IMPORTANCE: Diabetic retinopathy is a leading cause of blindness, but its detrimental effects are preventable with early detection and treatment. Screening for diabetic retinopathy has the potential to increase the number of cases treated early, especially in populations with limited access to care. OBJECTIVE: To determine the efficacy of an automated algorithm in interpreting screening ophthalmoscopic photographs from patients with diabetes compared with a reading center interpretation. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort analysis of 15,015 patients with type 1 or 2 diabetes in the Harris Health System in Harris County, Texas, who had undergone a retinal screening examination and nonmydriatic fundus photography via the Intelligent Retinal Imaging System (IRIS) from June 2013 to April 2014 were included. The IRIS-based interpretations were compared with manual interpretation. The IRIS algorithm population statistics were calculated. MAIN OUTCOMES AND MEASURES: Sensitivity and false-negative rate of the IRIS computer-based algorithm compared with reading center interpretation of the same images. RESULTS: A total of 15 015 consecutive patients (aged 18-98 years); mean 54.3 years with known type 1 or 2 diabetes underwent nonmydriatic fundus photography for a diabetic retinopathy screening examination. The sensitivity of the IRIS algorithm in detecting sight-threatening diabetic eye disease compared with the reading center interpretation was 66.4% (95% CI, 62.8%-69.9%) with a false-negative rate of 2%. The specificity was 72.8% (95% CI, 72.0%-73.5%). In a population where 15.8% of people with diabetes have sight-threatening diabetic eye disease, the IRIS algorithm positive predictive value was 10.8% (95% CI, 9.6%-11.9%) and the negative predictive value was 97.8% (95% CI, 96.8%-98.6%). CONCLUSIONS AND RELEVANCE: In this large urban setting, the IRIS computer algorithm-based screening program had a high sensitivity and a low false-negative rate, suggesting that it may be an effective alternative to conventional reading center image interpretation. The IRIS algorithm shows promise as a screening program, but algorithm refinement is needed to achieve better performance. Further studies of patient safety, cost-effectiveness, and widespread applications of this type of algorithm should be pursued to better understand the role of teleretinal imaging and automated analysis in the global health care system.
IMPORTANCE: Diabetic retinopathy is a leading cause of blindness, but its detrimental effects are preventable with early detection and treatment. Screening for diabetic retinopathy has the potential to increase the number of cases treated early, especially in populations with limited access to care. OBJECTIVE: To determine the efficacy of an automated algorithm in interpreting screening ophthalmoscopic photographs from patients with diabetes compared with a reading center interpretation. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort analysis of 15,015 patients with type 1 or 2 diabetes in the Harris Health System in Harris County, Texas, who had undergone a retinal screening examination and nonmydriatic fundus photography via the Intelligent Retinal Imaging System (IRIS) from June 2013 to April 2014 were included. The IRIS-based interpretations were compared with manual interpretation. The IRIS algorithm population statistics were calculated. MAIN OUTCOMES AND MEASURES: Sensitivity and false-negative rate of the IRIS computer-based algorithm compared with reading center interpretation of the same images. RESULTS: A total of 15 015 consecutive patients (aged 18-98 years); mean 54.3 years with known type 1 or 2 diabetes underwent nonmydriatic fundus photography for a diabetic retinopathy screening examination. The sensitivity of the IRIS algorithm in detecting sight-threatening diabetic eye disease compared with the reading center interpretation was 66.4% (95% CI, 62.8%-69.9%) with a false-negative rate of 2%. The specificity was 72.8% (95% CI, 72.0%-73.5%). In a population where 15.8% of people with diabetes have sight-threatening diabetic eye disease, the IRIS algorithm positive predictive value was 10.8% (95% CI, 9.6%-11.9%) and the negative predictive value was 97.8% (95% CI, 96.8%-98.6%). CONCLUSIONS AND RELEVANCE: In this large urban setting, the IRIS computer algorithm-based screening program had a high sensitivity and a low false-negative rate, suggesting that it may be an effective alternative to conventional reading center image interpretation. The IRIS algorithm shows promise as a screening program, but algorithm refinement is needed to achieve better performance. Further studies of patient safety, cost-effectiveness, and widespread applications of this type of algorithm should be pursued to better understand the role of teleretinal imaging and automated analysis in the global health care system.
Authors: Igor Kozak; John F Payne; Patrik Schatz; Eman Al-Kahtani; Moritz Winkler Journal: Graefes Arch Clin Exp Ophthalmol Date: 2017-04-26 Impact factor: 3.117
Authors: Meagan R Talbott; Sarah Dufek; Lonnie Zwaigenbaum; Susan Bryson; Jessica Brian; Isabel M Smith; Sally J Rogers Journal: J Autism Dev Disord Date: 2020-09
Authors: Rolake O Alabi; Amy Ansin; Jameson Clover; John Wilkins; Naveen K Rao; Mark A Terry; Khoa D Tran; Christopher S Sales Journal: Cornea Date: 2019-04 Impact factor: 2.651