2,395 research outputs found

    Detection of occludable angle with anterior segment optical coherence tomography and Pentacam as non-contact screening methods

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    Purpose To evaluate diagnostic capacity for occludable anterior chamber angle detection with anterior segment optical coherence tomography (AS-OCT) and Pentacam. Methods Observational cross-sectional study with AS-OCT and Pentacam. AS-OCT measures: angle opening distance from Schwalbe line (SL) perpendicular (AOD-SL-Perp) and vertical to iris (AOD-SL-Vert), and iridotrabecular angle (ITA). Pentacam measures: anterior chamber depth (ACD), anterior chamber volume (ACV), and anterior chamber angle (ACA). We analysed Spearman's correlation with gonioscopic classification. Area under receiver operating characteristic curves (AUCs) for occludable angle detection were compared. Agreement between iridocorneal values of methods was evaluated. Results Seventy-four left eyes of 74 patients. Correlation between temporal AS-OCT and gonioscopy: 0.83 (p < 0.0001) AOD-SL-Perp temporal, 0.82 (p < 0.0001) AOD-SL-Vert temporal, and 0.69 (p < 0.0001) ITA temporal. Correlation between AS-OCT nasal and gonioscopy: 0.74 (p < 0.0001) AOD-SL-Perp nasal, 0.74 (p < 0.0001) AOD-SL-Vert nasal, and 0.70 (p < 0.0001) ITA nasal. Correlation of Pentacam with temporal gonioscopy: 0.57 (p < 0.0001) ACD, 0.56 (p < 0.0001) ACV, and 0.63 (p < 0.0001) ACA. Correlation of Pentacam with nasal gonioscopy: 0.47 (IC 0.27-0.73, p < 0.0001) ACD, 0.49 (p < 0.0001) ACV, and 0.56 (CI 0.38-0.7, p < 0.0001) ACA. AS-OCT AUCs: AOD-SL-Perp temporal 0.89 (CI 0.80-0.95), AOD-SL-Vert 0.87 (CI 0.77-0.94), ITA temporal 0.88 (CI 0.78-0.94), AOD-SL-Perp nasal 0.83 (CI 0.72-0.91), AOD-SL-Vert nasal 0.87 (CI 0.77-0.94), and ITA nasal 0.91 (IC 0.81-0.96). Pentacam AUCs: ACD 0.76 (CI 0.64-0.85), ACV 0.75 (CI 0.63-0.84), and ACA 0.84 (CI 0.74-0.92). No statistical differences between different AUCs. Intraclass correlation coefficient (ICC) of ACA (Pentacam) with ITA temporal (AS-OCT) 0.59 and with nasal ITA nasal (AS-OCT) 0.65. Conclusion Both systems show high capacity for non-contact occludable angle detection. But agreement between methods is moderate or low

    Measurement of and Factors Associated with the Anterior Chamber Volume in Healthy Chinese Adults

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    Purpose. To measure the anterior chamber volume (ACV) and determine factors associated with the ACV in healthy Chinese adults. Methods. In this cross-sectional study, we used swept-source optical coherence tomography (SS-OCT) to measure ACV and other anterior segment parameters. Factors associated with ACV were also determined. Results. A total of 313 healthy Chinese adults were enrolled. The anterior segment parameters, including ACV, could be measured by SS-OCT with excellent repeatability and reproducibility. There was a significant difference between the horizontal and vertical anterior chamber widths (ACW) (P<0.05), with a mean difference of 390โ€‰ฮผm. The ACV (mean 153.83ยฑ32.42โ€‰mm3) was correlated with most of the anterior segment parameters, especially anterior chamber depth (ACD), which accounted for about 85% of the variation of ACV. Most of the anterior segment parameters were significantly correlated with age, and the relative changes in ACV and ACD were greatest in subjects aged 41โ€“50 years. Conclusion. ACV was correlated with most of the anterior segment parameters measured in this study, particularly ACD. The relatively large difference between horizontal and vertical ACW suggests that the ACV could and should be measured using multiple OCT scans

    ์ŠคํŽ™ํŠธ๋Ÿผ์˜์—ญ ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋ฅผ ์ด์šฉํ•œ ํ™์ฑ„๊ฐ๋ง‰๊ฐ๊ณผ ๊ทผ์œ„ ๋ˆ„๊ด€์˜ ํ•ด๋ถ€ํ•™์  ๊ตฌ์กฐ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2022. 8. ์„œ๊ฐ•๋ฌธ.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ˆˆ์˜ ์ „์•ˆ๋ถ€ ๊ตฌ์กฐ ๋ฐ ๋ถ€์†๊ธฐ๋ฅผ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ™์ฑ„๊ฐ๋ง‰๊ฐ๊ณผ ๋ˆˆ๋ฌผ๊ด€์„ ํฌํ•จํ•œ ๋ˆˆ์˜ ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ œ1์žฅ์—์„œ๋Š” ์ž„์ƒ ํ™˜๊ฒฝ์—์„œ ํ™์ฑ„๊ฐ๋ง‰๊ฐ ์ฒ™๋„์˜ ์˜์ƒํ™” ๊ฐ€๋Šฅ์„ฑ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ ๋ฐ ์ดˆ์ŒํŒŒ ์ƒ์ฒด ํ˜„๋ฏธ๊ฒฝ์„ ์ด์šฉํ•˜์—ฌ ๊ฐœ์˜ ์™ธ์ธก ์•ˆ๋ฅœ๋ถ€์—์„œ ์ด 47๊ฐœ์˜ ๋ˆˆ์„ ๊ฒ€์‚ฌํ•˜์˜€๋‹ค. ์ด์— ๋”ฐ๋ผ ํš๋“ํ•œ ์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ํ™์ฑ„๊ฐ๋ง‰๊ฐ(ICA)๊ณผ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ(AOD)๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ด€์ฐฐ์ž ๋‚ด ๋ฐ ๊ด€์ฐฐ์ž ๊ฐ„ ์žฌํ˜„์„ฑ์€ ๊ธ‰๋‚ด ์ƒ๊ด€ ๊ณ„์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๊ด€์ฐฐ์ž ๋‚ด ์žฌํ˜„์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์ฒซ ๋ฒˆ์งธ ๊ฒ€์‚ฌ์ž์˜ ์ฒซ ๋ฒˆ์งธ ๋ฐ ๋‘ ๋ฒˆ์งธ ๋“ฑ๊ธ‰ ์ธก์ •๊ฐ’์„ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ด€์ฐฐ์ž ๊ฐ„ ์žฌํ˜„์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๋‘ ๊ฒ€์‚ฌ์ž ๊ฐ„์˜ ์ธก์ •๊ฐ’์„ ๋น„๊ตํ•˜์˜€๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ ๋ฐ ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ์— ๋Œ€ํ•œ ํ™์ฑ„๊ฐ๋ง‰๊ฐ๊ณผ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ ๊ฐ„์˜ ์ผ์น˜๋Š” Bland-Altman plot์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ‰๊ฐ€์—์„œ ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ์— ๋Œ€ํ•œ ํ‰๊ท  ํ™์ฑ„๊ฐ๋ง‰๊ฐ ๋ฐ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ๋Š” ๊ฐ๊ฐ 31.4 ยฑ 6.4ยฐ๋ฐ 641.4 ยฑ 270.8 ฮผm์˜€๋‹ค. ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ์— ๋Œ€ํ•œ ํ‰๊ท  ํ™์ฑ„๊ฐ๋ง‰๊ฐ๊ณผ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ๋Š” ๊ฐ๊ฐ 32.0 ยฑ 4.8ยฐ์™€ 700.4 ยฑ 238.8 ฮผm์˜€๋‹ค. ํ™์ฑ„๊ฐ๋ง‰๊ฐ ๋ฐ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ ์ธก์ •์˜ ๊ฒฝ์šฐ ๋‘ ๊ธฐ๊ธฐ ๋ชจ๋‘์—์„œ ๊ด€์ฐฐ์ž ๋‚ด ์žฌํ˜„์„ฑ์ด ์šฐ์ˆ˜ํ•œ ๋ฐ˜๋ฉด ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ์—์„œ๋Š” ๊ด€์ฐฐ์ž ๊ฐ„ ์žฌํ˜„์„ฑ์ด ์šฐ์ˆ˜ํ•˜๊ณ  ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ์—์„œ๋Š” ์–‘ํ˜ธํ–ˆ๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ์™€ ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ ์‚ฌ์ด์˜ ํ™์ฑ„๊ฐ๋ง‰๊ฐ์˜ ํ‰๊ท  ์ฐจ์ด๋Š” 0.6o์˜€์œผ๋ฉฐ, ์ผ์น˜ ํ•œ๊ณ„ ๋ฒ”์œ„๋Š” 18.9o์˜€๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ์™€ ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ ์‚ฌ์ด์˜ ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ ํ‰๊ท  ์ฐจ์ด๋Š” 58.9 ฮผm์˜€๊ณ  ์ผ์น˜ ํ•œ๊ณ„๋Š” 804.4 ฮผm์˜€๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋Š” ์ž„์ƒ ํ™˜๊ฒฝ์—์„œ ํ™์ฑ„๊ฐ๋ง‰๊ฐ ์ฒ™๋„์˜ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ํšจ๊ณผ์ ์ธ ๋น„์ ‘์ด‰ ์˜์ƒ ๊ธฐ๋ฒ•์ด๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋กœ ์–ป์€ ์ธก์ • ๊ฐ’์˜ ์žฌํ˜„์„ฑ์€ ์ดˆ์ŒํŒŒ ์ƒ์ฒดํ˜„๋ฏธ๊ฒฝ๊ณผ ๋น„์Šทํ•˜๊ฑฐ๋‚˜ ์šฐ์ˆ˜ํ•˜์ง€๋งŒ, ๊ฐ๊ฐœ๋ฐฉ๊ฑฐ๋ฆฌ ๊ฐ’์€ ์ดˆ์ŒํŒŒ ์ƒ์ฒด ํ˜„๋ฏธ๊ฒฝ์œผ๋กœ ์ธก์ •ํ•œ ๊ฐ’๊ณผ ๊ต์ฐจํ•˜์—ฌ ์‚ฌ์šฉํ•  ์ˆ˜ ์—†์—ˆ๋‹ค. ์ œ2์žฅ์—์„œ๋Š” ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ƒ๋ถ€ ๋ฐ ํ•˜๋ถ€ ๊ทผ์œ„ ๋ˆ„๊ด€ ์˜์ƒํ™” ๊ฐ€๋Šฅ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ •์ƒ ๋น„๊ธ€๊ฒฌ 4๋งˆ๋ฆฌ์—์„œ 8๊ฐœ์˜ ๋ˆˆ์ด ์‹คํ—˜์— ํฌํ•จ๋˜์—ˆ๋‹ค. ์ƒ์•ˆ๊ฒ€์˜ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ์˜์ƒ์„ ์–ป๊ธฐ ์œ„ํ•ด ์˜์ƒํ™”ํ•˜๊ณ ์ž ํ•˜๋Š” ๋ˆˆ์˜ ๋‚ด์ธก ๋ฐฉํ–ฅ์œผ๋กœ ๋จธ๋ฆฌ๋ฅผ ๋Œ๋ฆฌ๊ณ  ์ƒ์•ˆ๊ฒ€์˜ ๋‚ด์ธก ๋ถ€๋ถ„์„ ์™ธ๋ฒˆํ•˜์—ฌ ์ƒ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€๋ฅผ ๋…ธ์ถœ์‹œ์ผฐ๋‹ค. ํ•˜๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ์ด๋ฏธ์ง€๋ฅผ ์–ป๊ธฐ ์œ„ํ•ด ๋ˆ„์  ๋ฐ”๋กœ ์•„๋ž˜์—์„œ ํ•˜์•ˆ๊ฒ€์„ ์™ธ๋ฒˆํ•˜์˜€๋‹ค. "๊ฐ๋„ ๋ชจ๋“œ"๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ดฌ์˜ ๊ธฐ์ค€์„ ์„ ๋ˆ„๊ด€์˜ ์žฅ์ถ•์— ํ‰ํ–‰ํ•˜๊ฒŒ ๋ฐฐ์น˜ํ•˜์—ฌ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ์ž…๊ตฌ ํญ์„ ์ธก์ •ํ–ˆ๋‹ค. ์ธ๊ณต๋ˆˆ๋ฌผ์„ ์ ์•ˆํ•˜๊ณ , ์ธ๊ณต๋ˆˆ๋ฌผ ์ ์•ˆ ์ „๊ณผ ํ›„์˜ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ˆ„๊ด€์ด ํ™•์žฅ๋œ ํ›„ ์ดˆ๊ธฐ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ์ž…๊ตฌ ํญ์œผ๋กœ ๋˜๋Œ์•„์˜ค๋Š” ์‹œ๊ฐ„์ธ ํšŒ๊ท€ ์‹œ๊ฐ„์„ ๊ธฐ๋กํ–ˆ๋‹ค. ์ธ๊ณต๋ˆˆ๋ฌผ ์ ์  ์ „์—๋Š” ์ƒ๋ˆ„๊ด€๊ณผ ํ•˜๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ ์‚ฌ์ด์— ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค(๊ฐ๊ฐ 91.8 ยฑ 3.2 ๋ฐ 110.1 ยฑ 8.4 ฮผm). ์ธ๊ณต๋ˆˆ๋ฌผ ์ ์  ํ›„ ํ‰๊ท  ์ƒ๋ˆ„๊ด€ ๋ฐ ํ•˜๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ๋Š” ๊ฐ๊ฐ 236.9 ยฑ 27.7 ๋ฐ 238.4 ยฑ 30.4 ฮผm์˜€๋‹ค. ์ƒ๋ˆ„๊ด€ ๋ฐ ํ•˜๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ์€ ์ธ๊ณต๋ˆˆ๋ฌผ ์ ์  ์ „ํ›„์˜ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ๊ณผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์ธ๊ณต๋ˆˆ๋ฌผ ์ ์  ํ›„ ์ƒ๋ถ€ ๋ฐ ํ•˜๋ถ€ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ํญ์˜ ์ดˆ๊ธฐ ๋„ˆ๋น„๋กœ์˜ ํ‰๊ท  ํšŒ๊ท€ ์‹œ๊ฐ„์€ 4๋ถ„ ์ด๋‚ด์˜€๋‹ค. ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋Š” ์ƒ๋ถ€ ๋ฐ ํ•˜๋ถ€ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€๋ฅผ ๊ณ ํ•ด์ƒ๋„ ์ด๋ฏธ์ง€๋กœ ์ดฌ์˜ ํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์ด์—ˆ๋‹ค. ์ด ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ˆ˜์˜ํ•™ ์ž„์ƒ์—์„œ ์ ์•ˆ์•ก ์ ์•ˆ ํ›„ ๋ˆ„๊ด€ ๊ทผ์œ„๋ถ€ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ๋น›๊ฐ„์„ญ๋‹จ์ธต์ดฌ์˜๊ธฐ๋Š” ์•ˆ๊ตฌ ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ ํ‰๊ฐ€ํ•˜๋Š”๋ฐ ์œ ์šฉํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ํŒ๋‹จ๋˜๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ์•ˆ๊ตฌ์˜ ์ „์•ˆ๋ถ€ ๋ฐ ๋ถ€์†๊ธฐ์˜ ๋ฏธ์„ธ๊ตฌ์กฐ์˜ ๋ณ‘ํƒœ์ƒ๋ฆฌํ•™์  ์ •๋ณด๋ฅผ ์ž˜ ํ™•์ธํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.This study was to evaluate the microstructures of the eye including iridocorneal angle and lacrimal canaliculi (LC) by spectral-domain optical coherence tomography (SD-OCT). In chapter 1, the feasibility of imaging the canine iridocorneal angle parameters in a clinical setting was investigated. A total of 47 eyes of dogs were scanned at the temporal limbus using SD-OCT and UBM. Iridocorneal angle (ICA) and angle opening distance (AOD) were measured from the obtained images accordingly. The intra-observer and inter-observer reproducibility were evaluated using the intraclass correlation coefficient. To evaluate intra-observer reproducibility, measurements of the first and second grading from the first examiner were compared. To evaluate inter-observer reproducibility, measurements between the two examiners were compared. Bland-Altman plots were used to evaluate agreement between ICA and AOD for SD-OCT and ultrasound biomicroscopy (UBM). In the first grading, ICA and AOD for SD-OCT were 31.4 ยฑ 6.4ยฐ and 641.4 ยฑ 270.8 ยตm (mean ยฑ SD), respectively. ICA and AOD for UBM were 32.0 ยฑ 4.8ยฐ and 700.4 ยฑ 238.8 ยตm (mean ยฑ SD), respectively. For ICA and AOD measurements, intra-observer reproducibility was excellent for both devices, whereas inter-observer reproducibility was excellent for SD-OCT and good for UBM. The mean difference in ICA between SD-OCT and UBM was 0.6ยฐ with a limit of agreement (LoA) span of 18.9ยฐ. The mean difference in AOD between SD-OCT and UBM was 58.9 ยตm with a LoA span of 804.4 ยตm. SD-OCT is an effective non-contact imaging modality for the evaluation of canine iridocorneal angle parameters in a clinical setting. Reproducibility of measurements obtained is comparable or superior to UBM, but values obtained by SD-OCT and UBM for AOD are not interchangeable between devices. In chapter 2, the feasibility of visualizing upper and lower proximal LC using SD-OCT was confirmed. Eight eyes of four normal Beagle dogs were included. To obtain an upper proximal LC image, the head was turned in the opposite direction to the eye being imaged, and the medial part of the upper eyelid was everted to expose the LC. To obtain a lower LC image, the lower eyelid was everted just below the punctum. Using โ€œangle modeโ€, the scan line was placed parallel on the long axis of the LC. The inlet LC width (LCW) was measured. Artificial tears (AT) were instilled, and LCW was compared before and after AT instillation. Additionally, the return time to the initial LCW inlet width was recorded. Before AT instillation, there was a significant difference between the mean upper and lower LCW (91.8 ยฑ 3.2 and 110.1 ยฑ 8.4 ยตm, respectively). After AT instillation, the mean upper and lower LCW were 236.9 ยฑ 27.7 and 238.4 ยฑ 30.4 ยตm, respectively. Significant differences in the LCW before and after AT instillation in both the upper and lower LCWs were observed. The mean return time of the upper and lower LCW to their initial widths after AT instillation was within 4 min. SD-OCT could be one option for providing high-resolution images of the upper and lower proximal LC. This method enables observation of LC changes after instillation of eyedrops in veterinary clinical practice. In conclusion, SD-OCT would be an useful method for evaluating the ocular microstructures and the pathophysiological information of anterior segment of the eye and adnexa could be well confirmed.GENERAL INTRODUCTION 1 CHAPTER I Comparison of Iridocorneal Angle Parameters Measured by Spectral Domain Optical Coherence Tomography and Ultrasound Biomicroscopy in Dogs ABSTRACT 5 INTRODUCTION 6 MATERIALS AND METHODS 1. Animals studied 8 2. Procedures 9 3. Image analyses 12 4. Statistical analyses 15 RESULTS 1. Intra-observer reproducibility 16 2. Inter-observer reproducibility 18 3. Agreement between SD-OCT and UBM 20 DISCUSSION 22 CONCLUSION 28 CHAPTER II Evaluation of the Upper and Lower Lacrimal Canaliculus Using Spectral Domain Optical Coherence Tomography in Normal Beagle Dogs ABSTRACT 30 INTRODUCTION 31 MATERIALS AND METHODS 1. Animals studied 33 2. Imaging device 33 3. Scanning procedures and image analyses 34 4. Statistical analyses 39 RESULTS 40 DISCUSSION 43 CONCLUSION 47 GENERAL CONCLUSIONS 48 REFERENCES 50 ABSTRACT IN KOREAN 58๋ฐ•

    Optical coherence tomography and optical coherence tomography angiography in uveitis : a review

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    Optical coherence tomography (OCT) has dramatically changed the understanding and management of uveitis and other ocular conditions. Currently, OCT angiography (OCTA) combines structural information with the visualization of blood flow within the imaged area. The aim of this review is to present the basic principles of OCT and OCTA interpretation and to investigate the role of these imaging techniques in the diagnosis and management of uveitis. Common complications of intraocular inflammation such as macular oedema and inflammatory choroidal neovascularization are often diagnosed and followed with OCT/OCTA scans. However, uveitis specialists can obtain much more information from tomographic scans. This review provides a comprehensive description of typical OCT/OCTA findings characterizing different ocular structures in uveitis, proceeding from the cornea to the choroid. A careful interpretation of OCT/OCTA images can help in the differential diagnosis, the prediction of clinical outcomes, and the follow-up of patients with uveitis

    A laser-induced mouse model with long-term intraocular pressure elevation

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    Purpose: To develop and characterize a mouse model with intraocular pressure (IOP) elevation after laser photocoagulation on the trabecular meshwork (TM), which may serve as a model to investigate the potential of stem cell-based therapies for glaucoma. Methods: IOP was measured in 281 adult C57BL/6 mice to determine normal IOP range. IOP elevation was induced unilaterally in 50 adult mice, by targeting the TM through the limbus with a 532-nm diode laser. IOP was measured up to 24 weeks post-treatment. The optic nerve damage was detected by electroretinography and assessed by semiautomatic counting of optic nerve axons. Effects of laser treatment on the TM were evaluated by histology, immunofluorescence staining, optical coherence tomography (OCT) and transmission electron microscopy (TEM). Results: The average IOP of C57BL/6 mice was 14.5ยฑ2.6 mmHg (Mean ยฑSD). After laser treatment, IOP averaged above 20 mmHg throughout the follow-up period of 24 weeks. At 24 weeks, 57% of treated eyes had elevated IOP with the mean IOP of 22.5ยฑ2.5 mmHg (Mean ยฑSED). The difference of average axon count (59.0%) between laser treated and untreated eyes was statistically significant. Photopic negative response (PhNR) by electroretinography was significantly decreased. CD45+ inflammatory cells invaded the TM within 1 week. The expression of SPARC was increased in the TM from 1 to 12 weeks. Histology showed the anterior chamber angle open after laser treatment. OCT indicated that most of the eyes with laser treatment had no synechia in the anterior chamber angles. TEM demonstrated disorganized and compacted extracellular matrix in the TM. Conclusions: An experimental murine ocular hypertension model with an open angle and optic nerve axon loss was produced with laser photocoagulation, which could be used to investigate stem cell-based therapies for restoration of the outflow pathway integrity for ocular hypertension or glaucoma. Copyright

    A review of artificial intelligence applications in anterior segment ocular diseases

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    Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases
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