■训练数据推动和塑造人工智能(AI)模型的发展。密集的数据需求是限制AI工具在固有数据稀缺的行业中成功的主要瓶颈。在医疗保健方面,训练数据很难策划,引发了越来越多的担忧,即当前贫困社会群体无法获得医疗保健将转化为未来医疗保健AI的偏见。在这份报告中,我们开发了一个自动编码器来增长和增强固有的稀缺数据集,以减轻我们对大数据的依赖。
■使用开源数据的计算研究。
■数据来自6个开源数据集,包括新加坡40-80岁的患者,中国,印度,和西班牙。
■报告的框架基于真实世界的患者成像数据生成合成图像。作为一个测试用例,我们使用自动编码器来扩展公开可用的光盘照片训练集,并评估所得数据集训练AI模型检测青光眼视神经病变的能力。
■接收器工作特征曲线(AUC)下的面积用于评估青光眼检测器的性能。更高的AUC指示更好的检测性能。
■结果表明,使用自动编码器生成的合成图像增强数据集导致了出色的训练集,从而提高了AI模型的性能。
■我们的发现有助于解决AI模型开发日益站不住脚的数据量和质量要求,并具有超出医疗保健的影响。为所有类似的数据挑战领域授权AI采用。
■作者对本文讨论的任何材料都没有专有或商业利益。
UNASSIGNED: Training data fuel and shape the development of artificial intelligence (AI) models. Intensive data requirements are a major bottleneck limiting the success of AI tools in sectors with inherently scarce data. In health care, training data are difficult to curate, triggering growing concerns that the current lack of access to health care by under-privileged social groups will translate into future bias in health care AIs. In this report, we developed an autoencoder to grow and enhance inherently scarce datasets to alleviate our dependence on big data.
UNASSIGNED: Computational study with open-source data.
UNASSIGNED: The data were obtained from 6 open-source datasets comprising patients aged 40-80 years in Singapore, China, India, and Spain.
UNASSIGNED: The reported framework generates synthetic images based on real-world patient imaging data. As a test case, we used autoencoder to expand publicly available training sets of optic disc photos, and evaluated the ability of the resultant datasets to train AI models in the detection of glaucomatous optic neuropathy.
UNASSIGNED: Area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the glaucoma detector. A higher AUC indicates better detection performance.
UNASSIGNED: Results show that enhancing datasets with synthetic images generated by autoencoder led to superior training sets that improved the performance of AI models.
UNASSIGNED: Our findings here help address the increasingly untenable data volume and quality requirements for AI model development and have implications beyond health care, toward empowering AI adoption for all similarly data-challenged fields.
UNASSIGNED: The authors have no proprietary or commercial interest in any materials discussed in this article.