Evaluating predictive models in reproductive medicine
Evaluating predictive models in reproductive medicine
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DOI：
10.1016/j.fertnstert.2020.09.159
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发表时间：
2020-11-01
影响因子：
6.7
通讯作者：
Chavez-Badiola, Alejandro
中科院分区：
文献类型：
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作者：
Lynn Curchoe, Carol;Flores-Saiffe Farias, Adolfo;Chavez-Badiola, Alejandro
预测建模已经成为生殖医学的一个独特的分支学科，研究人员和临床医生正在学习评估人工智能（AI）研究的技能和专业知识。诊断测试和模型预测有待评估。它们的使用在诊断、治疗和预后方面提供了潜在的危害和益处。AI模型的性能及其潜在的临床效用取决于所用数据库的质量和大小、数据的类型和分布以及所应用的特定AI方法。此外，当涉及到图像时，图像的捕获、预处理、处理和准确标记的方法成为人工智能建模的重要组成部分。不一致的图像处理或不准确的图像标记可能会导致数据库不一致，从而导致AI准确性差。我们讨论了人工智能模型在生殖医学中的关键评估，并传达了报告人工智能模型的透明度和标准化的重要性，以便评估人工智能的偏倚风险和潜在的临床效用。（（C）2020由美国生殖医学学会。
Predictive modeling has become a distinct subdiscipline of reproductive medicine, and researchers and clinicians are just learning the skills and expertise to evaluate artificial intelligence (AI) studies. Diagnostic tests and model predictions are subject to evaluation. Their use offers potential for both harm and benefit in terms of diagnosis, treatment, and prognosis. The performance of AI models and their potential clinical utility hinge on the quality and size of the databases used, the types and distribution of data, and the particular AI method applied. Additionally, when images are involved, the method of capturing, preprocessing, and treatment and accurate labeling of images becomes an important component of AI modeling. Inconsistent image treatment or inaccurate labeling of images can lead to an inconsistent database, resulting in poor AI accuracy. We discuss the critical appraisal of AI models in reproductive medicine and convey the importance of transparency and standardization in reporting AI models so that the risk of bias and the potential clinical utility of AI can be assessed. ((C)2020 by American Society for Reproductive Medicine.)