A Second Look at DREAM: Towards a New Paradigm in Meibomian Gland Evaluation Using Artificial Intelligence

重新审视 DREAM：利用人工智能迈向睑板腺评估的新范式

• 批准号: 10432877
• 负责人: Meng Ching Lin
• 金额: $ 21.96万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432877
• 关键词: Affect; Algorithms; Area; Artificial Intelligence; Atrophic; Characteristics; Classification; Clinical; Clinical Data; Clinical Management; Clinical Trials; Consumption; Correlation Studies; Data; Data Set; Databases; Diagnosis; Evaluation; Eyelid structure; Fatty Acids; Film; Gland; Goals; Hand; Health; Human; Image; Image Analysis; Individual; Laboratories; Lead; Learning; Length; Link; Lipids; Machine Learning; Measurement; Measures; Medical History; Medical Imaging; Methodology; Methods; Morphology; Osmolar Concentration; Outcome; Pathologic; Pathology; Patients; Personal Satisfaction; Phenotype; Process; Production; Property; Publishing; Quality of life; Questionnaires; Randomized Controlled Clinical Trials; Risk; Scanning; Scheme; Second Look Surgery; Series; Serum; Signs and Symptoms; Societies; Standardization; Symptoms; Technology; Thinness; Time; Training; Update; Visual; Width; Work; algorithm training; aqueous; clinical database; cost; data management; demographics; evaporation; eye dryness; improved; machine learning algorithm; meibomian gland; meibomian gland dysfunction; novel; ocular surface; research clinical testing; response; supervised learning; symptomatology; unsupervised learning

Project Summary Dry eye (DE) is a highly prevalent condition with significant impacts on individuals and society that continues to evade easy diagnosis and treatment. The most common cause of DE is thought to be Meibomian gland dysfunction (MGD). The Meibomian glands in the upper and lower eyelids secrete lipids that form a thin film covering the aqueous tears and inhibit their evaporation. In MGD, it is thought that inadequate and/or poor quality tear lipids are secreted, leading to tear film instability, evaporation, and symptoms of DE. The glandular changes that occur in MGD are not well understood, nor are we able to identify which aspects of MGD pose the greatest risk for tear film instability and DE. The Dry Eye Assessment and Management (DREAM) Study was a clinical trial of ω3 fatty acid supplements for the treatment of DE. Over the course of the trial a large database of meibography images – infrared images of the everted eyelids that reveal the Meibomian glands – was compiled and analyzed using a novel scheme to characterize 13 different aspects of the glands by visual inspection and analyze their relationships to the clinically assessed quality of the secreted lipids. The process was arduous and time consuming, inherently subject to human bias, and provided little new information on the links between Meibomian gland characteristics and DE signs and symptoms. Recent advances in artificial intelligence (AI) have allowed us to train supervised machine learning algorithms on meibography images to automatically detect and quantify detailed morphological features of the individual glands. These detailed morphological features potentially contain a wealth of information about the health and functioning of the Meibomian glands, and could provide valuable information on the mechanisms behind MGD and its clinical implications. A further emerging AI technology for use in medical imaging – unsupervised discriminative feature learning – mitigates the human bias, and could potentially discover previously unidentified properties in meibography images, and possible links to crucial clinical endpoints like tear film instability and DE symptoms. In this project, we propose to utilize this new AI technology to re-analyze the DREAM Study clinical database of meibography images to dramatically extend their initial findings. Specifically, we will employ unsupervised discriminative feature learning to mitigate the human bias in meibography analysis, discover previously unrecognized features of the Meibomian glands, and to analyze the links between these features and MGD, tear film instability, and the clinical signs and symptoms indicative of DE.

项目概要 干眼 (DE) 是一种非常普遍的疾病，对个人和社会产生重大影响， 继续逃避简单的诊断和治疗 DE 最常见的原因被认为是睑板腺。 腺体功能障碍（MGD）。上眼睑和下眼睑的睑板腺分泌脂质，形成薄层。 在MGD中，覆盖水性泪液并抑制其蒸发的薄膜被认为是不充分和/或较差的。 优质泪液脂质被分泌，导致泪膜不稳定、蒸发和DE症状。 MGD 中发生的变化尚不清楚，我们也无法确定 MGD 的哪些方面构成 泪膜不稳定和 DE 的最大风险。 干眼评估和管理 (DREAM) 研究是 ω3 脂肪酸的临床试验 治疗 DE 的补充 在试验过程中，有一个大型的睑板腺造影图像数据库 – 外翻眼睑的红外图像揭示了睑板腺——使用 通过目视检查来表征腺体 13 个不同方面并分析其特征的新颖方案 与临床评估的分泌脂质质量的关系该过程是艰巨且耗时的。 消费，本质上受到人类偏见的影响，并且几乎没有提供关于之间联系的新信息 睑板腺特征和 DE 体征和症状。 人工智能 (AI) 的最新进展使我们能够训练监督机器学习 用于自动检测和量化睑板照相图像的详细形态特征的算法 这些详细的形态特征可能包含有关腺体的大量信息。 睑板腺的健康和功能，并可以提供有关机制的有价值的信息 MGD 及其临床意义的背后是一种用于医学成像的新兴人工智能技术—— 无监督的判别特征学习——减轻人类偏见，并有可能发现 以前未识别的睑板描记图像中的特性，以及与关键临床终点的可能联系，例如 泪膜不稳定和DE症状。 在这个项目中，我们建议利用这种新的人工智能技术来重新分析 DREAM 研究临床 具体来说，我们将采用 meibography 图像数据库来极大地扩展他们的初步发现。 无监督判别特征学习可减轻眉谱分析中的人类偏见，发现 以前未被识别的睑板腺特征，并分析这些特征之间的联系 和 MGD、泪膜不稳定以及 DE 的临床体征和症状指标。