基于图像的饮食能量和巨量营养素估计与聊天GPT-5:跨源评估跨升级的背景情景
Marcela Rodríguez-Jiménez1, Gustavo Daniel Martín-Del-Campo-Becerra2, Sandra Sumalla-Cano1,3,4
1Research Group on Foods, Nutritional Biochemistry and Health, Universidad Europea del Atlántico, 39011 Santander, Spain.
Nutrients
|November 27, 2025
概括
聊天GPT-5从食物图像中准确估计卡路里和宏观营养素,通过添加描述符和成分等上下文进行改进. 视觉线索在很大程度上有助于准确性,即使配料数据可用.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 营养科学 营养科学
背景情况:
- 从图像中估计食物能量和宏观营养素至关重要,但具有挑战性.
- 准确的评估需要透明的指标和承认数据的限制.
研究的目的:
- 评估ChatGPT-5对卡路里和巨量营养素估计的准确性.
- 在四个场景中评估绩效,使用不同的上下文信息.
主要方法:
- 来自不同来源的195道菜在四个情况下进行了评估:仅图像,图像+描述符,图像+成分,仅成分.
- 主要终点:kcal 平均绝对误差 (MAE);次要:MedAE,RMSE 对于kcal和巨量营养素.
- 95%信心区间 (CI) 和引导重新抽样被用于准确度指标.
主要成果:
- 准确性 (MAE,MedAE,RMSE) 随着添加上下文 (案例1<案例2<案例3) 显著提高.
- 排除图像 (案例4) 降低了准确性,强调了视觉线索的重要性.
- 在家庭准备的饭菜中,收益最为明显,并且在不同数据源中一致.
结论:
- 聊天GPT-5提供了实际有用的食物能量和巨量营养素估计,增强了非视觉描述符和成分.
- 来自图像的视觉信息对估计准确性的有意义的贡献者.
- 未来的研究应该使用不同的数据集,共享提示/输出,并与非AI方法进行比较.
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