A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

许多大型科技公司已经创建了基于云的无代码平台，允许没有编码经验的研究人员和临床医生创建深度学习算法。在这项研究中，我们综合分析了六个平台的性能和特征集，使用四个具有代表性的横截面和正面医学成像数据集来创建图像分类模型。所有模型-数据集对跨平台的平均 (sd) F1 分数如下：亚马逊，93.9 (5.4)；苹果，72.0 (13.6)；克拉里费，74.2（7.1）；谷歌，92.0 (5.4)；医学思维，90.7 (9.6)；微软，88.6 (5.3)。这些平台在光学相干断层扫描模式下表现出一致更高的分类性能。经过适当验证的潜在用例包括研究数据集管理，