Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study.

BACKGROUND: To compare the performance of Medios (offline) and EyeArt (online) artificial intelligence (AI) algorithms for detecting diabetic retinopathy (DR) on images captured using fundus-on-smartphone photography in a remote outreach field setting. METHODS: In June, 2019 in the Yucatan Peninsula, 248 patients, many of whom had chronic visual impairment, were screened for DR using two portable Remidio fundus-on-phone cameras, and 2130 images obtained were analyzed, retrospectively, by Medios and EyeArt. Screening performance metrics also were determined retrospectively using masked image analysis combined with clinical examination results as the reference standard. RESULTS: A total of 129 patients were determined to have some level of DR; 119 patients had no DR. Medios was capable of evaluating every patient with a sensitivity (95% confidence intervals [CIs]) of 94% (88%-97%) and specificity of 94% (88%-98%). Owing primarily to photographer error, EyeArt evaluated 156 patients with a sensitivity of 94% (86%-98%) and specificity of 86% (77%-93%). In a head-to-head comparison of 110 patients, the sensitivities of Medios and EyeArt were 99% (93%-100%) and 95% (87%-99%). The specificities for both were 88% (73%-97%). CONCLUSIONS: Medios and EyeArt AI algorithms demonstrated high levels of sensitivity and specificity for detecting DR when applied in this real-world field setting. Both programs should be considered in remote, large-scale DR screening campaigns where immediate results are desirable, and in the case of EyeArt, online access is possible.

The Critical Appraisal Tool for Anatomical Meta-analysis: A framework for critically appraising anatomical meta-analyses.

The hallmark of evidence-based medicine is the meta-analysis (MA). For much of its rich history, the field of anatomy has been dominated by descriptive, cadaveric studies. In the last two decades, quantitative measurements and statistical analyses have frequently accompanied such studies. These studies have directly led to the publication of anatomical MAs, which have ushered in the exciting field of evidence-based anatomy. Although critical appraisal tools exist for clinical MAs, none of them are specifically tailored for anatomical MAs. Therefore, the purpose of this article is to provide a framework by which clinical anatomists and others can critically appraise anatomical MAs using the Critical Appraisal Tool for Anatomical Meta-analysis (CATAM). Using a running example from a recently published MA, we show how to use the CATAM rubric in a step-by-step fashion. Each scored section of the CATAM rubric is summated into a total score (maximum 50 points). This score is then referenced to a conversion chart, which assigns a qualitative value to the MA in a range from "very good" to "poor." Future studies can investigate the interrater reliability of the instrument, and possibly subject the CATAM rubric to a Delphi panel. As anatomical MAs become more commonplace at surgical grand rounds and journal clubs in academic medical centers throughout the world, we hope that the CATAM rubric can help facilitate meaningful discussions about the quality and clinical relevance of anatomical MAs.