Impact of retinal image quality: software aid for a low-cost device and effects on disease detection
| Autor: | John D. Maynard, Sheila C Nemeth, Simon Barriga, Jeremy Benson, Peter Soliz, Gilberto Zamora, Jeffrey Wigdahl |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial Intelligence System
Disease detection business.industry Computer science Image quality media_common.quotation_subject ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Retinal image Quality software Software Quality (business) Computer vision Artificial intelligence business media_common |
| Zdroj: | Ophthalmic Technologies XXIX. |
| DOI: | 10.1117/12.2510125 |
| Popis: | Adequate image quality is a necessary component to any retinal screening program whether the cases are to be read by a human reader or processed by an artificial intelligence system (AI). The need for expanded screening for retinal diseases has led to the adoption of low-cost, portable cameras that are ideal for reaching large underserved populations. However, the low-cost cameras generally require a higher level of operator skill to produce high quality images in comparison with more expensive table-top retinal cameras. This study, conducted at thirteen clinics in Monterrey, Mexico, compares unreadable rates between a table-top retinal camera (Canon CR2-AF) and a low-cost portable camera (Volk Pictor Plus) before and after implementation of automatic image quality assessment software, Image Quality Analyzer (IQA). The software determines if an image is of adequate quality to be read by a human or AI system; what the image quality issues are; and tips for fixing the issues. The process is performed in real time. Results show a significant decrease in unreadable cases (9% to 0%) for the Pictor Plus after IQA implementation bringing the percent of rejected cases in line with the table-top camera (3% to 5%). |
| Databáze: | OpenAIRE |
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