Dataset Information

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph.

ABSTRACT:

Objective

We aimed to develop a deep learning (DL)-based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps.

Design

Developing and evaluating an artificial intelligence detection tool.

Subjects

Pretraining paired data of color fundus photographs and SD-OCT images from 189 healthy participants and 371 patients with early glaucoma were used.

Methods

The variational autoencoder (VAE) network training architecture was used for training, and the correlation between the fundus photographs and RNFL thickness distribution was determined through the deep neural network. The reference standard was defined as a vertical cup-to-disc ratio of ≥0.7, other typical changes in glaucomatous optic neuropathy, and RNFL defects. Convergence indicates that the VAE has learned a distribution that would enable us to produce corresponding synthetic OCT scans.

Main outcome measures

Similarly to wide-field OCT scanning, the proposed model can extract the results of RNFL thickness analysis. The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used to assess signal strength and the similarity in the structure of the color fundus images converted to an RNFL thickness distribution model. The differences between the model-generated images and original images were quantified.

Results

We developed and validated a novel DL-based algorithm to extract thickness information from the color space of fundus images similarly to that from OCT images and to use this information to regenerate RNFL thickness distribution images. The generated thickness map was sufficient for clinical glaucoma detection, and the generated images were similar to ground truth (PSNR: 19.31 decibels; SSIM: 0.44). The inference results were similar to the OCT-generated original images in terms of the ability to predict RNFL thickness distribution.

Conclusions

The proposed technique may aid clinicians in early glaucoma detection, especially when only color fundus photographs are available.

SUBMITTER: Chen HS

PROVIDER: S-EPMC9559108 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph.

Chen Henry Shen-Lih HS Chen Guan-An GA Syu Jhen-Yang JY Chuang Lan-Hsin LH Su Wei-Wen WW Wu Wei-Chi WC Liu Jian-Hong JH Chen Jian-Ren JR Huang Su-Chen SC Kang Eugene Yu-Chuan EY

Ophthalmology science 20220611 3

<h4>Objective</h4>We aimed to develop a deep learning (DL)-based algorithm for early glaucoma detection based on color fundus photographs that provides information on defects in the retinal nerve fiber layer (RNFL) and its thickness from the mapping and translating relations of spectral domain OCT (SD-OCT) thickness maps.<h4>Design</h4>Developing and evaluating an artificial intelligence detection tool.<h4>Subjects</h4>Pretraining paired data of color fundus photographs and SD-OCT images from 18 ...[more]

PMID: 36245759

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Project description:PURPOSE:To investigate the role of sex on retinal nerve fiber layer (RNFL) thickness at 768 circumpapillary locations based on OCT findings. DESIGN:Population-based cross-sectional study. PARTICIPANTS:We investigated 5646 eyes of 5646 healthy participants from the Leipzig Research Centre for Civilization Diseases (LIFE)-Adult Study of a predominantly white population. METHODS:All participants underwent standardized systemic assessments and ocular imaging. Circumpapillary RNFL (cRNFL) thickness was measured at 768 points equidistant from the optic nerve head using spectral-domain OCT (Spectralis; Heidelberg Engineering, Heidelberg, Germany). To control ocular magnification effects, the true scanning radius was estimated by scanning focus. Student t test was used to evaluate sex differences in cRNFL thickness globally and at each of the 768 locations. Multivariable linear regression and analysis of variance were used to evaluate individual contributions of various factors to cRNFL thickness variance. MAIN OUTCOME MEASURES:Difference in cRNFL thickness between males and females. RESULTS:Our population consisted of 54.8% females. The global cRNFL thickness was 1 ?m thicker in females (P < 0.001). However, detailed analysis at each of the 768 locations revealed substantial location specificity of the sex effects, with RNFL thickness difference ranging from -9.98 to +8.00 ?m. Females showed significantly thicker RNFLs in the temporal, superotemporal, nasal, inferonasal, and inferotemporal regions (43.6% of 768 locations), whereas males showed significantly thicker RNFLs in the superior region (13.2%). The results were similar after adjusting for age, body height, and scanning radius. The superotemporal and inferotemporal RNFL peaks shifted temporally in females by 2.4° and 1.9°, respectively. On regions with significant sex effects, sex explained more RNFL thickness variance than age, whereas the major peak locations and interpeak angle explained most of the RNFL thickness variance unexplained by sex. CONCLUSIONS:Substantial sex effects on cRNFL thickness were found at 56.8% of all 768 circumpapillary locations, with specific patterns for different sectors. Over large regions, sex was at least as important in explaining the cRNFL thickness variance as was age, which is well established to have a substantial impact on cRNFL thickness. Including sex in the cRNFL thickness norm could therefore improve glaucoma diagnosis and monitoring.

Project description:PurposeTo compare the rates of retinal nerve fiber layer (RNFL) loss in patients suspected of having glaucoma who developed visual field damage (VFD) with those who did not develop VFD and to determine whether the rate of RNFL loss can be used to predict the development of VFD.DesignProspective, observational cohort study.ParticipantsGlaucoma suspects, defined as having glaucomatous optic neuropathy or ocular hypertension (intraocular pressure, >21 mmHg) without repeatable VFD at baseline, from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study.MethodsGlobal and quadrant RNFL thickness (RNFLT) were measured with the Spectralis spectral-domain optical coherence tomography (SD-OCT; Spectralis HRA+OCT [Heidelberg Engineering, Heidelberg, Germany]). Visual field damage was defined as having 3 consecutive abnormal visual fields. The rate of RNFL loss in eyes developing VFD was compared to eyes not developing VFD using multivariate linear mixed-effects models. A joint longitudinal survival model used the estimated RNFLT slope to predict the risk of developing VFD, while adjusting for potential confounding variables.Main outcome measuresThe rate of RNFL thinning and the probability of developing VFD.ResultsFour hundred fifty-four eyes of 294 glaucoma suspects were included. The average number of SD-OCT examinations was 4.6 (range, 2-9), with median follow-up of 2.2 years (0.4-4.1 years). Forty eyes (8.8%) developed VFD. The estimated mean rate of global RNFL loss was significantly faster in eyes that developed VFD compared with eyes that did not develop VFD (-2.02 μm/year vs. -0.82 μm/year; P<0.001). The joint longitudinal survival model showed that each 1-μm/year faster rate of global RNFL loss corresponded to a 2.05-times higher risk of developing VFD (hazard ratio, 2.05; 95% confidence interval, 1.14-3.71; P = 0.017).ConclusionsThe rate of global RNFL loss was more than twice as fast in eyes that developed VFD compared with eyes that did not develop VFD. A joint longitudinal survival model showed that a 1-μm/year faster rate of RNFLT loss corresponded to a 2.05-times higher risk of developing VFD. These results suggest that measuring the rate of SD-OCT RNFL loss may be a useful tool to help identify patients who are at a high risk of developing visual field loss.

Dataset Information

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph.

Objective

Design

Subjects

Methods

Main outcome measures

Results

Conclusions

Publications

Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph.

Similar Datasets

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