Thickness measurements taken with the spectralis OCT increase with decreasing signal strength

Gershoni, Assaf; Barayev, Edward; Vainer, Igor; Allon, Raviv; Yavnieli, Roy; Shapira, Yinon; Geffen, Noa; Nemet, Arie Y.; Segal, Ori

doi:10.1186/s12886-022-02356-4

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…The quantitative results of this study revealed that there are no significant differences between the two A-scan rates OCT in the thickness measurements of the retinal layers. The 85 kHz A-scan rate OCT is a reliable and reproducible diagnostic tool in retinal imaging [ 16 , 17 , 18 ]. The 20 kHz A-scan rate OCT demonstrated a comparable reproducibility in terms of the accuracy of retinal layer segmentation.…”

Section: Discussionmentioning

confidence: 99%

Quantitative and Qualitative Assessments of Retinal Structure with Variable A-Scan Rate Spectralis OCT: Insights into IPL Multilaminarity

Lupidi¹,

Mangoni²,

Centini³

et al. 2023

JCM

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The aim of this study was to evaluate the qualitative and quantitative differences between 20 and 85 kHz A-scan rate optical coherence tomography (OCT) images acquired by spectral domain OCT. The study included 60 healthy subjects analyzed with horizontal linear scans with a variable A-scan rate (SHIFT technology, Heidelberg Engineering, Heidelberg, Germany). The retinal thickness measurement of each retinal layer was performed in three different positions (subfoveal, nasal, and temporal). The qualitative assessment was performed by two independent observers who rated every image with a score ranging from 1 (“sufficient”) to 3 (“excellent”) on the basis of three parameters: visualization of the vitreo-retinal interface, characterization of the retinal layers, and visualization of the sclero-choroidal interface. No statistically significant differences in terms of retinal layer thickness between the two A-scan rate scans were observed (p > 0.05). The coefficient of variation of the retinal thickness values was lower in the 20 kHz group (25.8% versus 30.1% with the 85 kHz). The 20 kHz images showed a higher quality index for both observers. An inner plexiform layer (IPL) multilaminarity was detected in 78.3% of patients from the 20 kHz group and in 40% of patients from the 85 kHz group (p < 0.05).

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Section: Discussionmentioning

confidence: 99%

Quantitative and Qualitative Assessments of Retinal Structure with Variable A-Scan Rate Spectralis OCT: Insights into IPL Multilaminarity

Lupidi¹,

Mangoni²,

Centini³

et al. 2023

JCM

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“…Inclusion criteria were age older than 50 years, presence of AMD diagnosis, and the presence of high-quality OCTA scans in the database (defined as lack of motion artifacts and background noise and quality index Q had to be > or = 25). 21,22 Of 400 initially chosen OCTA scans, we excluded 14 scans due to the diagnosis of CNV of the etiology different than AMD (9 myopic CNV, two CNV secondary to central serous chorioretinopathy, and three peripapillary CNV) and 76 scans were excluded due to motion artifacts, incomplete scan, background noise, and poor quality of the scan (defined with quality index Q < 25). Each eye was analyzed independently.…”

Section: Methodsmentioning

confidence: 99%

“…or = 25). 21,22 Of 400 initially chosen OCTA scans, we excluded 14 scans due to the diagnosis of CNV of the etiology different than AMD (9 myopic CNV, two CNV secondary to central serous chorioretinopathy, and three peripapillary CNV) and 76 scans were excluded due to motion artifacts, incomplete scan, background noise, and poor quality of the scan (defined with quality index Q , 25). Each eye was analyzed independently.…”

Section: Optical Coherence Tomography Angiography Data Selection and ...mentioning

confidence: 99%

Artificial intelligence for OCTA-based disease activity prediction in age-related macular degeneration.

Heinke,

Zhang,

Deussen

et al. 2023

Retina

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Purpose: We hypothesize that OCTA-visualized vascular morphology may be a predictor of CNV status in AMD. We thus evaluated the use of AI to predict different stages of AMD disease based on OCTA en-face 2D projections scans. Methods: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active and wet-AMD in remission with no signs of activity. Two human experts graded the same test set and a consensus grading between 2 experts was used for the prediction of 4 categories. Results: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was: 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was: 0.4286 active CNV, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, 0.9762 respectively. The overall AI classification prediction was significantly better than the human (odds ratio=1.95, p=0.0021). Conclusion: Our data shows that CNV morphology can be used to predict disease activity by AI; Longitudinal studies are needed to better understand the evolution of CNV and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

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“…The median Automatic Real Time (ART) function and signal index quality were exported for each of the included ring scans. Signal quality between 20 and 25 is considered good and above 25 excellent 14 …”

Section: Methodsmentioning

confidence: 99%

“…Signal quality between 20 and 25 is considered good and above 25 excellent. 14 We analyzed the superior temporal arteriole of the central retinal artery at its crossing on the 12 • circular scan using intensity-based analysis. Inner wall thickness (IWT), outer wall thickness (OWT), and LD were assessed in both eyes.…”

Section: Oct Examination and Assessment Of The Retinal Arteriolesmentioning

confidence: 99%

Retinal arteriolar parameters as a surrogate marker of intracranial vascular pathology

Abdelhak

Solomon

Montes

et al. 2022

Alz & Dem Diag Ass & Dis Mo

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Introduction: Development of novel diagnostic tools is a top research priority in vascular dementia. A major obstacle is the lack of a simple, non-invasive method to visualize cerebral arteriolar walls in vivo. Retinal arterioles offer a window into the cerebral circulation. Methods: Intensity-based retinal arteriolar visualization in optical coherence tomography (I-bRAVO) was applied to evaluate mean wall thickness (MWT) and wallto-lumen ratio (WLR) in 250 subjects with sporadic and genetic cerebral small vessel disease (CSVD), non-vascular neurodegenerative diseases (NVND), and healthy controls (HC) in association with imaging and cognitive markers.Results: MWT and WLR were higher in CSVD, associated with severity of vascular white matter lesions, and correlated with magnetic resonance imaging-based intracranial arteriolosclerosis score. WLR correlated with gray and white matter volume and differentiated asymptomatic sporadic CSVD from HC (area under the curve = 0.82).Discussion: I-bRAVO is a rapid, non-invasive tool. MWT and WLR were associated with imaging markers of CSVD and could contribute to early identification of sporadic CSVD. K E Y W O R D Scerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, cerebral small vessel disease, retinal vessels, vascular dementia, white matter disease INTRODUCTIONCerebral vascular pathologies are pivotal contributors to neurodegenerative disease, stroke, and ultimately dementia, with tremendous # Fanny M. Elah and Ari J. Green contributed equally to this study.

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Thickness measurements taken with the spectralis OCT increase with decreasing signal strength

Cited by 10 publications

References 25 publications

Quantitative and Qualitative Assessments of Retinal Structure with Variable A-Scan Rate Spectralis OCT: Insights into IPL Multilaminarity

Quantitative and Qualitative Assessments of Retinal Structure with Variable A-Scan Rate Spectralis OCT: Insights into IPL Multilaminarity

Artificial intelligence for OCTA-based disease activity prediction in age-related macular degeneration.

Retinal arteriolar parameters as a surrogate marker of intracranial vascular pathology

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