1. Articles from Alessandro A. Jammal

    1-9 of 9
    1. Predicting Age From Optical Coherence Tomography Scans With Deep Learning

      Predicting Age From Optical Coherence Tomography Scans With Deep Learning

      Purpose : To assess whether age can be predicted from deep learning analysis of peripapillary spectral-domain optical coherence tomography (SD-OCT) B-scans and to determine the importance of specific retinal areas on the predictions. Methods : Deep learning (DL) convolutional neural networks were developed to predict chronological age in healthy subjects using peripapillary SD-OCT B-scan images. Models were built using the whole B-scan, as well as using specific regions through image ablation. Cross-validation was used for training and testing the model. Mean absolute error (MAE) and correlations between predicted and observed age were used to evaluate model performance. Results : A total of 7271 ...

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      Mentions: Duke University
    2. A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

      A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

      Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of artificial intelligence, with performances that often equal, or sometimes even exceed, those of human subjects on a variety of tasks, especially those related to image classification and pattern recognition. As one of the medical fields that is highly dependent on ancillary imaging tests, ophthalmology has been in a prime position to witness the application of deep learning algorithms that can help analyze the vast amount of data coming from those tests. In particular, glaucoma stands as one of the ...

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    3. Artificial Intelligence Mapping of Structure to Function in Glaucoma

      Artificial Intelligence Mapping of Structure to Function in Glaucoma

      Purpose : To develop an artificial intelligence (AI)–based structure-function (SF) map relating retinal nerve fiber layer (RNFL) damage on spectral domain optical coherence tomography (SDOCT) to functional loss on standard automated perimetry (SAP). Methods : The study included 26,499 pairs of SAP and SDOCT from 15,173 eyes of 8878 patients with glaucoma or suspected of having the disease extracted from the Duke Glaucoma Registry. The data set was randomly divided at the patient level in training and test sets. A convolutional neural network (CNN) was initially trained and validated to predict the 52 sensitivity threshold points of the 24-2 ...

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    4. Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach

      Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach

      This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as targets, forcing the DL algorithm to learn its own representation of RNFL. The algorithm was tested in three different sets: (1) images without segmentation errors or artefacts, (2) low-quality images with segmentation errors, and (3) images with other artefacts. In test set 1 ...

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    5. Human versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

      Human versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs

      Abstract Purpose To compare the diagnostic performance of human gradings versus predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm trained to quantify retinal nerve fiber layer (RNFL) damage on fundus photographs. Design Evaluation of a machine learning algorithm. Methods A M2M DL algorithm trained with RNFL thickness parameters from spectral-domain optical coherence tomography was applied to a subset of 490 fundus photos of 490 eyes of 370 subjects graded by two glaucoma specialists for the probability of glaucomatous optical neuropathy (GON), and estimates of cup-to-disc (C/D) ratios. Spearman correlations with standard automated perimetry (SAP) global indices were ...

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    6. Comparison of Short- And Long-Term Variability On Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma

      Comparison of Short- And Long-Term Variability On Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma

      Purpose To assess short-term and long-term variability on standard automated perimetry (SAP) and spectral domain optical coherence tomography (SD-OCT) in glaucoma. Design Prospective cohort. Methods Ordinary least squares linear regression of SAP mean deviation (MD) and SD-OCT global retinal nerve fiber layer (RNFL) thickness were fitted over time for sequential tests conducted within 5 weeks (short-term testing) and annually (long-term testing). Residuals were obtained by subtracting the predicted and observed values and each patient’s standard deviation (SD) of the residuals was used as a measure of variability. Wilcoxon signed-rank test was performed to test the hypothesis of equality between ...

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    7. Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm

      Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm

      n this study we developed a deep learning (DL) algorithm that detects errors in retinal never fibre layer (RNFL) segmentation on spectral-domain optical coherence tomography (SDOCT) B-scans using human grades as the reference standard. A dataset of 25,250 SDOCT B-scans reviewed for segmentation errors by human graders was randomly divided into validation plus training (50%) and test (50%) sets. The performance of the DL algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The ability of the algorithm to detect segmentation errors was evaluated with the area under the ...

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    8. Performance of the “Rule of 5” for Detecting Glaucoma Progression Between Visits with Optical Coherence Tomography

      Performance of the “Rule of 5” for Detecting Glaucoma Progression Between Visits with Optical Coherence Tomography

      Purpose To evaluate whether loss of 5 μm in global retinal nerve fiber layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT) between two consecutive visits is specific for glaucoma progression. Design Prospective cohort. Participants 92 eyes in 49 controls and 300 eyes in 210 glaucoma subjects. Methods Study subjects completed at least five standard automated perimetry and SDOCT examinations at 6-month intervals over at least 2 years. Eyes were categorized as progressing from glaucoma if the average RNFL declined by 5 μm between two consecutive visits. The false positive proportion was estimated by two methods: 1) 5 μm loss ...

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    9. A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

      A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

      Purpose To train a deep learning (DL) algorithm that quantifies glaucomatous neuroretinal damage on fundus photographs using the minimum rim width relative to Bruch’s membrane opening (BMO-MRW) from spectral domain-optical coherence tomography (SDOCT). Design Cross-sectional study Methods 9,282 pairs of optic disc photographs and SDOCT optic nerve head scans from 927 eyes of 490 subjects were randomly divided into the validation plus training (80%) and test sets (20%). A DL convolutional neural network was trained to predict the SDOCT BMO-MRW global and sector values when evaluating optic disc photographs. The predictions of the DL network were compared to ...

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    1-9 of 9
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  2. Topics in the News

    1. (8 articles) Felipe A. Medeiros
    2. (7 articles) Duke University
    3. (2 articles) UCSD
    4. (1 articles) Universidade Federal de São Paulo
    5. (1 articles) Yamagata University
    6. (1 articles) Bern University of Applied Sciences
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    A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs Performance of the “Rule of 5” for Detecting Glaucoma Progression Between Visits with Optical Coherence Tomography Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm Comparison of Short- And Long-Term Variability On Standard Perimetry and Spectral Domain Optical Coherence Tomography in Glaucoma Human versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach Artificial Intelligence Mapping of Structure to Function in Glaucoma A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression Predicting Age From Optical Coherence Tomography Scans With Deep Learning Hybrid registration of retinal fluorescein angiography and optical coherence tomography images of patients with diabetic retinopathy Optical Coherence Tomography Angiography Quality Across Three Multicenter Clinical Studies of Diabetic Retinopathy The Role of Optical Coherence Tomography Angiography(OCTA) in Detecting Choroidal Neovascularization in DifferentStages of Best Macular Dystrophy: A Case Series