Dataset Information

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

ABSTRACT:

Background

To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiography (FA) dataset and the reverse, to be used as a plausible alternative to either imaging technique in patients with diabetic macular edema (DME).

Methods

Original images of 1,195 eyes of 708 nonconsecutive diabetic patients with or without DME were retrospectively analyzed. OCT macular thickness maps and corresponding FA images were preprocessed for use in training and testing the proposed pix2pix GAN. The best quality synthesized images using the test set were selected based on the Fréchet inception distance score, and their quality was studied subjectively by image readers and objectively by calculating the peak signal-to-noise ratio, structural similarity index, and Hamming distance. We also used original and synthesized images in a trained deep convolutional neural network (DCNN) to plot the difference between synthesized images and their ground-truth analogues and calculate the learned perceptual image patch similarity metric.

Results

The pix2pix GAN-synthesized images showed plausible subjectively and objectively assessed quality, which can provide a clinically useful alternative to either image modality.

Conclusion

Using the pix2pix GAN to synthesize mutually dependent OCT color-coded macular thickness maps or FA images can overcome issues related to machine unavailability or clinical situations that preclude the performance of either imaging technique.

Trial registration

ClinicalTrials.gov Identifier: NCT05105620, November 2021. "Retrospectively registered".

SUBMITTER: Abdelmotaal H

PROVIDER: S-EPMC9434904 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

Abdelmotaal Hazem H Sharaf Mohamed M Soliman Wael W Wasfi Ehab E Kedwany Salma M SM

BMC ophthalmology 20220901 1

<h4>Background</h4>To assess the ability of the pix2pix generative adversarial network (pix2pix GAN) to synthesize clinically useful optical coherence tomography (OCT) color-coded macular thickness maps based on a modest-sized original fluorescein angiography (FA) dataset and the reverse, to be used as a plausible alternative to either imaging technique in patients with diabetic macular edema (DME).<h4>Methods</h4>Original images of 1,195 eyes of 708 nonconsecutive diabetic patients with or with ...[more]

PMID: 36050661

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Project description:ObjectiveTo evaluate agreement between fluorescein angiography (FA) and optical coherence tomography (OCT) results for diagnosis of macular edema in patients with uveitis.DesignMulticenter cross-sectional study.ParticipantsFour hundred seventy-nine eyes with uveitis from 255 patients.MethodsThe macular status of dilated eyes with intermediate uveitis, posterior uveitis, or panuveitis was assessed via Stratus-3 OCT and FA. To evaluate agreement between the diagnostic approaches, ? statistics were used.Main outcome measuresMacular thickening (MT; center point thickness, ? 240 ?m per reading center grading of OCT images) and macular leakage (ML; central subfield fluorescein leakage, ? 0.44 disc areas per reading center grading of FA images), and agreement between these outcomes in diagnosing macular edema.ResultsOptical coherence tomography (90.4%) more frequently returned usable information regarding macular edema than FA (77%) or biomicroscopy (76%). Agreement in diagnosis of MT and ML (? = 0.44) was moderate. Macular leakage was present in 40% of cases free of MT, whereas MT was present in 34% of cases without ML. Biomicroscopic evaluation for macular edema failed to detect 40% and 45% of cases of MT and ML, respectively, and diagnosed 17% and 17% of cases with macular edema that did not have MT or ML, respectively; these results may underestimate biomicroscopic errors (ophthalmologists were not explicitly masked to OCT and FA results). Among eyes free of ML, phakic eyes without cataract rarely (4%) had MT. No factors were found that effectively ruled out ML when MT was absent.ConclusionsOptical coherence tomography and FA offered only moderate agreement regarding macular edema status in uveitis cases, probably because what they measure (MT and ML) are related but nonidentical macular pathologic characteristics. Given its lower cost, greater safety, and greater likelihood of obtaining usable information, OCT may be the best initial test for evaluation of suspected macular edema. However, given that ML cannot be ruled out if MT is absent and vice versa, obtaining the second test after negative results on the first seems justified when detection of ML or MT would alter management. Given that biomicroscopic evaluation for macular edema erred frequently, ancillary testing for macular edema seems indicated when knowledge of ML or MT status would affect management.Financial disclosure(s)Proprietary or commercial disclosure may be found after the references.

Project description:ImportanceDiabetic macular edema (DME) is the predominant cause of visual impairment in patients with type 1 or 2 diabetes. Automated fluid volume measurements using optical coherence tomography (OCT) may improve the diagnostic accuracy of DME screening.ObjectiveTo assess the diagnostic accuracy of an automated central macular fluid volume (CMFV) quantification using OCT for DME.Design, setting, and participantsA cross-sectional observational study was conducted at a tertiary academic center among 215 patients with diabetes (1 eye each) enrolled from January 26, 2015, to December 23, 2019. All participants underwent comprehensive examinations, 6 × 6-mm macular structural OCT horizontal raster scans, and 6 × 6-mm macular OCT angiography volumetric scans. From January 1 to March 30, 2020, 2 retinal specialists reviewed the structural OCT scans independently and diagnosed DME if intraretinal or subretinal fluid was present. Diabetic macular edema was considered center involved if fluid was present within the central fovea (central 1-mm circle). A third retinal specialist arbitrated any discrepancy. The mean central subfield thickness (CST) within the central fovea was measured on structural OCT horizontal raster scans. A deep learning algorithm automatically quantified fluid volumes on 6 × 6-mm OCT angiography volumetric scans and within the central foveas (CMFV).Main outcomes and measuresThe area under the receiver operating characteristic curve (AUROC) and the sensitivity and specificity of CST and CMFV for DME diagnosis.ResultsWe enrolled 1 eye each of 215 patients with diabetes (117 women [54.4%]; mean [SD] age, 59.6 [12.4] years). Diabetic macular edema was present in 136 eyes; 93 cases of DME were center involved. The AUROC of CMFV for diagnosis of center-involved DME (0.907 [95% CI, 0.861-0.954]) was greater than the AUROC of CST (0.832 [95% CI, 0.775-0.889]; P = .02). With the specificity set at 95%, the sensitivity of CMFV for detection of center-involved DME (78.5% [95% CI, 68.8%-86.3%]) was higher than that of CST (53.8% [95% CI, 43.1%-64.2%]; P = .002). Center-involved DME cases not detected by CST but detected by CMFV were associated with a thinner CST (290.8 μm [95% CI, 282.3-299.3 μm] vs 369.4 μm [95% CI, 347.1-391.7 μm]; P < .001), higher proportion of previous macular laser treatment (11 of 28 [39.3%; 95% CI, 21.5%-59.4%] vs 12 of 65 [18.5%; 95% CI, 9.9%-30.0%]; P = .03), and female sex (20 of 28 [71.4%; 95% CI, 51.3%-86.8%] vs 31 of 65 [47.7%; 95% CI, 35.1%-60.5%]; P = .04).Conclusions and relevanceThese findings suggest that an automated CMFV is a more accurate diagnostic biomarker than CST for DME and may improve screening for DME.

Dataset Information

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

Background

Methods

Results

Conclusion

Trial registration

Publications

Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.

Similar Datasets

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