The "F" in SAFE: Reliability of assessing clean faces for trachoma control in the field

<div><p>Background</p><p>Although facial cleanliness is part of the SAFE strategy for trachoma there is controversy over the reliability of measuring a clean face. A child’s face with no ocular and nasal discharge is clean and the endpoint of interest, regardless of the number of times it must be washed to achieve that endpoint. The issue of reliability rests on the reproducibility of graders to assess a clean face. We report the reproducibility of assessing a clean face in a field trial in Kongwa, Tanzania.</p><p>Methods/Findings</p><p>Seven graders were trained to assess the presence and absence of nasal and ocular discharge on children’s faces. Sixty children ages 1–7 years were recruited from a community and evaluated independently by seven graders, once and again about 50 minutes later. Intra-and inter-observer variation was calculated using unweighted kappa statistics. The average intra-observer agreement was kappa = 0.72, and the average inter-observer agreement was kappa = 0.78.</p><p>Conclusions</p><p>Intra-observer and inter-observer agreement was substantial for the assessment of clean faces using trained Tanzania staff who represent a variety of educational backgrounds. As long as training is provided, the estimate of clean faces in children should be reliable, and reflect the effort of families to keep ocular and nasal discharge off the faces. These data suggest assessment of clean faces could be added to trachoma surveys, which already measure environmental improvements, in districts.</p></div

Example of a minimal amount of nasal discharge.

<p>Example of a minimal amount of nasal discharge.</p

Example image of fly on face below the child’s right eye.

<p>Example image of fly on face below the child’s right eye.</p

Example of ocular discharge and a crusty eyelid.

<p>Example of ocular discharge and a crusty eyelid.</p

Inter-observer agreement (Kappa score) between grader 1 and graders 2 through 7.

<p>Inter-observer agreement (Kappa score) between grader 1 and graders 2 through 7.</p

Example of agreement for clean face.

<p>Example of agreement for clean face.</p

Automatic segmentation and quantification of OCT images pre and post cataract surgery using deep learning.

Obtaining quantitative geometry of the anterior segment of the eye, generally from Optical Coherence Tomography (OCT) images, is important to construct 3-D computer eye models, used to understand the optical quality of the normal and pathological eye, and to improve treatment (for example, selecting the intraocular lens to be implanted in cataract surgery, or guiding refractive surgery). An important step to quantify OCT images is segmentation (i.e., finding and labeling the surfaces of interest in the images) which for the purpose of feeding optical models, needs to be automatic, accurate, robust, and fast. In this work, we designed a segmentation algorithm based on deep learning, which we applied to OCT images from pre- and post-cataract surgery eyes obtained using anterior segment OCT commercial systems. We proposed a feature pyramid network architecture with a pre-trained encoder, and trained, validated, and tested the algorithm using 1640 OCT images. We showed that the proposed method outperformed a classical image processing-based approach in terms of accuracy (from 91.4% to 93.2% accuracy), robustness (decreasing the standard deviation of accuracy across images by a factor of 1.7), and processing time (from 0.48 s/image to 0.34 s/image). We also described a method for the 3-D models’ construction and their quantification from the segmented images and applied the proposed segmentation/quantification algorithms to quantify 136 new eye measurements (780 images) obtained from OCT commercial systems