Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential for human error. This study describes the development and evaluation of machine learning models intended to reduce cost and improve reliability of these surveys. METHODS: Fifty-six thousand seven hundred twenty-five everted eyelid photographs were obtained from 11,358 children of age 0 to 9 years in a single trachoma-endemic region of Ethiopia over a 3-year period. Expert graders reviewed all images from each examination to determine the estimated number of tarsal conjunctival follicles and the degree of trachomatous inflammation-intense. The median estimate of the 3 grader groups was used as the ground truth to train a MobileNetV3 large deep convolutional neural network to detect cases with TF. RESULTS: The classification model predicted a TF prevalence of 32%, which was not significantly different from the human consensus estimate (30%; 95% confidence interval of difference, -2 to +4%). The model had an area under the receiver operating characteristic curve of 0.943, F1 score of 0.923, 88% accuracy, 83% sensitivity, and 91% specificity. The area under the receiver operating characteristic curve increased to 0.995 when interpreting nonborderline cases of TF. CONCLUSIONS: Deep convolutional neural network models performed well at classifying TF and detecting the number of follicles evident in conjunctival photographs. Implementation of similar models may enable accurate, efficient, large-scale trachoma screening. Further validation in diverse populations with varying TF prevalence is needed before implementation at scale.

Photographic grading to evaluate facial cleanliness and trachoma among children in Amhara region, Ethiopia.

BACKGROUND: Promotion of facial cleanliness is recommended for the elimination of blinding trachoma, largely because of observational studies that have found an association between various measures of facial uncleanliness and trachoma. However, when a field grader assesses both facial cleanliness and trachoma, associations may be biased. Assessment of photographs of the face and conjunctiva by masked graders may provide a less biased estimate of the relationship between facial cleanliness and trachoma. METHODS: Face photographs, conjunctival photographs, and conjunctival swabs were obtained on a random sample of 0-9-year-old children from each of 40 communities in Amhara region, Ethiopia. Face photographs were assessed for the presence of seven measures of an unclean face (i.e., wet nasal discharge, dry nasal discharge, wet ocular discharge, dry ocular discharge, food, dust/dirt, and flies) by three independent masked photo-graders. Conjunctival photographs were similarly graded in a masked fashion for signs of clinically active trachoma. Conjunctival swabs were processed for Chlamydia trachomatis DNA. RESULTS: Of 2073 children with complete data, 808 (39%) had evidence of clinically active trachoma, 150 (7%) had evidence of ocular chlamydia infection, and 2524 (91%) had at least one measure of an unclean face. Dry ocular discharge had the strongest association with clinically active trachoma (age- and sex-adjusted prevalence ratio [PR] 1.4, 95% CI 1.2-1.6) and ocular chlamydia infection (PR 1.9, 95%CI 1.3-2.9), although significant associations were observed between each of the measures of facial uncleanliness and trachoma. CONCLUSIONS: Masked assessment of face and conjunctival photographs confirmed prior observational studies that have noted associations between various measures of facial uncleanliness and trachoma. The causal relationship between facial uncleanliness and trachoma is unclear since many features used to measure facial cleanliness (e.g., ocular discharge, nasal discharge, and flies) could be consequences of antecedent ocular chlamydia infection. TRIAL REGISTRATION: NCT02754583, clinicaltrials.gov.

Photographic grading for trachoma diagnosis within trachoma impact surveys in Amhara region, Ethiopia.

BACKGROUND: As countries reach the trachoma elimination threshold and cases of trachomatous inflammation follicular (TF) become rare, it becomes difficult to train survey graders to recognize clinical signs. We assess the use of photography as a grading tool, the efficiency of an in-country grading center and the comparability of field and photographic grading. METHODS: During January-February 2017 surveys in Amhara, Ethiopia, field graders assessed TF, trachomatous inflammation intense (TI) and trachomatous scarring (TS). Photographs were taken from each conjunctiva and later graded at the Gondar Grading Center (GGC) at the University of Gondar in Amhara. Two trained ophthalmology residents graded each set of photographs and a third grader provided an adjudicating grade when needed. RESULTS: A total of 4953 photographs of 2477 conjunctivae from 1241 participants in 10 communities were graded over 5 d at the GGC. Six examined participants were not photographed. Agreement between field and photographic grades were for TF: percent agreement (PA) 96.7%, κ=0.70 (95% confidence interval [CI] 0.64 to 0.77; for TI: PA 94.7%, κ=0.32 (95% CI 0.20 to 0.43); and for TS: PA 83.5%, κ=0.22 (95% CI 0.15 to 0.29). CONCLUSIONS: Conjunctival photography may be a solution for programs near the elimination threshold where there are few available community cases for training field graders.