Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.
Age-related Macular Degeneration: Nutrition, Genes and Deep Learning-The LXXVI Edward Jackson Memorial Lecture.
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DOI：
10.1016/j.ajo.2020.05.042
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发表时间：
2020-09
影响因子：
4.2
通讯作者：
Chew EY
中科院分区：
文献类型：
--
作者：
Chew EY
评估营养补充剂、饮食模式和遗传关联在年龄相关性黄斑变性 (AMD) 中的重要性；并讨论人工智能/深度学习技术，以潜在地加强 AMD 检测和分类的研究。回顾性文献综述。回顾年龄相关眼病研究 (AREDS) 和 AREDS2 中对 AMD 营养、遗传变异和深度学习的前瞻性和回顾性（事后）分析研究。除了证明 AREDS 和 AREDS2 补充剂的抗氧化维生素和锌（加铜）对于降低进展为晚期 AMD 的风险的有益作用之外，这两项研究还证实了高度坚持地中海饮食对于不同疾病严重程度的人减少 AMD 进展的重要性。对于具有补体因子 H (CFH) 保护性遗传等位基因的人，地中海饮食具有进一步的有益作用。然而，尽管与 AMD 进展存在遗传相关性，但预测模型发现遗传信息对 AMD 基线严重程度对疾病进展的高预测价值几乎没有增加。深度学习技术是人工智能的一个分支，使用 AREDS/AREDS2 的彩色眼底照片，在某些情况下优于临床人类分级（视网膜专家）和经过认证的阅读中心分级师的黄金标准，在其他情况下也不逊色。就 AREDS2 补充剂的使用以及地中海饮食的有益关联向受 AMD 影响的个人提供咨询是重要的公共卫生信息。尽管基因检测在研究中很重要，但不建议将其用于预测疾病或指导 AMD 的治疗和/或饮食干预。深度学习技术前景广阔，但需要进一步的前瞻性研究来验证该技术的使用，以提高 AMD 患者临床研究和医疗管理的准确性和敏感性/特异性。
To evaluate the importance of nutritional supplements, dietary pattern, and genetic associations in age-related macular degeneration (AMD); and to discuss the technique of artificial intelligence/deep learning to potentially enhance research in detecting and classifying AMD. Retrospective literature review. To review the studies of both prospective and retrospective (post hoc) analyses of nutrition, genetic variants, and deep learning in AMD in both the Age-Related Eye Disease Study (AREDS) and AREDS2. In addition to demonstrating the beneficial effects of the AREDS and AREDS2 supplements of antioxidant vitamins and zinc (plus copper) for reducing the risk of progression to late AMD, these 2 studies also confirmed the importance of high adherence to Mediterranean diet in reducing progression of AMD in persons with varying severity of disease. In persons with the protective genetic alleles of complement factor H (CFH), the Mediterranean diet had further beneficial effect. However, despite the genetic association with AMD progression, prediction models found genetic information added little to the high predictive value of baseline severity of AMD for disease progression. The technique of deep learning, an arm of artificial intelligence, using color fundus photographs from AREDS/AREDS2 was superior in some cases and noninferior in others to clinical human grading (retinal specialists) and to the gold standard of the certified reading center graders. Counseling individuals affected with AMD regarding the use of the AREDS2 supplements and the beneficial association of the Mediterranean diet is an important public health message. Although genetic testing is important in research, it is not recommended for prediction of disease or to guide therapies and/or dietary interventions in AMD. Techniques in deep learning hold great promise, but further prospective research is required to validate the use of this technique to provide improvement in accuracy and sensitivity/specificity in clinical research and medical management of patients with AMD.
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DOI：
10.1073/pnas.0501536102
发表时间：
2005-05-17
影响因子：
11.1
作者：
Hageman, GS;Anderson, DH;Allikmets, R
通讯作者：
Allikmets, R
影响因子：
3.3
作者：
Ding, Ying;Liu, Yi;Chen, Wei
通讯作者：
Chen, Wei
影响因子：
13.7
作者：
Chew EY;Clemons TE;Agrón E;Sperduto RD;Sangiovanni JP;Kurinij N;Davis MD;Age-Related Eye Disease Study Research Group
通讯作者：
Age-Related Eye Disease Study Research Group
影响因子：
56.9
作者：
Edwards, AO;Ritter, R;Farrer, LA
通讯作者：
Farrer, LA
影响因子：
4.4
作者：
Delcourt, Cicile;Carriere, Isabelle;Schalch, Wolfgang
通讯作者：
Schalch, Wolfgang