Automated detection of diabetic retinopathy: barriers to translation into clinical practice.
Automated identification of diabetic retinopathy (DR), the primary cause of blindness and visual loss for those aged 18-65 years, from color images of the retina has enormous potential to increase the quality, cost-effectiveness and accessibility of preventative care for people with diabetes. Through advanced image analysis techniques, retinal images are analyzed for abnormalities that define and correlate with the severity of DR. Translating automated DR detection into clinical practice will require surmounting scientific and nonscientific barriers. Scientific concerns, such as DR detection limits compared with human experts, can be studied and measured. Ethical, legal and political issues can be addressed, but are difficult or impossible to measure. The primary objective of this review is to survey the methods, potential benefits and limitations of automated detection in order to better manage translation into clinical practice, based on extensive experience with the systems we have developed.
Abramoff
Michael D
MD
Department of Ophthalmology and Visual Sciences, University of Iowa, 11290C PFP UIHC, 200 Hawkins Drive, Iowa City, IA 52242, USA. michael-abramoff@uiowa.edu
Niemeijer
Meindert
M
Russell
Stephen R
SR
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D055815:Young Adult