April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Analysis of Anterior Chamber Inflammation By Anterior Segment Spectral Domain Optical Coherence Tomography
Author Affiliations & Notes
  • Sunil K Srivastava
    Cole Eye Institute, Cleveland Clinic, Cleveland, OH
    Ophthalmic Imaging Center, Cole Eye Institute, Cleveland, OH
  • Sumit Sharma
    Cole Eye Institute, Cleveland Clinic, Cleveland, OH
  • Kimberly Baynes
    Cole Eye Institute, Cleveland Clinic, Cleveland, OH
  • Careen Y Lowder
    Cole Eye Institute, Cleveland Clinic, Cleveland, OH
  • Footnotes
    Commercial Relationships Sunil Srivastava, Alimera (C), Allergan (F), Bausch and Lomb (C), Bioptigen (P), Clearside (F), Novartis (F), Regeneron (C); Sumit Sharma, None; Kimberly Baynes, None; Careen Lowder, Clearside (C), Santen (C)
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4870. doi:
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      Sunil K Srivastava, Sumit Sharma, Kimberly Baynes, Careen Y Lowder; Analysis of Anterior Chamber Inflammation By Anterior Segment Spectral Domain Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4870.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: Currently the gold standard for measuring and grading anterior chamber inflammation is the Standarization of Uveitis Nomenclature (SUN) criteria which grades inflammation on a 6 step ordinal scale. In order to create a more continuous measure of anterior chamber inflammation we assessed the feasibility of measuring anterior chamber inflammation using anterior segment spectral domain optical coherence tomography. (AS-OCT)

Methods: Patients with both active and inactive inflammation were assessed clinically with slit lamp examination and graded using the SUN criteria. Eyes were then imaged using AS-OCT. Line scans were manually graded for the presence of anterior chamber cells and correlated to clinical exam. Next, 3-D cube scans were manually graded for the presence of anterior chamber cells and correlated to both clinical exam and an automated program which was developed to grade the number of cells. Finally, eyes were then assessed at several different time points to assess the ability of the automated count to independently grade inflammation.

Results: A total of 215 eyes were assessed in this study. In the initial assessment comparing manual line scans to clinical grading, there was high correlation between line scans and clinical grades (Spearman correlation coefficient .967, p<.001) Three dimensional cube scans correlated highly with clinical grade. Both automated and manual cell grades correlated highly with each other (Spearman correlation coefficient .997, p<.001). A cellular density measure was created of the number of cells/mm3. Using this measure, automated analysis of patients at several different time points displayed high correlation between clinical grade and automated cellular measures (r=.76, p<.001). Automated grading revealed a variability in cellular measure at 3+ and 4+ clinical grades. Additionally, automated SD-OCT measurements revealed the presence of cells in quiet eyes.

Conclusions: AS-OCT is a useful tool to objectively image and grade anterior chamber inflammation. The number of cells on clinical exam strongly correlates with AS-OCT scans. Some patients were quiet on clinical exam but demonstrated cell on AS-OCT imaging. Automated algorithm is accurate compared to clinical grading. Automated measurements of inflammation by AS-OCT could be used as an objective measure in clinical trials and as a screening tool in high risk patients.

Keywords: 557 inflammation • 550 imaging/image analysis: clinical • 746 uveitis-clinical/animal model  
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