Visual Field (VF) test is a widely used, noninvasive technique for evaluating pathology or dysfunction in the visual pathways. The VF test, in conjunction with other diagnostics, is used for detection of early stages of glaucoma and for following its progression. Early detection is critical as blindness from glaucoma is preventable in nearly all cases, provided treatment is administered early in the progression. However, the inherent subjectivity of the VF test makes it often difficult to interpret even for a skilled practitioner. There is a need for automated decision aid tool that will facilitate and standardize the interpretation task. In Phase 1 of this project, IAC will design and implement novel software algorithms to automate the interpretation of VF test data for detection of glaucoma. The software will classify VF test data into normal, borderline glaucomatous, glaucomatous and unknown (not normal or glaucomatous). The aim is to provide classification performance close to that of a highly skilled human expert. The emphasis will be on the detection of early stages of glaucoma. In addition to the classification output, the software will produce a set of comprehensive rules that will explain the decision path leading to the suggested diagnosis.
Thesaurus Terms: computer assisted diagnosis, computer system design /evaluation, diagnosis design /evaluation, glaucoma test, visual field artificial intelligence, early diagnosis, glaucoma, noninvasive diagnosis, vision test computer program /software, human data
