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NAILS:用于自我保健健康的标准化人工智能标签传感器。

NAILS: Normalized Artificial Intelligence Labeling Sensor for Self-Care Health.

作者信息

Tenze Livio, Canessa Enrique

机构信息

The Abdus Salam International Centre for Theoretical Physics (ICTP), 34151 Trieste, Italy.

出版信息

Sensors (Basel). 2024 Dec 14;24(24):7997. doi: 10.3390/s24247997.

Abstract

Visual examination of nails can reflect human health status. Diseases such as nutritive imbalances and skin diseases can be identified by looking at the colors around the plate part of the nails. We present the AI-based NAILS method to detect fingernails through segmentation and labeling. The NAILS leverages a pre-trained Convolutional Neural Network model to segment and label fingernail regions from fingernail images, normalizing RGB values to monitor tiny color changes via a GUI and the use of an HD webcam in real time. The use of normalized RGB values combined with AI-based segmentation for real-time health monitoring is novel and innovative. The NAILS algorithm could be used to self-extract and archive primary signs of diseases in humans, especially in rural areas or when other testing may be not available.

摘要

指甲的外观检查可以反映人体健康状况。营养失衡和皮肤病等疾病可以通过观察指甲甲床部分周围的颜色来识别。我们提出了基于人工智能的NAILS方法,通过分割和标记来检测指甲。NAILS利用预训练的卷积神经网络模型从指甲图像中分割和标记指甲区域,通过GUI将RGB值归一化,以实时使用高清网络摄像头监测微小的颜色变化。将归一化的RGB值与基于人工智能的分割相结合用于实时健康监测是新颖且创新的。NAILS算法可用于自动提取和存档人类疾病的主要体征,特别是在农村地区或其他检测可能无法进行的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2058/11679173/922d23fcb19c/sensors-24-07997-g001.jpg

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