背景:人工智能(AI)是一种有前途的新技术,具有诊断过敏性结膜疾病(ACD)的潜力。然而,由于缺乏量身定制的图像数据库和可解释的AI模型,其发展速度放缓。因此,这项研究的目的是开发一种可解释的AI模型,该模型不仅可以诊断ACDs,还可以提供诊断依据.
方法:将来自日本10个眼科机构的4942张裂隙灯图像数据集用作图像数据库。构建了一个连续的分割AI管道,以识别1038张季节性和常年性过敏性结膜炎(AC)图像中的12个临床发现。特应性角膜结膜炎(AKC),春季角膜结膜炎(VKC),巨大乳头状结膜炎(GPC),正常的科目。通过提取发现来确定管道获得可解释结果的能力来评估管道的性能。对4种基于严重程度的AC诊断分类确定了其诊断准确性,AKC/VKC,GPC,和正常。
结果:SegmentationAIpipeline有效地提取了关键的ACD指标,包括结膜充血,巨大的乳头,和盾牌溃疡,并提供了可解释的见解。AI管道诊断具有86.2%的高诊断准确率,董事会认证的眼科医生的比例为60.0%。该管道具有较高的分类性能,AC的曲线下面积(AUC)为0.959,正常受试者为0.905,GPC为0.847,0.829对于VKC,AKC为0.790。
结论:由全面的图像数据库创建的可解释的AI模型可用于高度准确地诊断ACD。
BACKGROUND: Artificial intelligence (AI) is a promising new technology that has the potential of diagnosing allergic conjunctival diseases (ACDs). However, its development is slowed by the absence of a tailored image database and explainable AI models. Thus, the purpose of this study was to develop an explainable AI model that can not only diagnose ACDs but also present the basis for the diagnosis.
METHODS: A dataset of 4942 slit-lamp images from 10 ophthalmological institutions across Japan were used as the image database. A sequential pipeline of segmentation AI was constructed to identify 12 clinical findings in 1038 images of seasonal and perennial allergic conjunctivitis (AC), atopic keratoconjunctivitis (AKC), vernal keratoconjunctivitis (VKC), giant papillary conjunctivitis (GPC), and normal subjects. The performance of the pipeline was evaluated by determining its ability to obtain explainable results through the extraction of the findings. Its diagnostic accuracy was determined for 4 severity-based diagnosis classification of AC, AKC/VKC, GPC, and normal.
RESULTS: Segmentation AI pipeline efficiently extracted crucial ACD indicators including conjunctival hyperemia, giant papillae, and shield ulcer, and offered interpretable insights. The AI pipeline diagnosis had a high diagnostic accuracy of 86.2%, and that of the board-certified ophthalmologists was 60.0%. The pipeline had a high classification performance, and the area under the curve (AUC) was 0.959 for AC, 0.905 for normal subjects, 0.847 for GPC, 0.829 for VKC, and 0.790 for AKC.
CONCLUSIONS: An explainable AI model created by a comprehensive image database can be used for diagnosing ACDs with high degree of accuracy.