Diabetic retinopathy, damage to the retina caused by diabetes, can lead to vision loss if it is not detected early. This study tested an AI system for diabetic retinopathy screening in real-world conditions, in two endocrinology clinics in Melbourne and three Aboriginal medical services in Western Australia, and also gathered the views of patients and health professionals.
Of the 236 people screened, 203 were included in the main analysis. Overall, the system identified diabetic retinopathy requiring referral with a sensitivity of 96.9% and a specificity of 87.7%, and over 93% of participants were satisfied with the service. However, specificity was substantially lower among participants recruited from Aboriginal medical services, meaning that more people without the condition were wrongly flagged for referral. The study used clinic location as an approximation of Indigenous status.
The authors note that the AI system had been trained exclusively on images from Chinese adults, and that differences in retinal pigmentation may affect its accuracy. They also point to other possible factors, such as poorer image quality, older age and other eye conditions. These explanations were not tested in the study.
Learn more about this study here: https://doi.org/10.1038/s41598-021-94178-5
Reference:
Scheetz, J., Koca, D., McGuinness, M., Holloway, E., Tan, Z., Zhu, Z., O’Day, R., Sandhu, S., MacIsaac, R.J., Gilfillan, C., Turner, A., Keel, S., & He, M. (2021). Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Scientific Reports, 11, 15808.