Purpose Specular and confocal microscopes are important tools to monitor the health of the corneal endothelium (CE), but their high costs significantly limit accessibility in low-resource environments. We developed and validated a low-cost, fully automated method to quantitatively evaluate the CE using smartphone-based specular microscopy. Methods A OnePlus 7 Pro smartphone attached to a Topcon SL-D701 slit-lamp was used to image the central corneal endothelium of 30 eyes using the specular reflection technique. A novel on-device image processing algorithm automatically computed endothelial cell density (ECD), percentage of hexagonal cells (HEX), and coefficient of variation (CV) values. These values were compared with the ECD, HEX, and CV generated by a Tomey EM-4000 specular microscope used to image the same set of eyes. Results No significant differences were found in ECD (2799 ± 156 cells/mm 2 vs. 2779 ± 166 cells/mm 2 ; P = 0.28) and HEX (52 ± 6% vs. 53 ± 6%; P = 0.50) computed by smartphone-based specular imaging and specular microscope, respectively. A statistically significant difference in CV (34 ± 3% vs. 30 ± 3%; P < 0.01) was found between the two methods. The concordance achieved between the smartphone-based method and the Tomey specular microscope is very similar to the concordance between two specular microscopes reported in the literature. Conclusions Smartphone-based specular imaging and automated analysis is a low-cost method to quantitatively evaluate the CE with accuracy comparable to the clinical standard. Translational Relevance This tool can be used to screen the CE in low-resource regions and prompt investigation of suspected corneal endotheliopathies.
PurposeSpecular and confocal microscopes are important tools to monitor the health of the corneal endothelium (CE), but their high costs significantly limit accessibility in low-resource environments. In our study, we developed and validated a low-cost, fully automated method to quantitatively evaluate the CE using smartphone-based specular microscopy.MethodsA OnePlus 7 Pro smartphone attached to a Topcon SL-D701 slit-lamp was used to image normal the central corneal endothelium using the specular reflection technique. Images were automatically processed on-device and endothelial cell density (ECD), percentage of hexagonal cells (HEX), and coefficient of variation (CV) values were determined using our novel image analysis algorithm. The morphometric parameters generated from the images taken by Tomey EM-4000 specular microscope were compared between the testing modalities.ResultsNo significant differences in ECD (2799 ± 156 cells/mm2 vs 2779 ± 166 cells/mm2; p=0.28) and HEX (52 ± 6% vs 53 ± 6%; p=0.50) computed by smartphone-based specular imaging and specular microscope, respectively, were found. A statistically significant difference in CV (34 ± 3% vs 30 ± 3%; p<0.01) was found between the two methods. The concordance achieved between the smartphone-based method and the Tomey specular microscope is very similar to the concordance between two specular microscopes reported in the literature.ConclusionsSmartphone-based specular imaging and automated analysis is a low-cost method to quantitatively evaluate the CE with accuracy comparable to the clinical standard.Translational RelevanceThis tool can be used to screen the CE in low-resource regions and reveal the need for further investigation of suspected corneal endotheliopathies.
Background Conventional means for dementia diagnosis rely on qualitative tests usually administered after significant pathogenesis. Past studies suggest the utility of more quantitative analytical approaches such as handwriting/drawing tasks (Impedevo et al., 2018). Such tools would provide low‐cost, portable, and instantaneous quantitative diagnostics for more efficient patient screening. However, efforts to realize these methods have faced challenges such as low sample size, incomplete feature extraction, and lack of task diversity. We attempted to create a tablet application that uses pen‐tracking technology to surmount these challenges. Method As fine motor control provides fundamental markers of neurological health (Bisio et al., 2017; Thomas et al., 2017), rigorous statistical analysis of simple drawing tasks on a tablet permitted differentiation between neuronormative patients and dementia patients with high fidelity. We have started testing our data analysis pipeline with open access datasets: PaHaW (Drotár et al., 2016), Isuniba (Impedovo et al, 2013), ParkinsonHW (Isenkul et al., 2014). These datasets contain drawing data for healthy individuals and those with both dementia and other neurodegenerative diseases. They contain similar raw data that the in‐house iPad app collects. From that raw data, we extracted predictive features, including velocity, acceleration, jerk, curvature, and measures of variation. Result We have successfully created an iPad app that is able to record the dynamic handwriting process with an Apple Pencil. Our platform has the potential to generate more standardized datasets with improved documentation compared to existing archives. Patients trace complex figures such as spirals and infinity symbols at varying speeds over multiple trials. Additionally, the subjects are asked to remember and draw a shape that was presented to them at the beginning of the test. The app collects key data such as the position of the pen tip, velocity of pen movement, pen angle relative to the surface, and pressure exerted on the surface. Conclusion We plan to deploy our in‐house iPad app in clinical trials to collect pen‐tracking data with which to facilitate differential diagnoses for neurodegenerative diseases afflicting Alzheimer’s and Parkinson’s patients. The validation of such a platform would improve upon existing diagnostic datasets and lower major barriers to dementia screening.
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