■在关键的临床试验中,食品和药物管理局诺沃“批准”了第一个完全自主的人工智能(AI)糖尿病视网膜疾病诊断系统,使用了反身扩张方案.在实施反身扩张之前使用真实世界的部署数据,我们确定了与非诊断结果相关的因素.这些因素允许一种新颖的预测性扩张工作流程,患者最有可能从药物扩张中受益的先验扩张,以最大限度地提高效率和患者满意度。
■对在约翰霍普金斯医学(2020年8月至2021年5月)接受自主AI评估的患者进行回顾性审查。我们构建了一个非诊断结果的多变量逻辑回归模型,以比较有和没有诊断结果的患者的特征。使用调整后的比值比(AOR)。P<0.05被认为具有统计学意义。
■241名患者(59%为女性,中位年龄=59岁),123(51%)有非诊断结果。在多变量分析中,1型糖尿病(T1D,OR=5.82,95%置信区间[CI]:1.45-23.40,P=.01),吸烟(aOR=2.86,95%CI:1.36-5.99,P=0.005),和年龄(每10年增加,aOR=2.12,95%CI:1.62-2.77,P<.001)与非诊断结果相关。功能消除后,使用T1D创建了一个预测模型,吸烟,年龄,种族,性别,和高血压作为输入。在五倍交叉验证中,该模型显示接受者-操作者特征曲线下的面积为0.76。
■我们使用与非诊断结果相关的因素来设计一种新颖的,预测性扩张工作流程,最有可能从药物扩张中受益的患者是先验扩张的。这种新的工作流程有可能比反身扩张更有效,从而最大限度地增加接受糖尿病视网膜检查的高危患者的数量。
UNASSIGNED: In the pivotal clinical trial that led to Food and Drug Administration De Novo \"approval\" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction.
UNASSIGNED: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant.
UNASSIGNED: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation.
UNASSIGNED: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.