren, J.; Gao, X.; Chen, L.; Lin, H.; Liu, Y.; Zhou, Y.; Liao, Y.; Xie, C.; Zuo, C.; Lin, M. Characteristics of the Ciliary Body in Healthy Chinese Subjects Evaluated by Radial and Transverse Imaging of Ultrasound Biometric Microscopy. J. Clin. Med. 2022, 11, 3696. [Google Scholar] [CrossRef]
  8. Kim, M.-S.; Lim, H.-B.; Lee, W.-H.; Won, Y.-K.; Nam, K.-Y.; Kim, J.-Y. Wide-Field Swept-Source OCT Analysis of Interocular Symmetry of Choroidal Thickness in Subjects with Uncomplicated Pachychoroid. J. Clin. Med. 2021, 10, 4253. [Google Scholar] [CrossRef]
  9. Zhang, Z.; Yao, J.; Chang, S.; Kanclerz, P.; Khoramnia, R.; Deng, M.; Wang, X. Incidence and Risk Factors for Berger’s Space Development after Uneventful Cataract Surgery: Evidence from Swept-Source Optical Coherence Tomography. J. Clin. Med. 2022, 11, 3580. [Google Scholar] [CrossRef]
  10. Parekh, M.; Leon, P.; Ruzza, A.; Borroni, D.; Ferrari, S.; Ponzin, D.; Romano, V. Graft detachment and rebubbling rate in Descemet membrane endothelial keratoplasty. Surv. Ophthalmol. 2018, 63, 245–250. [Google Scholar] [CrossRef]
  11. Romano, V.; Steger, B.; Myneni, J.; Batterbury, M.; Willoughby, C.E.; Kaye, S.B. Preparation of ultrathin grafts for Descemet-stripping endothelial keratoplasty with a single microkeratome pass. J. Cataract Refract. Surg. 2017, 43, 12–15. [Google Scholar] [CrossRef] [PubMed]
  12. Ferrara, M.; Coco, G.; Sorrentino, T.; Jasani, K.M.; Moussa, G.; Morescalchi, F.; Dhawahir-Scala, F.; Semeraro, F.; Steel, D.H.W.; Romano, V.; et al. Retinal and corneal changes associated with intraocular silicone oil tamponade. J. Clin. Med. 2022, 11, 5234. [Google Scholar] [CrossRef] [PubMed]
  13. Romano, V.; Angi, M.; Scotti, F.; del grosso, R.; Romano, D.; Semeraro, F.; Vinciguerra, P.; Costagliola, C.; Romano, M.R. Inflammation and macular oedema after pars plana vitrectomy. Mediat. Inflamm. 2013, 2013, 971758. [Google Scholar] [CrossRef]
  14. Morescalchi, F.; Costagliola, C.; Duse, S.; Gambicorti, E.; Parolini, B.; Arcidiacono, B.; Romano, M.R.; Semeraro, F. Heavy silicone oil and intraocular inflammation. BioMed Res. Int. 2014, 2014, 574825. [Google Scholar] [CrossRef] [PubMed]
  15. Romano, M.R.; Vallejo-Garcia, J.L.; Parmeggiani, F.; Romano, V.; Vinciguerra, P. Interaction between perfluorcarbon liquid and heavy silicone oil: Risk factor for ‘sticky oil’ formation. Curr. Eye Res. 2012, 37, 563–566. [Google Scholar] [CrossRef]
  16. Romano, M.R.; Ferrara, M.; Nepita, I.; D’Amato Tothova, J.; Giacometti Schieroni, A.; Reami, D.; Mendichi, R.; Liggieri, L.; Repetto, R. Biocompatibility of intraocular liquid tamponade agents: An update. Eye 2021, 35, 2699–2713. [Google Scholar] [CrossRef] [PubMed]
  17. Schreur, V.; de Breuk, A.; Venhuizen, F.G.; Sánchez, C.I.; Tack, C.J.; Klevering, B.J.; de Jong, E.K.; Hoyng, C.B. Retinal Hyperreflective Foci in Type 1 Diabetes Mellitus. Retina 2020, 40, 1565–1573. [Google Scholar] [CrossRef]
  18. Sitnilska, V.; Enders, P.; Cursiefen, C.; Fauser, S.; Altay, L. Association of Imaging Biomarkers and Local Activation of Complement in Aqueous Humor of Patients with Early Forms of Age-Related Macular Degeneration. Graefes Arch. Clin. Exp. Ophthalmol. 2021, 259, 623–632. [Google Scholar] [CrossRef]
  19. Allegrini, D.; Vezzola, D.; Borgia, A.; Raimondi, R.; Sorrentino, T.; Tripepi, D.; Stradiotto, E.; Alì, M.; Montesano, G.; Romano, M.R. OCT Analysis of Retinal Pigment Epithelium in Myopic Choroidal Neovascularization: Correlation Analysis with Different Treatments. J. Clin. Med. 2022, 11, 5023. [Google Scholar] [CrossRef]
  20. Niro, A.; Sborgia, G.; Lampignano, L.; Giuliani, G.; Castellana, F.; Zupo, R.; Bortone, I.; Puzo, P.; Pascale, A.; Pastore, V.; et al. Association of Neuroretinal Thinning andMicrovascular Changes with Hypertension in an Older Population in Southern Italy. J. Clin. Med. 2022, 11, 1098. [Google Scholar] [CrossRef]
  21. Drobnjak Nes, D.; Berg-Hansen, P.; de Rodez Benavent, S.A.; H gest l, E.A.; Beyer, M.K.; Rinker, D.A.; Veiby, N.; Karabeg, M.; Petrovski, B.; Celius, E.G.; et al. Exploring Retinal Blood Vessel Diameters as Biomarkers in Multiple Sclerosis. J. Clin. Med. 2022, 11, 3109. [Google Scholar] [CrossRef] [PubMed]
  22. Yoon, J.; Sung, K.R.; Shin, J.W. Changes in Peripapillary and Macular Vessel Densities and Their Relationship with Visual Field Progression after Trabeculectomy. J. Clin. Med. 2021, 10, 5862. [Google Scholar] [CrossRef] [PubMed]
  23. Mencucci, R.; Cennamo, M.; Alonzo, L.; Senni, C.; Vagge, A.; Ferro Desideri, L.; Scorcia, V.; Giannaccare, G. Corneal Findings Associated to Belantamab-Mafodotin (Belamaf) Use in a Series of Patients Examined Longitudinally by Means of Advanced Corneal Imaging. J. Clin. Med. 2022, 11, 2884. [Google Scholar] [CrossRef] [PubMed]
  24. Kanclerz, P.; Khoramnia, R.; Wang, X. Current Developments in Corneal Topography and Tomography. Diagnostics 2021, 11, 1466. [Google Scholar] [CrossRef] [PubMed]
  25. Napolitano, P.; Tranfa, F.; D’Andrea, L.; Caruso, C.; Rinaldi, M.; Mazzucco, A.; Ciampa, N.; Melenzane, A.; Costagliola, C. Topographic Outcomes in Keratoconus Surgery: Epi-on versus Epi-off Iontophoresis Corneal Collagen Cross-Linking. J. Clin. Med. 2022, 11, 1785. [Google Scholar] [CrossRef]
  26. Gasser, T.; Romano, V.; Seifarth, C.; Bechrakis, N.E.; Kaye, S.B.; Steger, B. Morphometric characterisation of pterygium associated with corneal stromal scarring using high-resolution anterior segment optical coherence tomography. Br. J. Ophthalmol. 2017, 101, 660–664. [Google Scholar] [CrossRef]
  27. Brunner, M.; Czanner, G.; Vinciguerra, R.; Romano, V.; Ahmad, S.; Batterbury, M.; Britten, C.; Willoughby, C.E.; Kaye, S.B. Improving precision for detecting change in the shape of the cornea in patients with keratoconus. Sci Rep. 2018, 8, 12345. [Google Scholar] [CrossRef]
  28. Vinciguerra, P.; Mencucci, R.; Romano, V.; Spoerl, E.; Camesasca, F.I.; Favuzza, E.; Azzolini, C.; Mastropasqua, R.; Vinciguerra, R. Imaging mass spectrometry by matrix-assisted laser desorption/ionization and stress-strain measurements in iontophoresis transepithelial corneal collagen cross-linking. BioMed Res Int. 2014, 2014, 404587. [Google Scholar] [CrossRef]
  29. Lanza, M.; Cennamo, M.; Iaccarino, S.; Romano, V.; Bifani, M.; Irregolare, C.; Lanza, A. Evaluation of corneal deformation analyzed with a Scheimpflug based device. Cont. Lens Anterior Eye 2015, 38, 89–93. [Google Scholar] [CrossRef]
  30. Pérez-Bartolomé, F.; Rocha-De-Lossada, C.; Sánchez-González, J.-M.; Feu-Basilio, S.; Torras-Sanvicens, J.; Peraza-Nieves, J. Anterior-Segment Swept-Source Ocular Coherence Tomography and Scheimpflug Imaging Agreement for Keratometry and Pupil Measurements in Healthy Eyes. J. Clin. Med. 2021, 10, 5789. [Google Scholar] [CrossRef]
  31. Chaurasia, S.; Vanathi, M. Specular microscopy in clinical practice. Indian J. Ophthalmol. 2021, 69, 517–524. [Google Scholar] [CrossRef]
  32. Garcin, T.; Crouzet, E.; Perrache, C.; Lepine, T.; Gain, P.; Thuret, G. Specular Microscopy of Human Corneas Stored in an Active Storage Machine. J. Clin. Med. 2022, 11, 3000. [Google Scholar] [CrossRef]
  33. Teo, A.W.J.; Mansoor, H.; Sim, N.; Lin, M.T.-Y.; Liu, Y.-C. In Vivo Confocal Microscopy Evaluation in Patients with Keratoconus. J. Clin. Med. 2022, 11, 393. [Google Scholar] [CrossRef] [PubMed]
  34. dell’Omo, R.; Cifariello, F.; De Turris, S.; Romano, V.; Di Renzo, F.; Di Taranto, D.; Coclite, G.; Agnifili, L.; Mastropasqua, L.; Costagliola, C. Confocal microscopy of corneal nerve plexus as an early marker of eye involvement in patients with type 2 diabetes. Diabetes Res. Clin. Pract. 2018, 142, 393–400. [Google Scholar] [CrossRef] [PubMed]
  35. Tran, K.; Pakzad-Vaezi, K. Multimodal imaging of diabetic retinopathy. Curr. Opin. Ophthalmol. 2018, 29, 566–575. [Google Scholar] [CrossRef] [PubMed]
  36. Guymer, R.; Wu, Z. Age-related macular degeneration (AMD): More than meets the eye. The role of multimodal imaging in today’s management of AMD-A review. Clin. Exp. Ophthalmol. 2020, 48, 983–995. [Google Scholar] [CrossRef]
  37. Tsai, G.; Banaee, T.; Conti, F.F.; Singh, R.P. Optical Coherence Tomography Angiography in Eyes with Retinal Vein Occlusion. J. Ophthalmic Vis. Res. 2018, 13, 315–332. [Google Scholar] [CrossRef]
  38. Duker, J.S.; Kaiser, P.K.; Binder, S.; de Smet, M.D.; Gaudric, A.; Reichel, E.; Sadda, S.R.; Sebag, J.; Spaide, R.F.; Stalmans, P. The International Vitreomacular Traction Study Group classification of vitreomacular adhesion, traction, and macular hole. Ophthalmology 2013, 120, 2611–2619. [Google Scholar] [CrossRef]
  39. Brunner, M.; Romano, V.; Steger, B.; Vinciguerra, R.; Lawman, S.; Williams, B.; Hicks, N.; Czanner, G.; Zheng, Y.; Willoughby, C.E.; et al. Imaging of Corneal Neovascularization: Optical Coherence Tomography Angiography and Fluorescence Angiography. Investig. Ophthalmol. Vis. Sci. 2018, 59, 1263–1269. [Google Scholar] [CrossRef]
  40. Brunner, M.; Steger, B.; Romano, V.; Hodson, M.; Zheng, Y.; Heimann, H.; Kaye, S.B. Identification of Feeder Vessels in Ocular Surface Neoplasia Using Indocyanine Green Angiography. Curr. Eye Res. 2018, 43, 163–169. [Google Scholar] [CrossRef]
  41. Romano, V.; Steger, B.; Brunner, M.; Ahmad, S.; Willoughby, C.E.; Kaye, S.B. Method for Angiographically Guided Fine-Needle Diathermy in the Treatment of Corneal Neovascularization. Cornea 2016, 35, 1029–1032. [Google Scholar] [CrossRef] [PubMed]
  42. Romano, V.; Steger, B.; Zheng, Y.; Ahmad, S.; Willoughby, C.E.; Kaye, S.B. Angiographic and In Vivo Confocal Microscopic Characterization of Human Corneal Blood and Presumed Lymphatic Neovascularization: A Pilot Study. Cornea 2015, 34, 1459–1465. [Google Scholar] [CrossRef] [PubMed]
  43. Palme, C.; Wanner, A.; Romano, V.; Franchi, A.; Haas, G.; Kaye, S.B.; Steger, B. Indocyanine Green Angiographic Assessment of Conjunctival Melanocytic Disorders. Cornea 2021, 40, 1519–1524. [Google Scholar] [CrossRef] [PubMed]
  44. Hubschman, J.P.; Govetto, A.; Spaide, R.F.; Schumann, R.; Steel, D.; Figueroa, M.S.; Sebag, J.; Gaudric, A.; Staurenghi, G.; Haritoglou, C.; et al. Optical coherence tomography-based consensus definition for lamellar macular hole. Br. J. Ophthalmol. 2020, 104, 1741–1747. [Google Scholar] [CrossRef] [PubMed]
  45. Romano, M.R.; Allegrini, D.; Della Guardia, C.; Schiemer, S.; Baronissi, I.; Ferrara, M.; Cennamo, G. Vitreous and intraretinal macular changes in diabetic macular edema with and without tractional components. Graefes Arch. Clin. Exp. Ophthalmol. 2019, 257, 1–8. [Google Scholar] [CrossRef]
  46. Romano, M.R.; Ilardi, G.; Ferrara, M.; Cennamo, G.; Allegrini, D.; Pafundi, P.C.; Costagliola, C.; Staibano, S.; Cennamo, G. Intraretinal changes in idiopathic versus diabetic epiretinal membranes after macular peeling. PLoS ONE 2018, 13, e0197065. [Google Scholar] [CrossRef]
  47. Govetto, A.; Sarraf, D.; Hubschman, J.P.; Tadayoni, R.; Couturier, A.; Chehaibou, I.; Au, A.; Grondin, C.; Virgili, G.; Romano, M.R. Distinctive Mechanisms and Patterns of Exudative Versus Tractional Intraretinal Cystoid Spaces as Seen With Multimodal Imaging. Am. J. Ophthalmol. 2020, 212, 43–56. [Google Scholar] [CrossRef]
  48. Govetto, A.; Hubschman, J.P.; Sarraf, D.; Figueroa, M.S.; Bottoni, F.; dell’Omo, R.; Curcio, C.A.; Seidenari, P.; Delledonne, G.; Gunzenhauser, R.; et al. The role of Müller cells in tractional macular disorders: An optical coherence tomography study and physical model of mechanical force transmission. Br. J. Ophthalmol. 2020, 104, 466–472. [Google Scholar] [CrossRef]
  49. Chua, P.Y.; Sandinha, M.T.; Steel, D.H. Idiopathic epiretinal membrane: Progression and timing of surgery. Eye 2022, 36, 495–503. [Google Scholar] [CrossRef]
  50. Yang, J.M.; Choi, S.U.; Kim, Y.J.; Kim, R.; Yon, D.K.; Lee, S.W.; Shin, J.I.; Lee, J.Y.; Kim, J.G. Association between epiretinal membrane, epiretinal proliferation, and prognosis of full-thickness macular hole closure. Retina 2022, 42, 46–54. [Google Scholar] [CrossRef]
  51. Romano, M.R.; Rossi, T.; Borgia, A.; Catania, F.; Sorrentino, T.; Ferrara, M. Management of refractory and recurrent macular holes: A comprehensive review. Surv. Ophthalmol. 2022, 67, 908–931. [Google Scholar] [CrossRef] [PubMed]
  52. Romano, M.R.; Comune, C.; Ferrara, M.; Cennamo, G.; De Cillà, S.; Toto, L.; Cennamo, G. Retinal Changes Induced by Epiretinal Tangential Forces. J. Ophthalmol. 2015, 2015, 372564. [Google Scholar] [CrossRef] [PubMed]
  53. Savastano, A.; Ripa, M.; Savastano, M.C.; Caporossi, T.; Bacherini, D.; Kilian, R.; Rizzo, C.; Rizzo, S. Retromode Imaging Modality of Epiretinal Membranes. J. Clin. Med. 2022, 11, 3936. [Google Scholar] [CrossRef]
  54. Dysli, M.; Rückert, R.; Munk, M.R. Differentiation of Underlying Pathologies of Macular Edema Using Spectral Domain Optical Coherence Tomography (SD-OCT). Ocul. Immunol. Inflamm. 2019, 27, 474–483. [Google Scholar] [CrossRef]
  55. Baharani, A.; Errera, M.H.; Jhingan, M.; Samanta, A.; Agarwal, A.; Singh, S.R.; Reddy, P.R.R.; Grewal, D.S.; Chhablani, J. Choroidal imaging in uveitis: An update. Surv. Ophthalmol. 2022, 67, 965–990. [Google Scholar] [CrossRef]
  56. Ferrara, M.; Eggenschwiler, L.; Stephenson, A.; Montieth, A.; Nakhoul, N.; Araùjo-Miranda, R.; Foster, C.S. The Challenge of Pediatric Uveitis: Tertiary Referral Center Experience in the United States. The Challenge of Pediatric Uveitis: Tertiary Referral Center Experience in the United States. Ocul. Immunol. Inflamm. 2019, 27, 410–417. [Google Scholar] [CrossRef] [PubMed]
  57. Thomas, A.S.; Lin, P. Multimodal imaging in infectious and noninfectious intermediate, posterior and panuveitis. Curr. Opin. Ophthalmol. 2021, 32, 169–182. [Google Scholar] [CrossRef]
  58. Zweifel, S.A.; Foa, N.; Wiest, M.R.J.; Carnevali, A.; Zaluska-Ogryzek, K.; Rejdak, R.; Toro, M.D. Differences between Mycobacterium chimaera and tuberculosis Using Ocular Multimodal Imaging: A Systematic Review. J. Clin. Med. 2021, 10, 4880. [Google Scholar] [CrossRef]
  59. Williams, B.M.; Borroni, D.; Liu, R.; Zhao, Y.; Zhang, J.; Lim, J.; Ma, B.; Romano, V.; Qi, H.; Ferdousi, M.; et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: A development and validation study. Diabetologica 2020, 63, 419–430. [Google Scholar] [CrossRef]
  60. Wan, C.; Li, H.; Cao, G.-F.; Jiang, Q.; Yang, W.-H. An Artificial Intelligent Risk Classification Method of High Myopia Based on Fundus Images. J. Clin. Med. 2021, 10, 4488. [Google Scholar] [CrossRef]
  61. Zhang, Z.; Lin, X.; Yu, X.; Fu, Y.; Chen, X.; Yang, W.; Dai, Q. Meibomian Gland Density: An Effective Evaluation Index of Meibomian Gland Dysfunction Based on Deep Learning and Transfer Learning. J. Clin. Med. 2022, 11, 2396. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ferrara, M.; Zheng, Y.; Romano, V. Editorial: Imaging in Ophthalmology. J. Clin. Med. 2022, 11, 5433.

AMA Style

Ferrara M, Zheng Y, Romano V. Editorial: Imaging in Ophthalmology. Journal of Clinical Medicine. 2022; 11(18):5433.

Chicago/Turabian Style

Ferrara, Mariantonia, Yalin Zheng, and Vito Romano. 2022. "Editorial: Imaging in Ophthalmology" Journal of Clinical Medicine 11, no. 18: 5433.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop