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SIGNIFICANCE: Recent evidence suggests that hydroxyapatite (HAP) in sub-retinal pigment epithelial (sub-RPE) deposits in aged human eyes may act to nucleate and contribute to their growth to clinically detectable size. Sub-RPE deposits such as drusen are clinical hallmarks of age-related macular degeneration (AMD), therefore enhanced and earlier detection is a clinical need. We found that tetracycline-family antibiotics, long known to stain HAP in teeth and bones, can also label the HAP in sub-RPE deposits. However, HAP-bound tetracycline fluorescence excitation and emission spectra overlap with the well-known autofluorescence of outer retinal tissues, making them difficult to resolve.AIM: In this initial study, we sought to determine if the HAP-bound tetracyclines also exhibit enhanced fluorescence lifetimes, providing a useful difference in lifetime compared with the short lifetimes observed in vivo in the human retina by the pioneering work of Schweitzer, Zinkernagel, Hammer, and their colleagues, and thus a large enough effect size to resolve the HAP from background by fluorescence lifetime imaging.APPROACH: We stained authentic HAP with tetracyclines and measured the lifetime(s) by phase fluorometry, and stained aged, fixed human cadaver retinas with drusen with selected tetracyclines and imaged them by fluorescence lifetime imaging microscopy (FLIM).RESULTS: We found that chlortetracycline and doxycycline exhibited substantial increase in fluorescence lifetime compared to the free antibiotics and the retinal background, and the drusen were easily resolvable from the retinal background in these specimens by FLIM.CONCLUSIONS: These findings suggest that FLIM imaging of tetracycline (and potentially other molecules) binding to HAP could become a diagnostic tool for the development and progression of AMD.

Age-related macular degeneration (AMD) is a vision-threatening disease that affects the outer retina and choroid of elderly adults. Because photoreceptors are found in the outer retina and rely primarily on the trophic support of the underlying choriocapillaris, imaging of flow or lack thereof in choriocapillaris by optical coherence tomography angiography (OCTA) has great clinical potential in AMD assessment. We introduce a metric using OCTA, named “focal perfusion loss” (FPL) to describe the effects of age and non-neovascular AMD on choriocapillaris flow. Because OCTA imaging of choriocapillaris is vulnerable to artifacts—namely motion, projections, segmentation errors, and shadows—they are removed by postprocessing software. The shadow detection software is a machine learning algorithm recently developed for the evaluation of the retinal circulation and here adapted for choriocapillaris analysis. It aims to exclude areas with unreliable flow signal due to blocking of the OCT beam by objects anterior to the choriocapillaris (e.g., drusen, retinal vessels, vitreous floaters, and iris). We found that both the FPL and the capillary density were able to detect changes in the choriocapillaris of AMD and healthy age-matched subjects with respect to young controls. The dominant cause of shadowing in AMD is drusen, and the shadow exclusion algorithm helps determine which areas under drusen retain sufficient signal for perfusion evaluation and which areas must be excluded. Such analysis allowed us to determine unambiguously that choriocapillaris density under drusen is indeed reduced.

In conventional retinal region detection methods for optical coherence tomography (OCT) images, many parameters need to be set manually, which is often detrimental to their generalizability. We present a scheme to detect retinal regions based on fully convolutional networks (FCN) for automatic diagnosis of abnormal maculae in OCT images. The FCN model is trained on 900 labeled age-related macular degeneration (AMD), diabetic macular edema (DME) and normal (NOR) OCT images. Its segmentation accuracy is validated and its effectiveness in recognizing abnormal maculae in OCT images is tested and compared with traditional methods, by using the spatial pyramid matching based on sparse coding (ScSPM) classifier and Inception V3 classifier on two datasets: Duke dataset and our clinic dataset. In our clinic dataset, we randomly selected half of the B-scans of each class (300 AMD, 300 DME, and 300 NOR) for training classifier and the rest (300 AMD, 300 DME, and 300 NOR) for testing with 10 repetitions. Average accuracy, sensitivity, and specificity of 98.69%, 98.03%, and 99.01% are obtained by using ScSPM classifier, and those of 99.69%, 99.53%, and 99.77% are obtained by using Inception V3 classifier. These two classification algorithms achieve 100% classification accuracy when directly applied to Duke dataset, where all the 45 OCT volumes are used as test set. Finally, FCN model with or without flattening and cropping and its influence on classification performance are discussed.

The vascular endothelial growth factor (VEGF) level in aqueous humor has been used as an indicator to monitor specific diseases in the retinal ischemic condition. For clinical diagnosis, only about 200 μL of aqueous humor can be collected from the anterior chamber before the threat of anterior chamber collapse. It is necessary to develop an inexpensive diagnostic approach with the characteristics of highly sensitive, short operation duration, and requires small clinical sample quantities. To achieve the main objective of this study, we first prepared bevacizumab to be conjugated with HRP. We then deposited 2 μL aqueous humor from patients with different diseases onto each test zone of paper-based 96-well plates. After the colorimetric results were performed via ELISA protocol, the output signals were recorded using a commercial desktop scanner for analysis. In this study, only 2 μL from the aqueous humor of each patient was required for paper-based ELISA. The mean aqueous VEGF level was 14.4 pg/mL from thirteen patients (N = 13) with senile cataract as the control. However, the mean aqueous VEGF level from other patients with proliferative diabetic retinopathy (N = 14), age-related macular degeneration (N = 17), and retinal vein occlusion (N = 10) showed VEGF increases to 740.1 pg/mL, 383 pg/mL, and 219.4 pg/mL, respectively.

To evaluate quantitative and qualitative differences in documentation of the ophthalmic examination between paper and electronic health record (EHR) systems.Comparative case series.One hundred fifty consecutive pairs of matched paper and EHR notes, documented by 3 attending ophthalmologist providers.An academic ophthalmology department implemented an EHR system in 2006. Database queries were performed to identify cases in which the same problems were documented by the same provider on different dates, using paper versus EHR methods. This was done for 50 consecutive pairs of examinations in 3 different diseases: age-related macular degeneration (AMD), glaucoma, and pigmented choroidal lesions (PCLs). Quantitative measures were used to compare completeness of documenting the complete ophthalmologic examination, as well as disease-specific critical findings using paper versus an EHR system. Qualitative differences in paper versus EHR documentation were illustrated by selecting representative paired examples.(1) Documentation score, defined as the number of examination elements recorded for the slit-lamp examination, fundus examination, and complete ophthalmologic examination and for critical clinical findings for each disease. (2) Paired comparison of qualitative differences in paper versus EHR documentation.For all 3 diseases (AMD, glaucoma, PCL), the number of complete examination findings recorded was significantly lower with paper than the EHR system (P ≤ 0.004). Among the 3 individual examination sections (general, slit lamp, fundus) for the 3 diseases, 5 of the 9 possible combinations had significantly lower mean documentation scores with paper than EHR notes. For 2 of the 3 diseases, the number of critical clinical findings recorded was significantly lower using paper versus EHR notes (P ≤ 0.022). All (150/150) paper notes relied on graphical representations using annotated hand-drawn sketches, whereas no (0/150) EHR notes contained drawings. Instead, the EHR systems documented clinical findings using textual descriptions and interpretations.There were quantitative and qualitative differences in the nature of paper versus EHR documentation of ophthalmic findings in this study. The EHR notes included more complete documentation of examination elements using structured textual descriptions and interpretations, whereas paper notes used graphical representations of findings.The author(s) have no proprietary or commercial interest in any materials discussed in this article.