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: 10703363
• 负责人: Meng Ching Lin
• 金额: $ 17.84万
• 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/10703363
• 关键词: Affect; Algorithms; Area; Artificial Intelligence; Atrophic; Characteristics; Classification; Clinical; Clinical Data; Clinical Management; Clinical Trials; Clinical assessments; Consumption; Correlation Studies; Data; Data Set; Databases; Diagnosis; Evaluation; Eyelid structure; Fatty Acids; Film; Gland; Goals; Hand; Health; Human; Image; Image Analysis; Individual; Laboratories; 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.

项目摘要 干眼症（DE）是一种高度普遍的状况，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的最常见原因被认为是Meibomian 腺功能障碍（MGD）。上眼睑和下眼睑中的Meibomian腺体分泌脂质，形成薄的脂质 覆盖水性眼泪并抑制其蒸发的薄膜。在MGD中，人们认为不足和/或贫穷 优质的泪液是分泌的，导致撕裂膜不稳定性，蒸发和DE症状。腺体 MGD中发生的变化尚不清楚，我们无法确定MGD姿势的哪些方面 撕裂膜不稳定和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 用于治疗的补充。在试验过程中 露出梅博米亚腺的灭眼皮的红外图像是使用A编译和分析的 通过视觉检查来表征腺体的13个不同方面的新颖方案，并分析其 与分泌脂质的临床评估质量的关系。这个过程是充满活力和时间的 消费，固有地受到人类偏见的影响，并提供有关链接之间几乎没有的新信息 Meibomian腺体的特征以及符号和符号。 人工智能（AI）的最新进展使我们能够培训监督的机器学习 大缩影图像上的算法自动检测和量化了该图像的详细形态特征 个别腺体。这些详细的形态特征可能包含有关 Meibomian腺体的健康和功能，可以提供有关机制的宝贵信息 MGD及其临床意义的背后。进一步的新兴AI技术用于医学成像 - 无监督的歧视性特征学习 - 减轻人类的偏见，并可能发现 以前未知的物质图像中的特性，以及与关键临床终点的可能链接 泪膜不稳定和症状。 在这个项目中，我们建议利用这种新的AI技术重新分析梦想研究 大缩影图像的数据库可极大地扩展其初始发现。具体来说，我们将雇用 无监督的歧视性特征学习以减轻人为分析中的人类偏见，发现 以前无法识别的Meibomian腺体，并分析这些功能之间的链接 和MGD，泪膜不稳定性以及指示DE的临床体征和符号。