TRACHOMA SURVEILLANCE AT SCALE: AUTOMATIC DISEASE GRADING OF EYELID PHOTOS

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PROJECT SUMMARY Trachoma is the leading cause of infectious blindness worldwide. The WHO has set a goal of controlling trachoma to a low enough level that blindness from the disease is no longer a public health concern. Control is defined as a district-level prevalence of follicular trachomatous inflammation (TF) in the upper tarsal conjunctiva of less than 5% in children, currently determined by clinical examination. While not required for the current definition, intense trachomatous inflammation (TI) correlates better with presence of the causative agent, Chlamydia trachomatis. Grading of both TF and TI vary widely between individuals, and even in the same individual over time. As cases become rarer, training new graders becomes more difficult. As areas become controlled, trachoma budgets are being cut, and the institutional knowledge of grading lost, making detection of remaining cases and potential resurgence difficult. One of the greatest obstacles to reaching our trachoma goals is an inadequate diagnostic test. The WHO relies on field grading of TF; human inconsistency, grader bias, and training costs are becoming major obstacles, but they do not need to be. We propose to test the central hypothesis that a fully automatic, deep learning grader can perform as well as trained physicians in detecting and grading trachoma. The hypothesis will be tested in the following Specific aims: 1) Automatic identification of follicles and grading of TF and 2) Automatic tarsal blood vessels detection and grading of TI. Our approach includes the development, training and testing of novel image processing pipelines based on semantic segmentation and disease classification using deep learning neural networks and state-of-the-art object detection. All of the data to be used in this study is secondary data from NEI-funded and other trachoma clinical trials conducted by our study team. We aim to facilitate widespread adoption of these novel tools across the trachoma research and grading community, by open source availability of generated code and interoperability of generated machine learning models across programming languages through use of the open neural networks exchange format. Our proposed research addresses the problem of subjectivity, cost and reliability of human trachoma grading. Successful completion of the proposed specific aims will also be a key step forward towards future study and development of providing health organizations and research teams with a novel, efficient and extensible tool to ensure objective, automated, scalable trachoma grading in the field to enhance, or in some cases replace, traditional field grading during the critical endgame of trachoma control, as well surveillance for potential resurgence.