1. Sina Farsiu

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    1. Mentioned In 100 Articles

    2. Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema

      Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema
      Anti-vascular endothelial growth factor (VEGF) agents are widely regarded as the first line of therapy for diabetic macular edema (DME) but are not universally effective. An automatic method that can predict whether a patient is likely to respond to anti-VEGF therapy can avoid unnecessary trial and error treatment strategies and promote the selection of more effective first-line therapies. The objective of this study is to automatically predict the efficacy of ...
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    3. Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome

      Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome
      Purpose To validate the efficacy of a fully-automatic, deep learning-based segmentation algorithm beyond conventional performance metrics by measuring the primary outcome of a clinical trial for macular telangiectasia type 2 (MacTel2) Design Evaluation of diagnostic test or technology Participants 92 eyes from 62 participants with MacTel2 from a phase 2 clinical trial (NCT01949324) randomized to one of two treatment groups Methods The ellipsoid zone (EZ) defect areas were measured on ...
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    4. Special Section Guest Editorial: Advances in Retinal Imaging

      Special Section Guest Editorial: Advances in Retinal Imaging
      The guest editorial provides an introduction to the Special Section on Advanced Retinal Imaging: Instrumentation, Methods, and Applications. The retina is a peripheral part of the central nerve system (CNS) and shares many similarities with the cerebral cortex. They both have layered anatomy, the same types of functional elements and neurotransmitters, and similar vascular organization and blood-tissue barriers. With far fewer neuronal cell types and simpler anatomical structures, the retina ...
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    5. Optical coherence refraction tomography

      Optical coherence refraction tomography
      Optical coherence tomography (OCT) is a cross-sectional, micrometre-scale imaging modality with widespread clinical application. Typical OCT systems sacrifice lateral resolution to achieve long depths of focus for bulk tissue imaging, and therefore tend to have better axial than lateral resolution. Such anisotropic resolution can obscure fine ultrastructural features. Furthermore, conventional OCT suffers from refraction-induced image distortions. Here, we introduce optical coherence refraction tomography (OCRT), which extends the superior axial resolution ...
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    6. Duke University Receives NIH Grant for Handheld Portable Adaptive Optics Optical Coherence Tomography for Imaging Young Children

      Duke University Receives NIH Grant for Handheld Portable Adaptive Optics Optical Coherence Tomography for Imaging Young Children
      ...rtable Adaptive Optics Optical Coherence Tomography for Imaging Young Children. The principal investigator is Sina Farsiu. The program began in 2019 and ends in 2021. Below is a summary of the proposed work.

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    7. Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2

      Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2
      Photoreceptor ellipsoid zone (EZ) defects visible on optical coherence tomography (OCT) are important imaging biomarkers for the onset and progression of macular diseases. As such, accurate quantification of EZ defects is paramount to monitor disease progression and treatment efficacy over time. We developed and trained a novel deep learning-based method called Deep OCT Atrophy Detection (DOCTAD) to automatically segment EZ defect areas by classifying 3-dimensional A-scan clusters as normal or ...
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    8. Comparison of chorioretinal layers in rhesus macaques using spectral-domain optical coherence tomography and high-resolution histological sections

      Comparison of chorioretinal layers in rhesus macaques using spectral-domain optical coherence tomography and high-resolution histological sections
      Nonhuman primates are important preclinical models of retinal diseases because they uniquely possess a macula similar to humans. Ocular imaging technologies such as spectral-domain optical coherence tomography (SD-OCT) allow noninvasive, in vivo measurements of chorioretinal layers with near-histological resolution. However, the boundaries are based on differences in reflectivity, and detailed correlations with histological tissue layers have not been explored in rhesus macaques, which are widely used for biomedical research. Here ...
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  2. About Sina Farsiu

    Sina Farsiu

    Sina Farsiu is an assistant professor in the departments of Ophthalmology and Biomedical Engineering (secondary appointment) and a faculty member of Fitzpatrick Institute for Photonics at Duke University. 

  3. Quotes

    1. We developed an algorithm that can be used to automatically analyze optical coherence tomography (OCT) images of the retina to predict whether a patient is likely to respond to anti-VEGF treatments...This research represents a step toward precision medicine, in which such predictions help clinicians better select first-line therapies for patients based on specific disease conditions.
      In Artificial intelligence predicts treatment outcome for diabetes-related vision loss
    2. When President Bush said he looked Vladimir Putin in the eye and got a sense of his soul, he was actually on to something...The retina is part of the central nervous system, and there is a lot you can learn by looking at it in detail.
      In Sina Farsiu: Biomedical Engineer Processes an Avalanche of Images
    3. Advancements in medical imaging hardware are both a blessing and a curse. The technology is extremely valuable, but it is also creating so much data that clinicians don’t have the time or the capacity to handle it all...We want to make automated algorithms that can condense the data into a few quantified measures that clinicians can use to make better diagnoses.
      In Sina Farsiu: Biomedical Engineer Processes an Avalanche of Images