PDF(Pubmed)
Abstract：
UNASSIGNED: Thyroid-associated ophthalmopathy (TAO) is the most prevalent autoimmune orbital condition, significantly impacting patients\' appearance and quality of life. Early and accurate identification of active TAO along with timely treatment can enhance prognosis and reduce the occurrence of severe cases. Although the Clinical Activity Score (CAS) serves as an effective assessment system for TAO, it is susceptible to assessor experience bias. This study aimed to develop an ensemble deep learning system that combines anterior segment slit-lamp photographs of patients with facial images to simulate expert assessment of TAO.
UNASSIGNED: The study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.
UNASSIGNED: The proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.
UNASSIGNED: The ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.
UNASSIGNED: The study included 156 patients with TAO who underwent detailed diagnosis and treatment at Shanxi Eye Hospital Affiliated to Shanxi Medical University from May 2020 to September 2023. Anterior segment slit-lamp photographs and facial images were used as different modalities and analyzed from multiple perspectives. Two ophthalmologists with more than 10 years of clinical experience independently determined the reference CAS for each image. An ensemble deep learning model based on the residual network was constructed under supervised learning to predict five key inflammatory signs (redness of the eyelids and conjunctiva, and swelling of the eyelids, conjunctiva, and caruncle or plica) associated with TAO, and to integrate these objective signs with two subjective symptoms (spontaneous retrobulbar pain and pain on attempted upward or downward gaze) in order to assess TAO activity.
UNASSIGNED: The proposed model achieved 0.906 accuracy, 0.833 specificity, 0.906 precision, 0.906 recall, and 0.906 F1-score in active TAO diagnosis, demonstrating advanced performance in predicting CAS and TAO activity signs compared to conventional single-view unimodal approaches. The integration of multiple views and modalities, encompassing both anterior segment slit-lamp photographs and facial images, significantly improved the prediction accuracy of the model for TAO activity and CAS.
UNASSIGNED: The ensemble multi-view multimodal deep learning system developed in this study can more accurately assess the clinical activity of TAO than traditional methods that solely rely on facial images. This innovative approach is intended to enhance the efficiency of TAO activity assessment, providing a novel means for its comprehensive, early, and precise evaluation.
摘要：
■甲状腺相关眼病（TAO）是最常见的自身免疫性眼眶疾病，显着影响患者的外观和生活质量。早期准确识别活动性TAO，及时治疗可提高预后，减少重症病例的发生。尽管临床活动评分（CAS）是TAO的有效评估系统，它容易受到评估者经验偏见的影响。这项研究旨在开发一种集成的深度学习系统，该系统将患者的眼前段裂隙灯照片与面部图像相结合，以模拟TAO的专家评估。
■该研究包括2020年5月至2023年9月在山西医科大学附属山西眼科医院接受详细诊断和治疗的156例TAO患者。使用前段裂隙灯照片和面部图像作为不同的模式，并从多个角度进行分析。两名具有10年以上临床经验的眼科医生独立确定了每张图像的参考CAS。在监督学习下构建了基于残差网络的集成深度学习模型，以预测五个关键的炎症体征（眼睑和结膜发红，眼睑肿胀，结膜,以及与TAO相关的Carbut或plica），并将这些客观体征与两个主观症状（自发性球后疼痛和尝试向上或向下凝视时的疼痛）结合起来，以评估TAO活动。
■所提出的模型达到了0.906的精度，0.833特异性，0.906精度，0.906召回，在主动TAO诊断中，F1评分为0.906，与传统的单视图单峰方法相比，在预测CAS和TAO活动迹象方面表现出先进的性能。多种观点和方式的整合,包括眼前段裂隙灯照片和面部图像，显著提高了模型对TAO活性和CAS的预测精度。
■在本研究中开发的集成多视图多模态深度学习系统比单纯依靠面部图像的传统方法可以更准确地评估TAO的临床活动。这种创新方法旨在提高TAO活动评估的效率，为其全面提供了一种新颖的手段，早期，和精确的评估。
■该研究包括2020年5月至2023年9月在山西医科大学附属山西眼科医院接受详细诊断和治疗的156例TAO患者。使用前段裂隙灯照片和面部图像作为不同的模式，并从多个角度进行分析。两名具有10年以上临床经验的眼科医生独立确定了每张图像的参考CAS。在监督学习下构建了基于残差网络的集成深度学习模型，以预测五个关键的炎症体征（眼睑和结膜发红，眼睑肿胀，结膜,以及与TAO相关的Carbut或plica），并将这些客观体征与两个主观症状（自发性球后疼痛和尝试向上或向下凝视时的疼痛）结合起来，以评估TAO活动。
■所提出的模型达到了0.906的精度，0.833特异性，0.906精度，0.906召回，在主动TAO诊断中，F1评分为0.906，与传统的单视图单峰方法相比，在预测CAS和TAO活动迹象方面表现出先进的性能。多种观点和方式的整合,包括眼前段裂隙灯照片和面部图像，显著提高了模型对TAO活性和CAS的预测精度。
■在本研究中开发的集成多视图多模态深度学习系统比单纯依靠面部图像的传统方法可以更准确地评估TAO的临床活动。这种创新方法旨在提高TAO活动评估的效率，为其全面提供了一种新颖的手段，早期，和精确的评估。
