Eye diseases of young children, if not detected and treated early, can lead to serious vision loss and even blindness. As an alternative to the classic 2-D color photographs, we have pioneered utilization of handheld spectral domain optical coherence tomography (HH-SDOCT) imaging systems to provide 3-D images of intra- retinal structures. Our previous studies based on HH-SDOCT of neonatal retina have already provided unique and previously unseen information about disease progression in young children. However, due to the limited resolution of conventional OCT, HH-SDOCT does not visualize individual photoreceptors. In vivo photoreceptor imaging, achieved by adaptive optics scanning laser ophthalmoscopy (AOSLO), has enhanced the way vision scientists and ophthalmologists understand retinal structure, function, and the etiology of numerous retinal pathologies in adults. Unfortunately, the complexity and large footprint of current AOSLO systems limits imaging to cooperative adults, excluding an important fraction of patients: small children, infants, and the bedridden. Our long-term goal is to improve the vision outcomes of at-risk young children with retinal diseases through earlier and better directed therapy. To achieve this goal, the overall objective of this proposal is to develop accurate yet portable and non-invasive diagnostic tools customized for young children care. In this need-driven proposal, we hypothesize that by taking advantage of recent advances in image processing and optics as an integrated technology, a small form-factor, portable hand-held (HH) AOSLO system capable of capturing retinal images with high resolution and motion stability can be obtained, which will visualize individual foveal photoreceptors and ultimately provide quantitative measurements of novel imaging biomarkers of the onset and progression of retinal diseases in young children. We will achieve our objectives by pursuit of the following specific aims: #1: Develop hardware to build the first HH-AOSLO system optimized for retinal imaging of young children. #2: Develop software to control the hardware in Aim 1 and to automatically quantify potential imaging biomarkers of the onset and progression of retinal diseases in young children. #3: Perform a pilot study in adults and young children. Evaluate, provide feedback, and improve the performance of methodologies in Aims 1&2, and then test the utility and validity of images and measurements compared to conventional diagnostic methods. The results of this study have the potential to provide practical diagnostic tools that will revolutionize the management of retinal diseases during the period of retinal development and maturation. This contribution would be significant as the first step in a continuum of research leading to better-directed therapy of ocular diseases in young children based on accurate quantitative measurement of disease imaging biomarkers and accurate staging of foveal development.

Public Health Relevance

Eye diseases of young children, if not detected and treated early, can lead to serious vision loss and even blindness. We propose to develop a technology to enable adaptive optics scanning laser ophthalmoscopy, currently widely used for imaging adults, as a novel and efficient ocular disease diagnostic tool for use in young children.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21EY027086-01
Application #
9167486
Study Section
Biomedical Imaging Technology A Study Section (BMIT-A)
Program Officer
Neuhold, Lisa
Project Start
2016-09-01
Project End
2018-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
1
Fiscal Year
2016
Total Cost
$192,742
Indirect Cost
$67,742
Name
Duke University
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Cunefare, David; Langlo, Christopher S; Patterson, Emily J et al. (2018) Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia. Biomed Opt Express 9:3740-3756
Cunefare, David; Fang, Leyuan; Cooper, Robert F et al. (2017) Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks. Sci Rep 7:6620
LaRocca, Francesco; Nankivil, Derek; DuBose, Theodore et al. (2016) In vivo cellular-resolution retinal imaging in infants and children using an ultracompact handheld probe. Nat Photonics 10:580-584