Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Tunable optical response of bowtie nanoantenna arrays on thermoplastic substrates.

Thermally responsive polymers present an interesting avenue for tuning the optical properties of nanomaterials on their surfaces by varying their periodicity and shape using facile processing methods. Gold bowtie nanoantenna arrays are fabricated using nanosphere lithography on prestressed polyolefin (PO), a thermoplastic polymer, and optical properties are investigated via a combination of spectroscopy and electromagnetic simulations to correlate shape evolution with optical response. Geometric features of bowtie nanoantennas evolve by annealing at temperatures between 105 °C and 135 °C by releasing the degree of prestress in PO. Due to the higher modulus of Au versus PO, compressive stress occurs on Au bowtie regions on PO, which leads to surface buckling at the two highest annealing temperatures; regions with a 5 nm gap between bowtie nanoantennas are observed and the average reduction is 75%. Reflectance spectroscopy and full-wave electromagnetic simulations both demonstrate the ability to tune the plasmon resonance wavelength with a window of approximately 90 nm in the range of annealing temperatures investigated. Surface-enhanced Raman scattering measurements demonstrate that maximum enhancement is observed as the excitation wavelength approaches the plasmon resonance of Au bowtie nanoantennas. Both the size and morphology tunability offered by PO allows for customizing optical response.