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"Non-Contact Biometric System for Early Detection of Hypertension and Diabetes Using AI and RGB Imaging" (2024) https://www.ahajournals.org/doi/10.1161/circ.150
by westurner 2y ago
"Non-Contact Biometric System for Early Detection of Hypertension and Diabetes Using AI and RGB Imaging" (2024) https://www.ahajournals.org/doi/10.1161/circ.150.suppl_1.4131796 https://www.ahajournals.org/doi/10.1161/circ.150.suppl_1.413...
From https://www.notebookcheck.net/University-of-Tokyo-researchers-prototype-optical-measurement-of-blood-pressure-and-glucose-levels.920305.0.html https://www.notebookcheck.net/University-of-Tokyo-researcher... :
> optical, non-contact method to detect [blood pressure, hypertension, blood glucose levels, and diabetes]. A multispectral [UV + IR] camera was used and pointed at trial participants' faces to detect variations in the blood flow, which was further analyzed by AI software.
- westurner 2y agoFrom https://news.ycombinator.com/item?id=41381977 https://news.ycombinator.com/item?id=41381977 : > "Tongue Disease Prediction Based on Machine Learning Algorithms" (2024) https://www.mdpi.com/2227-7080/12/7/97 https://www.mdpi.com/2227-7080/12/7/97 : >> This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%