scholarly journals Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris

2021 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Boonsong Wanichwecharungruang ◽  
Natsuda Kaothanthong ◽  
Warisara Pattanapongpaiboon ◽  
Pantid Chantangphol ◽  
Kasem Seresirikachorn ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Anterior Segment ◽  
Optical Coherence

scholarly journals Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e031313 ◽  
Author(s):  
Kazutaka Kamiya ◽  
Yuji Ayatsuka ◽  
Yudai Kato ◽  
Fusako Fujimura ◽  
Masahide Takahashi ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Anterior Segment ◽  
Optical Coherence ◽  
Diagnostic Accuracy Study ◽  
Clinical Trial Registration ◽  
Swept Source ◽  
Refractive Power ◽  
Accuracy Study

ObjectiveTo evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).DesignA diagnostic accuracy study.SettingA single-centre study.ParticipantsA total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes.Main outcome measuresThe diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map).ResultsDeep learning of the arithmetical mean output data of these six maps showed an accuracy of 0.991 in discriminating between normal and keratoconic eyes. For single map analysis, posterior elevation map (0.993) showed the highest accuracy, followed by posterior curvature map (0.991), anterior elevation map (0.983), corneal pachymetry map (0.982), total refractive power map (0.978) and anterior curvature map (0.976), in discriminating between normal and keratoconic eyes. This deep learning also showed an accuracy of 0.874 in classifying the stage of the disease. Posterior curvature map (0.869) showed the highest accuracy, followed by corneal pachymetry map (0.845), anterior curvature map (0.836), total refractive power map (0.836), posterior elevation map (0.829) and anterior elevation map (0.820), in classifying the stage.ConclusionsDeep learning using the colour-coded maps obtained by the AS-OCT effectively discriminates keratoconus from normal corneas, and furthermore classifies the grade of the disease. It is suggested that this will become an aid for improving the diagnostic accuracy of keratoconus in daily practice.Clinical trial registration number000034587.

scholarly journals A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

2019 ◽  
Vol 203 ◽  
pp. 37-45 ◽  
Author(s):  
Huazhu Fu ◽  
Mani Baskaran ◽  
Yanwu Xu ◽  
Stephen Lin ◽  
Damon Wing Kee Wong ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Anterior Segment ◽  
Learning System ◽  
Angle Closure ◽  
Optical Coherence

scholarly journals Deep learning based models to study the effect of glaucoma genes on angle dysgenesis in-vivo

2021 ◽  
Author(s):  
Viney Gupta ◽  
Shweta Birla ◽  
Toshit Varshney ◽  
Bindu I Somarajan ◽  
Shikha Gupta ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Trabecular Meshwork ◽  
External Validation ◽  
Anterior Segment ◽  
Gene Mutations ◽  
Validation Dataset ◽  
Healthy Controls ◽  
Optical Coherence ◽  
Learning Models

Abstract Objective: To predict the presence of Angle Dysgenesis on Anterior Segment Optical Coherence Tomography (ADoA) using deep learning and to correlate ADoA with mutations in known glaucoma genes. Design: A cross-sectional observational study. Participants: Eight hundred, high definition anterior segment optical coherence tomography (ASOCT) B-scans were included, out of which 340 images (One scan per eye) were used to build the machine learning (ML) model and the rest were used for validation of ADoA. Out of 340 images, 170 scans included PCG (n=27), JOAG (n=86) and POAG (n=57) eyes and the rest were controls. The genetic validation dataset consisted of another 393 images of patients with known mutations compared with 320 images of healthy controls Methods: ADoA was defined as the absence of Schlemm's canal(SC), the presence of extensive hyper-reflectivity over the region of trabecular meshwork or a hyper-reflective membrane (HM) over the region of the trabecular meshwork. Deep learning was used to classify a given ASOCT image as either having angle dysgenesis or not. ADoA was then specifically looked for, on ASOCT images of patients with mutations in the known genes for glaucoma (MYOC, CYP1B1, FOXC1 and LTBP2). Main Outcome measures: Using Deep learning to identify ADoA in patients with known gene mutations. Results: Our three optimized deep learning models showed an accuracy > 95%, specificity >97% and sensitivity >96% in detecting angle dysgenesis on ASOCT in the internal test dataset. The area under receiver operating characteristic (AUROC) curve, based on the external validation cohort were 0.91 (95% CI, 0.88 to 0.95), 0.80 (95% CI, 0.75 to 0.86) and 0.86 (95% CI, 0.80 to 0.91) for the three models. Amongst the patients with known gene mutations, ADoA was observed among all the patients with MYOC mutations, as it was also observed among those with CYP1B1, FOXC1 and with LTBP2 mutations compared to only 5% of those healthy controls (with no glaucoma mutations). Conclusions: Three deep learning models were developed for a consensus-based outcome to objectively identify ADoA among glaucoma patients. All patients with MYOC mutations had ADoA as predicted by the models.

scholarly journals Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Kazutaka Kamiya ◽  
Yuji Ayatsuka ◽  
Yudai Kato ◽  
Nobuyuki Shoji ◽  
Takashi Miyai ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Anterior Segment ◽  
Optical Coherence

Pediatric Free-floating Anterior Chamber Cyst of the Iris Pigment Epithelium

2018 ◽  
Vol 1 ◽  
pp. 3
Author(s):  
Joshua S Agranat ◽  
Yoshihiro Yonekawa
Keyword(s):  
Optical Coherence Tomography ◽  
Anterior Chamber ◽  
Incidental Finding ◽  
Pigment Epithelium ◽  
Anterior Segment ◽  
Ultrasound Biomicroscopy ◽  
Optical Coherence ◽  
Visual Axis ◽  
Associated Malignancy ◽  
Anatomic Locations

Iris pigment epithelial (IPE) cysts are a subset of iris cysts that arise from the IPE. They are spontaneously erupting epithelial-lined cavities that are found in various anatomic locations of the iris, including the iris pupillary margin, midzone, periphery, and free floating in the vitreous or anterior chamber. We report the case of an asymptomatic 13-year-old boy with an incidental finding of a dislodged anterior chamber cyst diagnosed on routine examination. Modern multimodal image analysis of the cyst including anterior segment optical coherence tomography and ultrasound biomicroscopy (UBM) was utilized to characterize the microstructural anatomy of the lesion. The patient was managed conservatively without complications. Cysts of the IPE typically do not affect vision or ocular health and can be monitored and observed after ascertaining no associated malignancy. Initial diagnostic investigation can include UBM and anterior segment optical coherence tomography. Intervention should be reserved only for cases where the cyst growth leads to obstruction of the visual axis and/or other secondary complications.

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