Medical expert and machine learning analysis of lumbar disc herniation based on magnetic resonance imaging

Abstract

ObjectiveObservation and statistical analysis was used to evaluate the ability of lumbar disc magnetic resonance imaging (MRI) to obtain the smallest size of Al2O3 spots (calcified foci) and lumbar disc fiber signals. MethodsFirst, we perform image acquisition of the MRI, perform the statistical analysis using five different sizes of Al2O3 spots and lumbar disc fibers on the imaging plate (IP), use a molybdenum target MRI machine 26 kV, adjust the milliampere amount, select the appropriate image processing parameters, and obtain the experimental image of the density value (D=0.70±0.05), the 5-point judgment method is used to obtain the total score of 10 lines of signals composed of 5 signals and noise, and a group is computed using the statistical analysis that is built from human observation and machine prediction (based on machine learning), which are then compared. In particular, we implemented a convolutional neural network algorithm to evaluate the medical condition against human observers, so as to study the structure of the lumbar intervertebral disc. We compute the true positive probability P(S/s) and false positive probability P(S/n) values, draw ROC curve, and compute the judgment probability value of each signal Pdet. We then use SPSS 10.0 statistical single factor analysis of variance software to process the data, and obtain the smallest calcified focus and lumbar disc mass focus. ResultsUsing probability statistical methods to obtain the data of the ROC curve and the average value of the judgment probability Pdet, among 5 different sizes Al2O3 spots (calcifications), 0.20mm Pdet= 0.6250minimum, 0.55mm Pdet = 0.9000 the largest, but the difference between 0.20mm and 0.25mm Pdet is not statistically significant, and the difference is statistically significant; among the five types of lumbar disc fibers (tumor foci) of different sizes, 0.45mm Pdet= 0.5313minimum, 1.00mm Pdet =0.8813 is the largest, while the difference between 0.45mm and 0.60mm is not statistically significant, and the difference between 0.45mm and other is statistically significant. We note that the human observation and machine learning prediction is not significantly different (P<0.05). ConclusionsThe computation of the ROC curve and that of the probability of judgment using the statistical analysis based on a deep learning platform is simple and fast, and approximates that of human observation. It is suitable for the evaluation of image quality control carried out by daily clinical work.