Supraspinatus tendon injury is a common clinical shoulder joint disease and is one of the most common causes of shoulder pain and dysfunction. Supraspinatus tendon injury will lead to articular cartilage injury and degeneration, then cause joint disease, seriously affect the quality of life of patients, and bring a huge burden to the family and society. This paper mainly studies and evaluates the application value of special signs of shoulder joint and indirect MR imaging in the diagnosis of supraspinatus tendon injury. Through a series of special examinations for the diagnosis of supraspinatus tendon injury in 90 patients, including zero degree abduction resistance test, arm drop test, Jobe test, Neer sign, and Hawkins sign, all patients in the study underwent indirect magnetic resonance imaging of the shoulder joint. Finally, arthroscopic examination results were used as the “gold standard” to evaluate and analyze the diagnosis. The results showed that among the special signs, the specificity of the falling-arm test was the highest (72.2%) in the diagnosis of full-thickness supraspinatus tendon injury. Hawkins sign had the highest sensitivity (84.0%). In the diagnosis of partial supraspinatus tendon injury, the specificity of the Jobe test was the highest, which was 66.6%. The Neer sign had the highest sensitivity of 50.0%. In the diagnosis of full-thickness supraspinatus tendon injury, there was no significant difference in sensitivity between indirect MRI and Hawkins sign, but the diagnostic specificity of indirect MRI was higher than that of special sign examination. In the diagnosis of partial supraspinatus tendon injury, the sensitivity and specificity of indirect MR imaging are higher than those of special sign examination.
This paper proposes a tempo feature extraction method based on the long-term modulation spectrum analysis. To transform the modulation spectrum to a condensed feature vector, the log-scale modulation frequency coefficients are introduced. This idea aims at averaging the modulation frequency energy via the constant-Q filter-banks. Further it is pointed out that the feature can be extracted directly from the perceptually compressed data of digital music archives. To verify the effectiveness of the feature and its utility to music applications, the feature vector is used in a music emotion classification system. The system consisting two layers of Adaboost classifiers. In the first layer the conventional timbre features are employed. Then by adding the tempo feature in the second layer, the classification precision is improved dramatically. By this way the discriminability of the classifier based on the given features can be exploited extremely. The system obtains high classification precision on a small corpus. It proves that the proposed feature is very effective and computationally efficient to characterize the tempo information of music.
Aim. To study the application value of ankle fracture classification and diagnosis. In this paper, the clinical data of 100 cases of ankle fracture patients admitted from May 2020 to May 2021 were analyzed by CT 3D reconstruction. All patients received surgical treatment and underwent spiral CT 3D reconstruction and X-ray examination before surgery. The results showed that 20 cases (20.00%) of the 100 cases were PER, 24 cases (24%) of the 100 cases were PAB, 31 cases (31%) of the 100 cases were SER, and 25 cases (25%) of the 100 cases were SAB, respectively. Conclusion. The diagnostic accuracy of CT 3D reconstruction for different types of ankle fracture is higher than that of X-ray, and the differences are statistically significant (
P
<
0.05
). CT 3D reconstruction is applied in the early diagnosis of ankle fracture, which can accurately detect the classification of patients. It has important clinical application value and can be used as the first choice for the early classification diagnosis of ankle fracture.
This paper mainly introduces the relevant contents of automatic assessment of upper limb mobility after stroke, including the relevant knowledge of clinical assessment of upper limb mobility, Kinect sensor to realize spatial location tracking of upper limb bone points, and GCRNN model construction process. Through the detailed analysis of all FMA evaluation items, a unique experimental data acquisition environment and evaluation tasks were set up, and the results of FMA prediction using bone point data of each evaluation task were obtained. Through different number and combination of tasks, the best coefficient of determination was achieved when task 1, task 2, and task 5 were simultaneously used as input for FMA prediction. At the same time, in order to verify the superior performance of the proposed method, a comparative experiment was set with LSTM, CNN, and other deep learning algorithms widely used. Conclusion. GCRNN was able to extract the motion features of the upper limb during the process of movement from the two dimensions of space and time and finally reached the best prediction performance with a coefficient of determination of 0.89.
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