Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA.
Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA.
Int J Mol Sci. 2023 Oct 12;24(20):15105. doi: 10.3390/ijms242015105.
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
儿科患者的视网膜出血可能对眼科医生构成诊断挑战。这些出血可能由多种潜在病因引起，包括虐待性头部外伤、意外创伤和医疗状况。准确识别病因对于适当的管理和法律考虑至关重要。近年来，深度学习技术在帮助医疗保健专业人员更准确、更及时地诊断各种疾病方面显示出了潜力。我们探讨了深度学习方法在区分儿科视网膜出血病因方面的潜力。我们的研究跨越了多个中心，分析了 898 张图像，最终得到了一个包含 597 张眼底视网膜出血照片的数据集，这些照片分为医学（49.9%）和创伤（50.1%）病因。我们应用了深度学习模型，特别是基于 ResNet 和 transformer 架构的模型；FastViT-SA12，一种混合 transformer 模型，取得了最高的准确率（90.55%）和 90.55%的接收器操作特征曲线下面积（AUC），而 ResNet18 在独立测试数据集上获得了最高的敏感性值（96.77%）。该研究强调了在人工智能（AI）模型中针对儿科视网膜出血进行优化的领域。虽然 AI 在诊断这些出血方面很有价值，但医疗专业人员的专业知识仍然是不可替代的。AI 专家和儿科眼科医生之间的合作对于充分发挥 AI 在诊断儿科视网膜出血病因方面的潜力至关重要。
