Abstract: The present invention provides a method for evaluating body composition and a system for using thereof. The method comprises inputting a body composition index and an individual variable to a body composition prediction model so as to produce a body composition evaluation index. The body composition index, comprising a muscle index, a fat index or a comprehensive index, is measured according to a medical image, and the individual variable corresponds to an individual variable of the medical image. The system is configured to obtain the body composition index and the individual variable, and produces the body composition evaluation index accordingly. By using the method and the system, a quantile and a body age evaluation value of a corresponding individual among a normal population can be obtained, and requires only a medical image or a body composition index produced according thereto, which assists judgement of the individual's health status.

Abstract: A method for predicting cancer prognosis is disclosed, and the method comprises capturing a reference radiomics and obtaining reference pathological eigenvalues, wherein the reference pathological eigenvalues are based on pathological features of a reference patients, and the pathological features comprise genomic features, gene expression, test values or a combination of two or more thereof. Then, capturing a test radiomics and obtaining test pathological eigenvalues are performed, wherein the test pathological eigenvalues are based on pathological features of a test patients, and the pathological features comprise genomic features, gene expression, test values or a combination of two or more thereof. A mathematical formula is used to calculate a prognostic index based on the aforementioned reference radiomics, reference pathological eigenvalues, test radiomics and test pathological eigenvalues, and the prognostic change risk of the test patient is evaluated according to the prognostic index.

Abstract: A method for analyzing gait is provided for a system including multiple accelerometers. The method includes: for each time point and each accelerometer, calculating a root mean square (RMS) value according to the accelerations sensed on sensing axes of the corresponding accelerometer; calculating a cross correlation coefficient according to the RMS values of a first accelerometer and a second accelerometer; calculating a first auto-correlation coefficient of the RMS values of the first accelerometer; calculating a second auto-correlation coefficient of the RMS values of the second accelerometer; and calculating a first gait index according to the cross correlation coefficient, the first auto-correlation coefficient, and the second auto-correlation coefficient.