Algorithm achieves very high accuracy to diagnose many different diseases and predict their progression via tongue color

Good news! Coming to your smartphone soon! Say  "aah"!

What is next? Hand reading? 😊

"... Two teaching hospitals in the Middle East supplied 60 tongue images from patients with various health conditions. The artificial intelligence (AI) model was able to match the tongue color with the disease in almost all cases. ...

AI is replicating a 2,000-year-old practice widely used in traditional Chinese medicine—examining the tongue for signs of disease. ...

a smartphone will be used to diagnose disease in this way. ..."

"... The proposed imaging system developed by Iraqi and Australian researchers can diagnose diabetes, stroke, anaemia, asthma, liver and gallbladder conditions, COVID-19, and a range of vascular and gastrointestinal issues. ..."

From the abstract:

"The diagnosis of ... disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. 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%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems."

Algorithm achieves 98% accuracy in disease prediction via tongue color

Say ‘aah’ and get a diagnosis on the spot: is this the future of health? (original news release) "A computer algorithm has achieved a 98% accuracy in predicting different diseases by analysing the colour of the human tongue."

Tongue Disease Prediction Based on Machine Learning Algorithms (open access)

Figure 2. Block diagram of the tongue color analysis system.