ncement of clinical artificial intelligence tools. METHODS: A total of 377 videolaryngoscopy videos from YouTube were used to create 6 varied datasets, each differing in patient diversity and image count. The study also incorporates data augmentation techniques to enhance these datasets further. Two machine-learning models, YOLOv5-Small and YOLOv8-Small, were trained and evaluated on metrics such as F1 score (a statistical measure that combines the precision and recall of the model into a single metric, reflecting its overall accuracy), precision, recall, mAP@50, and mAP@50–95. RESULTS: The findings indicate a significant impact of dataset configuration on model performance, especially the balance between diversity and quantity. The Multi-25 × 10 dataset, featuring 25 images from 10 different patients, demonstrates superior performance, highlighting the value of a well-balanced dataset. The study also finds that the effects of data augmentation vary across different types of datasets. CONCLUSIONS: Overall, this study emphasizes the critical role of dataset structure in the performance of machine-learning models in medical image analysis. It underscores the necessity of striking an optimal balance between dataset size and diversity, thereby illuminating the complexities inherent in data-driven machine-learning development....

中文翻译：

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图像分析中机器学习模型性能的比较分析：数据集多样性和大小的影响


临床人工智能工具的 ncement。方法： 总共使用来自 YouTube 的 377 个视频喉镜视频来创建 6 个不同的数据集，每个数据集在患者多样性和图像计数方面都不同。该研究还结合了数据增强技术，以进一步增强这些数据集。两个机器学习模型 YOLOv5-Small 和 YOLOv8-Small 根据 F1 分数（一种统计指标，将模型的精度和召回率组合成一个指标，反映其整体准确性）、精度、召回率、mAP@50 和 mAP@50-95 等指标进行了训练和评估。结果： 研究结果表明，数据集配置对模型性能有重大影响，尤其是多样性和数量之间的平衡。Multi-25 × 10 数据集包含来自 10 名不同患者的 25 张图像，展示了卓越的性能，突出了均衡数据集的价值。该研究还发现，数据增强的效果因不同类型的数据集而异。结论： 总体而言，本研究强调了数据集结构在机器学习模型在医学图像分析性能中的关键作用。它强调了在数据集大小和多样性之间取得最佳平衡的必要性，从而阐明了数据驱动的机器学习开发中固有的复杂性。