SCH: INT: Collaborative Research: Assistive Integrative Support Tool for Retinopathy of Prematurity

早产儿视网膜病变（ROP）是世界范围内儿童视力丧失的主要原因，婴儿获得性失明的社会负担是巨大的。早期诊断对成功治疗至关重要，可以预防大多数失明病例。然而，缺乏获得专家医疗诊断和护理的机会，特别是在农村地区，仍然是一个日益严重的保健挑战。此外，缺乏ROP的临床专业知识，医疗专业人员正在努力满足对ROP护理日益增长的需求。随着诊断和干预的即时护理技术的迅速发展，在任何有互联网连接和摄像头的地方评估ROP严重程度的潜在能力，即使没有即时的眼科咨询，也可以通过识别最迫切需要转诊和治疗的婴儿，显著改善ROP护理的提供。这将大大减少失明的发病率，而不会相应地增加对人力资源的需求，这需要多年的发展。该项目为ROP开发了一个原型辅助集成支持工具，其特点是模块化设计，包括：(a)图像分析，(b)临床、成像和诊断数据的信息融合，以及(c)生成概率和回归模型以及相关的计算效率高的机器学习算法。该项目的结果包括疾病严重程度指标和通过专家生成标签联合训练的临床证据分类器获得的诊断估计。这些标签包括离散的诊断标签，以及图像对之间相对严重程度的比较结果。视网膜上血管扭曲度和直径分布的随机过程模型，以及通过在整个图像上使用卷积神经网络的基于补丁的无血管图像分析，增强和增强了特征提取。此外，通过新的硬约束和软约束方法结合严重性比较结果，迫使推断的严重性与专家提供的有序信息一致，并解决专家基础真值标签中固有的不确定性。通过回顾性分析（包括交叉验证、纵向测试和跨多个站点的测试）以及前瞻性分析（评估其现实世界的临床影响），在广泛的生成模型上对上述严重性推断方法进行了评估和微调。

Retinopathy of prematurity (ROP) is a leading cause of childhood visual loss worldwide, and the social burdens of infancy-acquired blindness are enormous. Early diagnosis is critically important for successful treatment, and can prevent most cases of blindness. However, lack of access to expert medical diagnosis and care, especially in rural areas, remains a growing healthcare challenge. In addition, clinical expertise in ROP is lacking, and medical professionals are struggling to meet the increasing need for ROP care. As point-of-care technologies for diagnosis and intervention are rapidly expanding, the potential ability to assess ROP severity from any location with an internet connection and a camera, even without immediate ophthalmologic consultation available, could significantly improve delivery of ROP care by identifying infants who are in most urgent need for referral and treatment. This would dramatically reduce the incidence of blindness without a proportionate increase in the need for human resources, which take many years to develop. This project develops a prototype assistive integrative support tool for ROP, featuring a modular design comprising: (a) image analysis, (b) information fusion of clinical, imaging, and diagnostic data, and (c) generative probabilistic and regression models with associated computationally efficient machine learning algorithms. The outcomes of the project include disease severity metrics and diagnostic estimates obtained through clinical evidence classifiers trained jointly over expert-generated labels. These labels consist of discrete diagnostic labels, as well as comparison outcomes of relative severity between pairs of images. Random process models for vessel tortuosity and diameter distributions over the retina, as well as patch-based vessel-free image analysis through the use of convolutional neural networks on the entire image, enhance and augment feature extraction. Moreover, incorporating severity comparison outcomes through novel hard and soft constraint methods force inferred severity to agree with ordinal information provided by experts and address inherent uncertainty in expert ground-truth labels. The above severity inference methods are evaluated and fine-tuned over a broad array of generative models, both through retrospective analysis, including cross-validation, longitudinal tests, and tests across multiple sites, as well as through prospective analysis, evaluating its real-world clinical impact.