No abstract

No abstract

Tongue diagnosis, a noninvasive examination, is an essential step for syndrome differentiation and treatment in traditional Chinese medicine (TCM). Sublingual vein (SV) is examined to determine the presence of blood stasis and blood stasis syndrome. Many studies have shown that the degree of SV stasis positively correlates with disease severity. However, the diagnoses of SV examination are often subjective because they are influenced by factors such as physicians’ experience and color perception, resulting in different interpretations. Therefore, objective and scientific diagnostic approaches are required to determine the severity of sublingual varices. This study aims at developing a computer-assisted system based on machine learning (ML) techniques for diagnosing the severity of sublingual varicose veins. We conducted a comparative study of the performance of several supervised ML models, including the support vendor machine, K-neighbor, decision tree, linear regression, and Ridge classifier and their variants. The main task was to differentiate sublingual varices into mild and severe by using images of patients’ SVs. To improve diagnostic accuracy and to accelerate the training process, we proposed using two model reduction techniques, namely, the principal component analysis in conjunction with the slice inverse regression and the convolution neural network (CNN), to extract valuable features during the preprocessing of data. Our results showed that these two extraction methods can reduce the training time for the ML methods, and the Ridge-CNN method can achieve an accuracy rate as high as 87.5%, which is similar to that of experienced TCM physicians. This computer-aided tool can be used for reference clinical diagnosis. Furthermore, it can be employed by junior physicians to learn and to use in clinical settings.

No abstract

This study aims to measure Brewster’s angle of glass and acrylic brick with an easy-to-obtain mobile application (app) by changing the tungsten light source to a red laser. The popularization of the smartphone has inspired many to use its various built-in sensors to carry out general physics experiments. Many others have adopted both a laser and a light sensor to perform such measurements. In Refs. 10 and 11, the devices required specific software and are not widely available in physics laboratories. In this paper, we present a similar study, but one that has the advantage that it uses the ambient light sensor of the smartphone. In this work, a simple and inexpensive experimental setup is proposed to quantitatively measure Brewster’s angle.