A preliminary study of contrast-enhanced ultrasound guided capsule-preserving hydrodilatation in shoulder adhesive capsulitis.

Background: Capsule-preserving hydrodilatation is a common treatment for adhesive capsulitis (AC), and ultrasound (US) has recently become the most popular adjuvant tool for image-guided glenohumeral joint injection. However, traditional US is hardly adequate to assess extracapsular fluid leakage, which may decide the treatment outcomes. In this study, we explored the value of contrast-enhanced ultrasound (CEUS) guided capsule-preserving hydrodilatation with steroids and ultrasonic contrast agents for treatment of AC. Methods: A total of 40 consecutive patients with AC were prospectively enrolled and received CEUS-guided capsule-preserving hydrodilatation. The number of injection attempts, injection volume, and fluid leakage were recorded, and the correlations with clinical features were analyzed by Pearson or Spearman correlation coefficients. Outcome measures including visual analog scale (VAS) score, passive range of motion (ROM), and shoulder pain and disability index (SPADI) score were evaluated at baseline and 4 weeks after treatment. Comparisons between patients with good and poor clinical outcomes were performed with independent t-test, Mann-Whitney U test, and chi-square test. Logistic regression was used to identify predictors of good clinical outcomes. A P value <0.05 defined significance. Results: Access to the glenohumeral joint was successful in 87.5% patients on the first attempt. The infused fluid volume was 21.0±3.40 mL. Longer symptom duration (r=-0.676, P<0.001), greater SPADI (r=-0.148, P=0.007), and decreased ROM in abduction (r=0.38, P=0.016) were associated with a decreased volume of infused fluid. CEUS detected massive fluid leakage in 5 (12.5%) patients, with 4 capsule ruptures confirmed by magnetic resonance imaging (MRI). Longer symptom duration (r=0.485, P=0.001), decreased ROM in the direction of abduction (r=-0.33, P=0.037), and external rotation (r=-0.34, P=0.032) were correlated with an increased incidence of massive fluid leakage. Moreover, patients with good outcomes had significantly shorter symptom duration (5.7±2.09 vs. 11.2±3.89 months, P=0.002) and greater initial VAS score (6.9±1.04 vs. 6.3±0.50, P=0.022) than those with poor outcomes. Absence of massive fluid leakage was an independent predictor of clinical good outcomes at 4 weeks after treatment [odd ratio (OR) =0.05, 95% confidential interval (CI): 0.003-0.882, P=0.041]. Conclusions: CEUS-guided capsule-preserving hydrodilatation allows real-time visualization of capsule dilatation, accurate detection of extracapsular fluid leakage, and identification of risks for capsule rupture. It provides an effective treatment for AC, and is useful to predict patients' clinical outcomes.

Thangka Image Captioning Based on Semantic Concept Prompt and Multimodal Feature Optimization.

Thangka images exhibit a high level of diversity and richness, and the existing deep learning-based image captioning methods generate poor accuracy and richness of Chinese captions for Thangka images. To address this issue, this paper proposes a Semantic Concept Prompt and Multimodal Feature Optimization network (SCAMF-Net). The Semantic Concept Prompt (SCP) module is introduced in the text encoding stage to obtain more semantic information about the Thangka by introducing contextual prompts, thus enhancing the richness of the description content. The Multimodal Feature Optimization (MFO) module is proposed to optimize the correlation between Thangka images and text. This module enhances the correlation between the image features and text features of the Thangka through the Captioner and Filter to more accurately describe the visual concept features of the Thangka. The experimental results demonstrate that our proposed method outperforms baseline models on the Thangka dataset in terms of BLEU-4, METEOR, ROUGE, CIDEr, and SPICE by 8.7%, 7.9%, 8.2%, 76.6%, and 5.7%, respectively. Furthermore, this method also exhibits superior performance compared to the state-of-the-art methods on the public MSCOCO dataset.

Data-driven prediction of greenhouse aquaponics air temperature based on adaptive time pattern network.

Greenhouse aquaponics system (GHAP) improves productivity by harmonizing internal environments. Keeping a suitable air temperature of GHAP is essential for the growth of plant and fish. However, the disturbance of various environmental factors and the complexity of temporal patterns affect the accuracy of the microclimate time-series forecasting. This work proposed an Adaptive Time Pattern Network (ATPNet) to predict GHAP air temperature, which consists of deep temporal feature (DTF) module, multiple temporal pattern convolution (MTPC) module, and spatial attention mechanism (SAM) module. The DTF module has a wide sensory range and can capture information over a long-time span. The MTPC module is designed to improve model response performance by exploiting the effective temporal information of different environmental factors at different times. At the same time, the SAM can explore the correlations among different environmental factors. The ATPNet found that air temperature of GHAP has a strong correlation with other temperature-related parameters (external air temperature, external soil temperature, and water temperature). Compared with the best performance of three baseline models (multilayer perceptron (MLP), recurrent neural network (RNN), and Temporal Convolutional Network (TCN)), the ATPNet enhanced overall prediction performance for the following 24 h by 7.44% for root mean squared error (RMSE), 2.53% for mean absolute error (MAE), and 3.15% for mean absolute percentage error (MAPE), respectively.

Detection of chlorophyll fluorescence parameters of potato leaves based on continuous wavelet transform and spectral analysis.

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650-800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.

Diagnostic Value of Quantitative Analysis by Contrast-Enhanced Ultrasound of Endometrial Lesions.

OBJECTIVES: To explore the diagnostic value of time-intensity curve parameters from contrast-enhanced ultrasound (CEUS) examinations of endometrial lesions. METHODS: Fifty-two patients with suspected endometrial lesions who received vaginal CEUS examinations were divided into a polyp group (n = 36), a malignant group (n = 11), and a normal control group (n = 5) based on the pathologic diagnosis. The midpoint of the myometrium at the same depth as the endometrial lesion was used as the reference point. The initial increase time, time to peak, area under the curve, and peak intensity were determined by the time-intensity curve. The relative parameters, namely, the initial increase time difference, peak time difference, area ratio, and peak intensity ratio, were also calculated and analyzed statistically. RESULTS: The comparison results of the relative parameters among the groups showed that the differences in the time difference, intensity ratio, and area ratio were statistically significant. The differences in the intensity ratio and time difference between the malignant and normal groups were statistically significant (P < .001). The differences in the intensity ratio, area ratio, and time difference between the malignant and polyp groups were statistically significant (P < .001). The diagnostic value of the intensity ratio and area ratio was confirmed by a receiver operating characteristic curve. The sensitivity and specificity of the intensity ratio and area ratio in the groups were 100% and 77.5% and 85.7% and 85.0%, respectively. CONCLUSIONS: The relative CEUS parameters in endometrial lesions provide more diagnostic value in differential diagnosis of benign and malignant lesions than the absolute parameters.

Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization.

Potato is the world's fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

Study on factors affecting local peak strain results in automatic functional imaging of transthoracic echocardiography.

BACKGROUND: To investigate the important impacting factors on the accuracy of local peak strain (RLS) in transthoracic echocardiography real-time tri-axial automatic functional imaging (AFI), to evaluate the clinical efficacy of AFI and to improve the accuracy of the results. METHODS: From May 2016 to May 2017, 82 healthy volunteers were enrolled in the AFI examination, of which 22 were excluded and 60 were eligible. The excluded 22 patients were analyzed for exclusion reasons, and the results of the 60 eligible AFI results were studied focusing on the longitudinal left ventricular regional longitudinal peak systolic strain (RLS) in different methods of operation, to compare the results and accuracies of AFI by different influencing factors, and to find the most important ones. RESULTS: The success rate of AFI for this group of subjects is 74%, and the exclusion reason is that the left ventricular segments cannot be fully displayed. Among eligible subjects, the main influencing factors on RLS were region of interest (ROI), aortic valve closure time adjustment and image frame rate selection. The differences of results obtained by different operations were statistically significant (P<0.05). CONCLUSIONS: The success rate of AFI for this group of subjects is 74%. The RLS results were influenced by multiple factors, which can be effectively avoided.