发表机构
School of Industrial Engineering and Management, Oklahoma State University; Department of Statistics, Oklahoma State University（俄克拉荷马州立大学工业工程与管理学院; 俄克拉荷马州立大学统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构DR预测模型集成难题，设计并实现React-Flask Web系统DR-LabStack，通过共享表单与后端适配器统一模型接口，功能评估验证了多模型加载与交互流程。
AI 中文摘要
预训练的糖尿病视网膜病变（DR）预测模型在输入字段、序列化格式、预处理要求和输出语义方面各不相同。因此，通过通用临床界面使这些模型可访问，需要用户界面与推理服务之间进行显式协调。我们设计并实现了DR-LabStack，这是一个React-Flask Web系统，集成了四个外部开发的预训练模型：RuleFit、Pruned RuleFit、Elaborative XGBoost和Two-level Ensemble。共享表单检索有序模型特征，渲染特定于模型的数值和分类控件，并构建位置输入向量。后端适配器加载异构工件并应用集成附带的缩放器，而通用JSON响应支持二元分类显示以及方法和来源信息。2026年9月8日的功能评估使用了复制的应用文件和真实模型工件，在记录在案的隔离环境中进行。所有四个模型均成功加载并暴露了其14、6、8和25字段的契约。六十二个Flask测试客户端请求表征了服务行为；12个有限向量检查确认了调用路径和阈值一致性。二十四个带有模拟传输的浏览器组件场景验证了输入排序和结果渲染，并表征了输入验证行为。该系统展示了针对异构DR模型的可复用交互和服务工作流程。贡献在于Web系统设计、集成和软件功能；临床有效性和临床医生可用性需要单独评估。
英文摘要
Pretrained diabetic retinopathy (DR) prediction models differ in their input fields, serialization formats, preprocessing requirements, and output semantics. Making these models accessible through a common clinical interface therefore requires explicit coordination between the user interface and the inference service. We designed and implemented DR-LabStack, a React-Flask web system integrating four externally developed pretrained models: RuleFit, Pruned RuleFit, Elaborative XGBoost, and Two-level Ensemble. A shared form retrieves ordered model features, renders model-specific numerical and categorical controls, and constructs a positional input vector. Backend adapters load heterogeneous artifacts and apply the ensemble's accompanying scaler, while a common JSON response supports binary classification display alongside method and source information. Functional evaluation on September 8, 2026 used copied application files and real model artifacts in a documented isolated environment. All four models loaded and exposed their 14-, 6-, 8-, and 25-field contracts. Sixty-two Flask test-client requests characterized service behavior; 12 limited-vector checks confirmed invocation-path and threshold consistency. Twenty-four browser-component scenarios with mocked transport verified input ordering and result rendering and characterized input-validation behavior. The resulting system demonstrates a reusable interaction and serving workflow for heterogeneous DR models. The contribution is web-system design, integration, and software functionality; clinical effectiveness and clinician usability require separate evaluation.
