PDF(Pubmed)
Abstract：
UNASSIGNED: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibography image analysis. The goal is to address limitations of existing assessment methods, bridge the curated and real-world dataset gap, and standardize MG image analysis.
UNASSIGNED: The approach involves a two-stage process: automated eyelid detection and tarsal plate segmentation. In the first stage, an AI model trained on curated data identifies relevant eyelid areas in non-curated datasets. The second stage refines the eyelid area in meibography images, enabling precise comparisons between normal and DE subjects. This approach also includes specular reflection removal and tarsal plate mask refinement.
UNASSIGNED: The methodology achieved a promising instance-wise accuracy of 80.8% for distinguishing meibography images from 399 DE and 235 non-DE subjects. By integrating diverse datasets and refining the area of interest, this approach enhances meibography feature extraction accuracy. Dimension reduction through Uniform Manifold Approximation and Projection (UMAP) allows feature visualization, revealing distinct clusters for DE and non-DE phenotypes.
UNASSIGNED: The AI-driven methodology presented here quantifies and classifies meibography image features and standardizes the analysis process. By bootstrapping the model from curated datasets, this methodology addresses real-world dataset challenges to enhance the accuracy of meibography image feature extraction.
UNASSIGNED: The study presents a standardized method for meibography image analysis. This method could serve as a valuable tool in facilitating more targeted investigations into MG characteristics.
UNASSIGNED: The approach involves a two-stage process: automated eyelid detection and tarsal plate segmentation. In the first stage, an AI model trained on curated data identifies relevant eyelid areas in non-curated datasets. The second stage refines the eyelid area in meibography images, enabling precise comparisons between normal and DE subjects. This approach also includes specular reflection removal and tarsal plate mask refinement.
UNASSIGNED: The methodology achieved a promising instance-wise accuracy of 80.8% for distinguishing meibography images from 399 DE and 235 non-DE subjects. By integrating diverse datasets and refining the area of interest, this approach enhances meibography feature extraction accuracy. Dimension reduction through Uniform Manifold Approximation and Projection (UMAP) allows feature visualization, revealing distinct clusters for DE and non-DE phenotypes.
UNASSIGNED: The AI-driven methodology presented here quantifies and classifies meibography image features and standardizes the analysis process. By bootstrapping the model from curated datasets, this methodology addresses real-world dataset challenges to enhance the accuracy of meibography image feature extraction.
UNASSIGNED: The study presents a standardized method for meibography image analysis. This method could serve as a valuable tool in facilitating more targeted investigations into MG characteristics.
摘要：
■这项研究通过人工智能（AI）增强了干眼（DE）研究中的睑板腺（MG）红外图像分析。它包括两个主要阶段：自动眼睑检测和tar板分割，以标准化睑板图图像分析。目标是解决现有评估方法的局限性，弥合策划和现实世界的数据集差距，并规范MG图像分析。
■该方法涉及两个阶段的过程：自动眼睑检测和睑板分割。在第一阶段,在精选数据上训练的AI模型识别非精选数据集中的相关眼睑区域。第二阶段细化睑板图图像中的眼睑区域，能够在正常和DE受试者之间进行精确比较。此方法还包括镜面反射去除和tarsal板掩模细化。
■该方法在区分399DE和235个非DE受试者的眼动脉造影图像方面实现了80.8％的有希望的实例级准确性。通过整合不同的数据集和完善感兴趣的领域，这种方法提高了介体特征提取的准确性。通过均匀流形逼近和投影(UMAP)降维允许特征可视化，揭示DE和非DE表型的不同簇。
■这里介绍的AI驱动方法量化和分类眼图图像特征，并标准化分析过程。通过从精选的数据集中引导模型，这种方法解决了现实世界数据集的挑战，以提高mebography图像特征提取的准确性。
■该研究提出了一种用于显微术图像分析的标准化方法。该方法可以作为促进对MG特征进行更有针对性的研究的有价值的工具。
■该方法涉及两个阶段的过程：自动眼睑检测和睑板分割。在第一阶段,在精选数据上训练的AI模型识别非精选数据集中的相关眼睑区域。第二阶段细化睑板图图像中的眼睑区域，能够在正常和DE受试者之间进行精确比较。此方法还包括镜面反射去除和tarsal板掩模细化。
■该方法在区分399DE和235个非DE受试者的眼动脉造影图像方面实现了80.8％的有希望的实例级准确性。通过整合不同的数据集和完善感兴趣的领域，这种方法提高了介体特征提取的准确性。通过均匀流形逼近和投影(UMAP)降维允许特征可视化，揭示DE和非DE表型的不同簇。
■这里介绍的AI驱动方法量化和分类眼图图像特征，并标准化分析过程。通过从精选的数据集中引导模型，这种方法解决了现实世界数据集的挑战，以提高mebography图像特征提取的准确性。
■该研究提出了一种用于显微术图像分析的标准化方法。该方法可以作为促进对MG特征进行更有针对性的研究的有价值的工具。
