Subramaniam Ananya, Orge Faruk, Douglass Michael, Can Basak, Monteoliva Guillermo, Fried Evelin, Schbib Vanina, Saidman Gabriela, Peña Brenda, Ulacia Soledad, Acevedo Pedro, Rollins Andrew M, Wilson David L
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Case Medical Center University Hospitals, Department of Ophthalmology, Cleveland, Ohio, United States.
J Med Imaging (Bellingham). 2023 Nov;10(6):061107. doi: 10.1117/1.JMI.10.6.061107. Epub 2023 Oct 3.
Retinopathy of prematurity (ROP) is a retinal vascular disease affecting premature infants that can culminate in blindness within days if not monitored and treated. A disease stage for scrutiny and administration of treatment within ROP is "plus disease" characterized by increased tortuosity and dilation of posterior retinal blood vessels. The monitoring of ROP occurs via routine imaging, typically using expensive instruments ($50 to $140 K) that are unavailable in low-resource settings at the point of care.
As part of the smartphone-ROP program to enable referrals to expert physicians, fundus images are acquired using smartphone cameras and inexpensive lenses. We developed methods for artificial intelligence determination of plus disease, consisting of a preprocessing pipeline to enhance vessels and harmonize images followed by deep learning classification. A deep learning binary classifier (plus disease versus no plus disease) was developed using GoogLeNet.
Vessel contrast was enhanced by 90% after preprocessing as assessed by the contrast improvement index. In an image quality evaluation, preprocessed and original images were evaluated by pediatric ophthalmologists from the US and South America with years of experience diagnosing ROP and plus disease. All participating ophthalmologists agreed or strongly agreed that vessel visibility was improved with preprocessing. Using images from various smartphones, harmonized via preprocessing (e.g., vessel enhancement and size normalization) and augmented in physically reasonable ways (e.g., image rotation), we achieved an area under the ROC curve of 0.9754 for plus disease on a limited dataset.
Promising results indicate the potential for developing algorithms and software to facilitate the usage of cell phone images for staging of plus disease.
早产儿视网膜病变（ROP）是一种影响早产儿的视网膜血管疾病，如果不进行监测和治疗，可能在数天内导致失明。ROP中需要仔细检查并进行治疗的一个疾病阶段是“增值性病变”，其特征是视网膜后部血管迂曲度增加和扩张。ROP的监测通过常规成像进行，通常使用昂贵的仪器（5万美元至14万美元），而在资源匮乏地区的医疗点无法获得这些仪器。
作为智能手机ROP项目的一部分，该项目旨在实现向专家医生的转诊，使用智能手机摄像头和廉价镜头获取眼底图像。我们开发了用于人工智能判定增值性病变的方法，包括一个预处理管道，以增强血管并使图像协调一致，随后进行深度学习分类。使用GoogLeNet开发了一种深度学习二元分类器（增值性病变与非增值性病变）。
通过对比度改善指数评估，预处理后血管对比度提高了90%。在图像质量评估中，来自美国和南美洲的有多年ROP和增值性病变诊断经验的儿科眼科医生对预处理后的图像和原始图像进行了评估。所有参与的眼科医生都同意或强烈同意预处理后血管的可见性得到了改善。使用来自各种智能手机的图像，通过预处理（如血管增强和尺寸归一化）进行协调，并以物理上合理的方式（如图像旋转）进行增强，我们在有限的数据集中实现了增值性病变的ROC曲线下面积为0.9754。
有前景的结果表明，开发算法和软件以促进使用手机图像进行增值性病变分期具有潜力。
