首页|Vita-Salute San Raffaele University Reports Findings in Artificial Intelligence (Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging)
Vita-Salute San Raffaele University Reports Findings in Artificial Intelligence (Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Milan,Italy,by NewsRx journalists,research stated,"To compare the diagnostic perfor mance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging sy stem across pseudocolor,simulated white light (SWL),and light-emitting diode ( LED) camera imaging modalities. A cross-sectional investigation involved patient s with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal,ultra-widefield pseudocolor camera,with and without SWL." The news reporters obtained a quote from the research from Vita-Salute San Raffa ele University,"Macula-centered and optic nerve-centered 45 x 45-degree photogr aphs were processed using EyeArt v2.1. Human graders established the ground trut h (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wk) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR s tages (sensitivity = 0.83,specificity = 0.95 for proliferative DR) and had the highest agreement with the GT (wk = 0.71). The addition of SWL to pseudocolor im aging resulted in decreased performance (sensitivity = 0.33,specificity = 0.98 for proliferative DR; wk = 0.55). Peripheral lesions reduced the likelihood of b eing staged in the same or higher DR category by 80% (P <0.001). Pseudocolor and LED cameras,although proficient,demonstrated noninte rchangeable performance,with the LED camera exhibiting superior accuracy in ide ntifying advanced DR stages. These findings underscore the importance of impleme nting AI systems trained for ultra-widefield imaging,considering the impact of peripheral lesions on correct DR staging."
MilanItalyEuropeArtificial Intelli genceDiabetic AngiopathiesDiabetic RetinopathyEmerging TechnologiesEye D iseases and ConditionsHealth and MedicineMachine LearningNutritional and M etabolic Diseases and ConditionsOphthalmologyRetinal Diseases and ConditionsRetinopathyVascular Diseases and Conditions
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