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We provide two options for acquiring images for external use. Below we have some preselected images that are ready for downloading (simply right click on the image and save). Or, at the bottom of the page, we have an order form for requesting a specific photo.

The major advantage of using this function is that it is portable in the sense that it works for all document formats that knitr supports, so you do not need to think if you have to use, for example, LaTeX or Markdown syntax, to embed an external image. Chunk options related to graphics output that work for normal R plots also work for these images, such as out.width and out.height.

[TRENDING] WPK External Images Gallery

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Notes on external images: External images might be under copyright. If you do not get permission to use it, you may be in violation of copyright laws. In addition, you cannot control external images; they can suddenly be removed or changed.

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

Because these external eye images were taken with fundus cameras, we next sought to examine whether lower-quality images would suffice. To simulate low-quality images, the input images were down-sampled to lower resolutions for both training and evaluation (Methods). The DLS performance decreased slightly as the input size decreased to 75 pixels and then substantially as the input size decreased below 35 pixels (Fig. 2c,d).

The discovery that predictions about systemic parameters and diabetic retinal disease could be derived from external eye photography is surprising, since such images are primarily used to identify and monitor anterior eye conditions, such as eyelid and conjunctival malignancies, corneal infections and cataracts. Although there are numerous studies noting that conjunctival vessel changes (fewer, wider and less tortuous conjunctival vessels)9,10,15,16,17 are associated with duration of diabetes18,19 and severity of DR20,21, and elevated cholesterol levels and atherosclerosis have been linked to xanthelasmas (yellowish deposit under the eyelid skin)22. However, to our knowledge, there have been no large studies linking HbA1c or diabetic macular oedema to conjunctival vessel changes in diabetes. Furthermore, conjunctival vessel assessment for signs of diabetes or elevated lipids is not a common clinical practice due to the subjectivity and time-consuming nature of such an evaluation and the option of a more accurate and easier test for the clinician (HbA1c). We verified that these surprising results were reproducible and not an artefact of a single dataset or site via broad geographical validation across 18 US states.

In summary, we demonstrated that external eye images can be used to detect the presence of several conditions, such as poor blood sugar control, elevated lipids and various diabetic retinal diseases. Further study is warranted to evaluate whether such a tool can be used in a home, pharmacy or primary care setting to improve disease screening and help with management of diabetes.

As part of the standard imaging protocol for DR screening at these sites, patients had photographs taken of the external eye for evaluation of the anterior segment (along with the retinal fundus photograph). At sites served by EyePACS, cameras (where this information was available) included Canon (CR1 and CR2), Topcon (NW200 and NW400), Zeiss Visucam, Optovue iCam and Centervue DRS. On the basis of available image Exif metadata, images were digitized using Canon EOS single-lens reflex cameras. The image protocol involved first positioning patients comfortably in front of the camera with their chin resting on the chin rest and their forehead an inch away from the forehead brace. The operator then took external images of the right eye and then the left eye, ensuring image clarity, focus and visibility of the entire eye, including the iris, pupil and sclera60. At sites served by the VA diabetic teleretinal screening programme, the cameras included Topcon NW8 and Topcon NW400. The acquisition protocol involved slightly distancing the patient from the fundus camera to capture a clear view of the external eye; further alignment was entrusted to the operator. The external eye photographs were intended to document findings such as cataracts and eyelid lesions. None of the external eye images was excluded for image quality or other reasons.

To better understand whether pupil size was associated with our results, we obtained segmentations of both pupil and iris size. To do so, 12 ophthalmologist graders evaluated a total of 5,000 external eye images. If the pupil and iris were distinct enough to delineate the borders accurately, the graders drew ellipses around both the iris and pupil (Supplementary Fig. 1). This was performed for a subset of 4,000 randomly selected images in the development set, another 500 images in validation sets A and B combined and 500 images in validation set C. We then trained a model to segment the iris and pupil using the labelled development set images and evaluated the accuracy in the validation sets (Supplementary Note 1 and Supplementary Fig. 2). Our pupil size analyses were based on running this model across all images. be457b7860