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

早产儿视网膜病变(ROP)是全球儿童视力丧失的主要原因，婴儿获得性失明造成的社会负担是巨大的。早期诊断是成功治疗的关键，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，仍然是一个日益严峻的医疗挑战。此外，ROP方面的临床专业知识匮乏，医疗专业人员正在努力满足日益增长的ROP护理需求。随着诊断和干预技术的迅速发展，即使在没有眼科咨询的情况下，通过互联网连接和摄像头从任何地点评估ROP严重程度的潜在能力，也可以通过识别最迫切需要转诊和治疗的婴儿来显著改善ROP护理的提供。这将极大地减少失明的发生率，而不会相应地增加对人力资源的需求，而人力资源的开发需要多年。该项目开发了一个用于ROP的原型辅助综合支持工具，采用模块化设计，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)生成性概率和回归模型以及相关的计算效率高的机器学习算法。该项目的成果包括通过在专家生成的标签上联合培训的临床证据分类器获得的疾病严重程度度量和诊断估计。这些标签由离散的诊断标签以及成对图像之间相对严重性的比较结果组成。视网膜上血管弯曲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进行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纳入严重性比较结果，迫使推断的严重性与专家提供的顺序信息一致，并解决专家基本事实标签中的固有不确定性。上述严重性推断方法通过回顾分析(包括交叉验证、纵向测试和跨多个站点的测试)以及前瞻性分析在广泛的生成模型上进行评估和微调，以评估其真实的临床影响。

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.