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Home / Equine Research Bank / Br Med Bull / Ophthalmic imaging.
British medical bulletin2014; 111(1); 77-88; doi: 10.1093/bmb/ldu022

Ophthalmic imaging.

Abstract: The last two decades have seen a revolution in ophthalmic imaging. In this review we present an overview of the breadth of ophthalmic imaging modalities in use today and describe how the role of ophthalmic imaging has changed from documenting abnormalities visible on clinical examination to the detection of clinically silent abnormalities which can lead to an earlier and more precise diagnosis. Methods: This review is based on published literature in the fields of ophthalmic imaging and with focus on most commonly used imaging modalities. Results: New imaging techniques enable non-invasive evaluation of ocular structures at a resolution of a few micrometres. This has led to a re-evaluation of diagnostic criteria for ocular disease, which were previously defined by clinical findings without significant reference to imaging. Results: Lack of formal training and clinical guidelines regarding use of new imaging techniques in diagnosing and monitoring various ocular conditions. Lack of large normative databases and interchangeability issues between different commercial machines can hinder the detection of disease progression. Conclusions: Imaging devices are being constantly refined with improved image capture and image analysis tools. Conclusions: Clinical applications of new techniques and devices have yet to be determined using systematic scientific research methods.
© The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Publication Date: 2014-08-18 PubMed ID: 25139430DOI: 10.1093/bmb/ldu022Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This article discusses the advancements in veterinary imaging techniques, particularly ultrasonography, CT, and MRI, their costs, availability, and potential pitfalls such as imaging artifacts.

Advanced Imaging Techniques in Veterinary Medicine

Ultrasonography, CT, and MRI are amongst the advanced imaging modalities that have revolutionized the field of veterinary medicine. These technologies provide detailed images of intraocular structures and surrounding soft tissues, which are essential in diagnosing and managing various animal health conditions, especially eye disorders in horses.

  • The increased availability and cost-effectiveness of Ultrasonography make it a preferred choice for many practitioners, referral centers, and academic institutions. It is noninvasive and offers rapid and detailed examination of internal structures in opaque eyes, a feature particularly useful in equine veterinary care. mobile specialist ultrasonographers offer additional support to these practitioners.
  • CT and MRI, although costlier and less widely available than ultrasonography, offer better image quality with their cross-sectional imaging capabilities.

Financial Considerations and Equipment Availability

Despite the improved quality of CT and MRI images, their high cost and restricted availability pose significant challenges. They are predominantly available at referral centers and academic institutions due to their high costs.

  • Out of the two, CT is more commonly used for equine disorders as it is relatively more affordable and thus more widely available.
  • Both CT and MRI procedures need general anesthesia which increases overall cost and presents additional health risks in critical patients.

Understanding Imaging Artifacts

In order to correctly interpret the images obtained, an understanding of potential imaging artifacts is crucial. Different imaging modalities can produce unique types of artifacts, which if unrecognized, can lead to misinterpretations.

  • The author emphasizes the need for practitioners to have a thorough understanding of normal animal anatomy, aberrant tissue patterns, and varying types of imaging artifacts in order to accurately interpret imaging results and avoid diagnostic errors.

Cite This Article

APA
Ilginis T, Clarke J, Patel PJ. (2014). Ophthalmic imaging. Br Med Bull, 111(1), 77-88. https://doi.org/10.1093/bmb/ldu022

Publication

British medical bulletin
ISSN: 1471-8391
NlmUniqueID: 0376542
Country: England
Language: English
Volume: 111
Issue: 1
Pages: 77-88

Researcher Affiliations

Ilginis, Tomas
  • NIHR Moorfields Biomedical Research Centre (Moorfields Eye Hospital and UCL Institute of Ophthalmology), London, UK.
Clarke, Jonathan
  • NIHR Moorfields Biomedical Research Centre (Moorfields Eye Hospital and UCL Institute of Ophthalmology), London, UK.
Patel, Praveen J
  • NIHR Moorfields Biomedical Research Centre (Moorfields Eye Hospital and UCL Institute of Ophthalmology), London, UK praveen.patel@moorfields.nhs.uk.

MeSH Terms

  • Eye Diseases / diagnosis
  • Fluorescein Angiography / methods
  • Fundus Oculi
  • Humans
  • Ophthalmoscopy / methods
  • Optical Imaging / methods
  • Optical Imaging / trends
  • Tomography, Optical Coherence / methods

Citations

This article has been cited 16 times.
  1. Xu Y, Kang D, Shi D, Tham YC, Grzybowski A, Jin K. Leveraging ChatGPT for Report Error Audit: An Accuracy-Driven and Cost-Efficient Solution for Ophthalmic Imaging Reports. Ophthalmol Ther 2025 Dec;14(12):3007-3020.
    doi: 10.1007/s40123-025-01248-2pubmed: 41026447google scholar: lookup
  2. Lee HJ, Lee TG, Doh I, Lee SW. Design and application of a realistic and multifunctional retinal phantom for standardizing ophthalmic imaging systems. Commun Eng 2025 Jul 26;4(1):134.
    doi: 10.1038/s44172-025-00475-6pubmed: 40715531google scholar: lookup
  3. Alibhai AY, Durbin MK, Hou H, Sadda SR, Marcus DM, You TT, El-Nimri NW, Huebschmann L, Waheed NK. Clinical Performance of Semi-Automated Spectral-Domain Optical Coherence Tomography Angiography. J Clin Med 2024 Oct 22;13(21).
    doi: 10.3390/jcm13216301pubmed: 39518440google scholar: lookup
  4. Zhang H, Zhang H, Jiang M, Li J, Li J, Zhou H, Song X, Fan X. Radiomics in ophthalmology: a systematic review. Eur Radiol 2025 Jan;35(1):542-557.
    doi: 10.1007/s00330-024-10911-4pubmed: 39033472google scholar: lookup
  5. Mbagwu M, Chu Z, Borkar D, Koshta A, Shah N, Torres A, Kalvaria H, Lum F, Leng T. Feasibility of cross-vendor linkage of ophthalmic images with electronic health record data: an analysis from the IRIS Registry(®). JAMIA Open 2024 Apr;7(1):ooae005.
    doi: 10.1093/jamiaopen/ooae005pubmed: 38283883google scholar: lookup
  6. Wan Zaki WMD, Abdul Mutalib H, Ramlan LA, Hussain A, Mustapha A. Towards a Connected Mobile Cataract Screening System: A Future Approach. J Imaging 2022 Feb 10;8(2).
    doi: 10.3390/jimaging8020041pubmed: 35200743google scholar: lookup
  7. Jeong Y, Hong YJ, Han JH. Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics (Basel) 2022 Jan 6;12(1).
    doi: 10.3390/diagnostics12010134pubmed: 35054301google scholar: lookup
  8. Vodrahalli K, Filipkowski M, Chen T, Zou J, Liao YJ. Predicting Visuo-Motor Diseases From Eye Tracking Data. Pac Symp Biocomput 2022;27:242-253.
    pubmed: 34890153
  9. Zhang Z, Tang Q, Wang Q, Nie F, Sun L, Luo D, Chen W, Ding X. HODD: A Manually Curated Database of Human Ophthalmic Diseases with Symptom Characteristics and Genetic Variants Towards Facilitating Quick and Definite Diagnosis. Interdiscip Sci 2022 Jun;14(2):385-393.
    doi: 10.1007/s12539-021-00494-9pubmed: 34846641google scholar: lookup
  10. Sil Kar S, Sevgi DD, Dong V, Srivastava SK, Madabhushi A, Ehlers JP. Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings. IEEE J Transl Eng Health Med 2021;9:1000113.
    doi: 10.1109/JTEHM.2021.3096378pubmed: 34350068google scholar: lookup
  11. Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration. Ophthalmol Retina 2017 Jul-Aug;1(4):322-327.
    doi: 10.1016/j.oret.2016.12.009pubmed: 30693348google scholar: lookup
  12. Lapierre-Landry M, Carroll J, Skala MC. Imaging retinal melanin: a review of current technologies. J Biol Eng 2018;12:29.
    doi: 10.1186/s13036-018-0124-5pubmed: 30534199google scholar: lookup
  13. Zaleska-Żmijewska A, Wawrzyniak ZM, Ulińska M, Szaflik J, Dąbrowska A, Szaflik JP. Human photoreceptor cone density measured with adaptive optics technology (rtx1 device) in healthy eyes: Standardization of measurements. Medicine (Baltimore) 2017 Jun;96(25):e7300.
    doi: 10.1097/MD.0000000000007300pubmed: 28640147google scholar: lookup
  14. Frampton GK, Kalita N, Payne L, Colquitt JL, Loveman E, Downes SM, Lotery AJ. Fundus autofluorescence imaging: systematic review of test accuracy for the diagnosis and monitoring of retinal conditions. Eye (Lond) 2017 Jul;31(7):995-1007.
    doi: 10.1038/eye.2017.19pubmed: 28282065google scholar: lookup
  15. Zhang RR, Schroeder AB, Grudzinski JJ, Rosenthal EL, Warram JM, Pinchuk AN, Eliceiri KW, Kuo JS, Weichert JP. Beyond the margins: real-time detection of cancer using targeted fluorophores. Nat Rev Clin Oncol 2017 Jun;14(6):347-364.
    doi: 10.1038/nrclinonc.2016.212pubmed: 28094261google scholar: lookup
  16. Bajwa A, Aman R, Reddy AK. A comprehensive review of diagnostic imaging technologies to evaluate the retina and the optic disk. Int Ophthalmol 2015 Oct;35(5):733-55.
    doi: 10.1007/s10792-015-0087-1pubmed: 26043677google scholar: lookup
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