The goal of this project is to develop a web-based, semi-automated system for identifying severe retinopathy of prematurity (ROP) with """"""""plus disease,"""""""" using an existing data set of retinal images collected from previous NIH- funded research studies. ROP is treatable if diagnosed early, yet continues to be a leading cause of childhood blindness throughout the world. Diagnosis and documentation of ophthalmoscopic findings in ROP are subjective and qualitative, and studies have found that there is often significant diagnostic variation, even when experts are shown the exact same clinical data. Computer-based image analysis and the application of machine learning techniques to feature extraction and image classification have potential to address many of these limitations. Recent advances in image processing have had led to sophisticated techniques for tracing vessel-like structures. Additionally, machine-learning techniques will enable us to leverage these existing annotated image databases to improve the performance of our algorithms for vessel segmentation and disease classification. Our overall hypothesis is that retinal vascular features may be quantified and used to assist clinicians in the diagnosis of ROP. These hypotheses will be tested using two Specific Aims: (1) Develop and evaluate semi-automated algorithms to segment retinal vessels and generate a set of retinal vessel-based features. (2) Develop computer-based decision support algorithms that best correlate with expert opinions. Overall, this project will build upon infrastructure developed from previous studies, create potential for improving the accuracy and consistency of clinical ROP diagnosis, provide a demonstration of computer-based decision support from image analysis during real-world medical care, and stimulate future research toward understanding the vascular features associated with severe ROP. This project will be performed by a multi- disciplinary team of investigators with expertise in ophthalmology, biomedical informatics, computer science, machine learning, and image processing.
Retinopathy of prematurity (ROP) is a leading cause of childhood blindness in the United States and throughout the world, and current diagnostic and documentation methods are often subjective and qualitative. We propose to use existing data sets to develop a web- based, semi-automated system for identifying severe retinopathy of prematurity (ROP) with plus disease from retinal images. This has potential to improve the accuracy and consistency of ROP diagnosis, provide a demonstration of computer-based decision support from image analysis during real-world medical care, and stimulate future research toward understanding the vascular features associated with severe ROP.
|Moleta, Chace; Campbell, J Peter; Kalpathy-Cramer, Jayashree et al. (2017) Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007. Am J Ophthalmol 176:70-76|
|Campbell, J Peter; Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica et al. (2016) Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis. JAMA Ophthalmol 134:651-7|
|Kalpathy-Cramer, Jayashree; Campbell, J Peter; Erdogmus, Deniz et al. (2016) Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis. Ophthalmology 123:2345-2351|
|Chiang, Michael F (2016) Retinopathy of Prematurity: Imaging in retinopathy of prematurity: where are we, and where are we going? J AAPOS 20:474-476|
|Chiang, Michael F; Chan, R V Paul; Vinekar, Anand et al. (2016) Science and art in retinopathy of prematurity diagnosis. Graefes Arch Clin Exp Ophthalmol 254:201-2|
|Campbell, J Peter; Kalpathy-Cramer, Jayashree; Erdogmus, Deniz et al. (2016) Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability. Ophthalmology 123:2338-2344|
|Gupta, Mrinali Patel; Chan, R V Paul; Anzures, Rachelle et al. (2016) Practice Patterns in Retinopathy of Prematurity Treatment for Disease Milder Than Recommended by Guidelines. Am J Ophthalmol 163:1-10|
|Bolón-Canedo, V; Ataer-Cansizoglu, E; Erdogmus, D et al. (2015) Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach. Comput Methods Programs Biomed 122:1-15|
|Ataer-Cansizoglu, Esra; Bolon-Canedo, Veronica; Campbell, J Peter et al. (2015) Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the ""i-ROP"" System and Image Features Associated With Expert Diagnosis. Transl Vis Sci Technol 4:5|
|Ataer-Cansizoglu, Esra; Akcakaya, Murat; Orhan, Umut et al. (2014) Manifold Learning by Preserving Distance Orders. Pattern Recognit Lett 38:120-131|
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