1. Felipe A. Medeiros

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    1. Mentioned In 58 Articles

    2. A Comparison of OCT Parameters in Identifying Glaucoma Damage in Eyes Suspected of Having Glaucoma

      A Comparison of OCT Parameters in Identifying Glaucoma Damage in Eyes Suspected of Having Glaucoma
      Purpose To compare retinal nerve fiber layer thickness (RNFLT) and Bruchs membrane opening (BMO) minimum rim width (MRW) measured by spectral-domain (SD) OCT for diagnosing glaucoma in those suspected of having the disease. Design Observational cohort study. Participants One hundred thirteen eyes from 81 patients suspected of having glaucoma based on optic nerve appearance. Methods Participants were imaged using SD OCT, and RNFLT and BMO MRW were measured. All participants ...
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    3. Faster. Stronger. Smarter.

      Faster. Stronger. Smarter.
      Artificial intelligence is shaping the future of eye careand Duke is leading the way. Artificial intelligence (AI) leverages the growing power and speed of computers to solve complex problems. AI is transforming medical research and clinical practiceand ophthalmology is leading the way. One of the most successful ways that AI has been applied recently is in the area of computer vision, which is the assessment and interpretation of images, explains ...
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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 ...
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    5. Sample Size Requirements of Glaucoma Clinical Trials When Using Combined Optical Coherence Tomography and Visual Field Endpoints

      Sample Size Requirements of Glaucoma Clinical Trials When Using Combined Optical Coherence Tomography and Visual Field Endpoints
      Glaucoma clinical trials using visual field (VF) endpoints currently require large sample sizes because of the slowly-progressive nature of this disease. We sought to examine whether the combined use of VF testing and non-invasive optical coherence tomography (OCT) imaging of the neuroretinal tissue could improve the feasibility of such trials. To examine this, we included 192 eyes of 121 glaucoma participants seen at 5 visits over a 2-year period to ...
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    6. 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 ...
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    7. 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 ...
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    8. 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 ...
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    9. 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 m 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 ...
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    10. 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 Bruchs 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 ...
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    11. Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression

      Retinal Nerve Fiber Layer Features Identified by Unsupervised Machine Learning on Optical Coherence Tomography Scans Predict Glaucoma Progression
      Purpose : To apply computational techniques to wide-angle swept-source optical coherence tomography (SS-OCT) images to identify novel, glaucoma-related structural features and improve detection of glaucoma and prediction of future glaucomatous progression. Methods : Wide-angle SS-OCT, OCT circumpapillary retinal nerve fiber layer (cpRNFL) circle scans spectral-domain (SD)-OCT, standard automated perimetry (SAP), and frequency doubling technology (FDT) visual field tests were completed every 3 months for 2 years from a cohort of 28 ...
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    12. The Association Between Macula and ONH Optical Coherence Tomography Angiography (OCT-A) Vessel Densities in Glaucoma, Glaucoma Suspect, and Healthy Eyes

      The Association Between Macula and ONH Optical Coherence Tomography Angiography (OCT-A) Vessel Densities in Glaucoma, Glaucoma Suspect, and Healthy Eyes
      Purpose: To evaluate strength of associations between optical coherence tomography (OCT)-angiography vessel density (VD) measurements in the macula and peripapillary region of the optic nerve head (ONH) with standard structural OCT thickness measures. Materials and Methods: This cross-sectional study included 333 eyes of 219 primary open-angle glaucoma patients, 41 glaucoma suspects, and 73 healthy participants from the Diagnostic Innovations in Glaucoma Study (DIGS) with good quality OCT angiography images ...
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    13. The Association between Macula and ONH Optical Coherence Tomography Angiography (OCT-A) Vessel Densities in Glaucoma, Glaucoma Suspect and Healthy Eyes

      The Association between Macula and ONH Optical Coherence Tomography Angiography (OCT-A) Vessel Densities in Glaucoma, Glaucoma Suspect and Healthy Eyes
      Purpose: To evaluate strength of associations between Optical Coherence Tomography-Angiography (OCT-A) vessel density (VD) measurements in the macula and peripapillary region of the optic nerve head (ONH) with standard structural OCT thickness measures. Methods: This cross-sectional study included 333 eyes of 219 primary open angle glaucoma patients, 41 glaucoma suspects, and 73 healthy participants from the Diagnostics Innovations in Glaucoma Study (DIGS) with good quality OCT-A images. The strength of ...
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    14. Detecting Structural Progression in Glaucoma with Optical Coherence Tomography

      Detecting Structural Progression in Glaucoma with Optical Coherence Tomography
      Optical coherence tomography (OCT) is increasingly used to obtain objective measurements of the retinal nerve fiber layer (RNFL), optic nerve head, and macula for assessing glaucoma progression. Although OCT has been adopted widely in clinical practice, uncertainty remains concerning its optimal role. Questions include: What is the best structure to measure? What quantity of change is significant? Are structural changes relevant to the patient? How are longitudinal measurements affected by ...
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    15. Comparing optical coherence tomography radial and cube scan patterns for measuring Bruch's membrane opening minimum rim width (BMO-MRW) in glaucoma and healthy eyes: cross-sectional and longitudinal analysis

      Comparing optical coherence tomography radial and cube scan patterns for measuring Bruch's membrane opening minimum rim width (BMO-MRW) in glaucoma and healthy eyes: cross-sectional and longitudinal analysis
      Aim To compare the cube and radial scan patterns of the spectral domain optical coherence tomography (SD-OCT) for quantifying the Bruch's membrane opening minimum rim width (BMO-MRW). Methods Sixty healthy eyes and 189 glaucomatous eyes were included. The optic nerve head cube and radial pattern scans were acquired using Spectralis SD-OCT. BMO-MRWs were automatically delineated using the San Diego Automated Layer Segmentation Algorithm. The BMO-MRW diagnostic accuracy for glaucoma ...
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  2. About Felipe A. Medeiros

    Felipe A. Medeiros

    Felipe A. Medeiros is an Associate Professor of Clinical Ophthalmology at Duke

  3. Quotes

    1. In ophthalmology we have a wealth of eye images—fundus photographs, optical coherence tomography (OCT), etc.—that are fundamental to the diagnosis, monitoring, and treatment of a number of eye diseases, so this field is ideally suited for deep learning applications.
      In Faster. Stronger. Smarter.
    2. Stratus OCT RNFL parameters were able to discriminate eyes with progressing disease by visual fields or optic disc photographs with eyes that remained stable according to these methods, and performed significantly better than [optic nerve head] and macular thickness parameters in detecting change over time.
      In Delving into glaucoma