Prediction of midpalatal suture maturation stage based on transfer learning and enhanced vision transformer.

BACKGROUND: Maxillary expansion is an important treatment method for maxillary transverse hypoplasia. Different methods of maxillary expansion should be carried out depending on the midpalatal suture maturation levels, and the diagnosis was validated by palatal plane cone beam computed tomography (CBCT) images by orthodontists, while such a method suffered from low efficiency and strong subjectivity. This study develops and evaluates an enhanced vision transformer (ViT) to automatically classify CBCT images of midpalatal sutures with different maturation stages. METHODS: In recent years, the use of convolutional neural network (CNN) to classify images of midpalatal suture with different maturation stages has brought positive significance to the decision of the clinical maxillary expansion method. However, CNN cannot adequately learn the long-distance dependencies between images and features, which are also required for global recognition of midpalatal suture CBCT images. The Self-Attention of ViT has the function of capturing the relationship between long-distance pixels of the image. However, it lacks the inductive bias of CNN and needs more data training. To solve this problem, a CNN-enhanced ViT model based on transfer learning is proposed to classify midpalatal suture CBCT images. In this study, 2518 CBCT images of the palate plane are collected, and the images are divided into 1259 images as the training set, 506 images as the verification set, and 753 images as the test set. After the training set image preprocessing, the CNN-enhanced ViT model is trained and adjusted, and the generalization ability of the model is tested on the test set. RESULTS: The classification accuracy of our proposed ViT model is 95.75%, and its Macro-averaging Area under the receiver operating characteristic Curve (AUC) and Micro-averaging AUC are 97.89% and 98.36% respectively on our data test set. The classification accuracy of the best performing CNN model EfficientnetV2_S was 93.76% on our data test set. The classification accuracy of the clinician is 89.10% on our data test set. CONCLUSIONS: The experimental results show that this method can effectively complete CBCT images classification of midpalatal suture maturation stages, and the performance is better than a clinician. Therefore, the model can provide a valuable reference for orthodontists and assist them in making correct a diagnosis.

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image.

BACKGROUND: Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M[Formula: see text]IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient's physical condition. METHODS: This retrospective study utilizes and analyzes chest CT images from 327 patients. Among them, 1613 images from 286 patients are used for model training and validation, while the remaining 41 patients are reserved as an additional test set for conducting ablation studies, comparative studies, and observer studies. The M[Formula: see text]IQA method is driven by DL technology and employs a multi-view fusion strategy, which incorporates three scanning planes (coronal, axial, and sagittal). It assesses image quality for multiple tasks, including inspiration evaluation, position evaluation, radiation protection evaluation, and artifact evaluation. Four algorithms (pixel threshold, neural statistics, region measurement, and distance measurement) have been proposed, each tailored for specific evaluation tasks, with the aim of optimizing the evaluation performance of the M[Formula: see text]IQA method. RESULTS: In the additional test set, the M[Formula: see text]IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score. Extensive ablation and comparative studies have demonstrated the effectiveness of the proposed algorithms and the generalization performance of the proposed method across various assessment tasks. CONCLUSION: This study develops and validates a DL-driven M[Formula: see text]IQA method, complemented by four proposed algorithms. It holds great promise in automating the assessment of chest CT image quality. The performance of this method, as well as the effectiveness of the four algorithms, is demonstrated on an additional test set.

Applications of radiomics-based analysis pipeline for predicting epidermal growth factor receptor mutation status.

BACKGROUND: This study aimed to develop a pipeline for selecting the best feature engineering-based radiomic path to predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS: The study enrolled 115 lung adenocarcinoma patients with EGFR mutation status from June 2016 and September 2017. We extracted radiomics features by delineating regions-of-interest around the entire tumor in 18F-FDG PET/CT images. The feature engineering-based radiomic paths were built by combining various methods of data scaling, feature selection, and many methods for predictive model-building. Next, a pipeline was developed to select the best path. RESULTS: In the paths from CT images, the highest accuracy was 0.907 (95% confidence interval [CI]: 0.849, 0.966), the highest area under curve (AUC) was 0.917 (95% CI: 0.853, 0.981), and the highest F1 score was 0.908 (95% CI: 0.842, 0.974). In the paths based on PET images, the highest accuracy was 0.913 (95% CI: 0.863, 0.963), the highest AUC was 0.960 (95% CI: 0.926, 0.995), and the highest F1 score was 0.878 (95% CI: 0.815, 0.941). Additionally, a novel evaluation metric was developed to evaluate the comprehensive level of the models. Some feature engineering-based radiomic paths obtained promising results. CONCLUSIONS: The pipeline is capable of selecting the best feature engineering-based radiomic path. Combining various feature engineering-based radiomic paths could compare their performances and identify paths built with the most appropriate methods to predict EGFR-mutant lung adenocarcinoma in 18FDG PET/CT. The pipeline proposed in this work can select the best feature engineering-based radiomic path.

Virtual monoenergetic images by spectral detector computed tomography may improve image quality and diagnostic ability for ischemic lesions in acute ischemic stroke.

BACKGROUND: Acute ischemic lesions are challenging to detect by conventional computed tomography (CT). Virtual monoenergetic images may improve detection rates by increased tissue contrast. PURPOSE: To compare the ability to detect ischemic lesions of virtual monoenergetic with conventional images in patients with acute stroke. MATERIAL AND METHODS: We included consecutive patients at our center that underwent brain CT in a spectral scanner for suspicion of acute stroke, onset <12 h, with or without (negative controls) a confirmed cortical ischemic lesion in the initial scan or a follow-up CT or magnetic resonance imaging. Attenuation was measured in predefined areas in ischemic gray (guided by follow-up exams), normal gray, and white matter in conventional images and retrieved in spectral diagrams for the same locations in monoenergetic series at 40-200 keV. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Visual assessment of diagnostic measures was performed by independent review by two neuroradiologists blinded to reconstruction details. RESULTS: In total, 29 patients were included (January 2018 to July 2019). SNR was higher in virtual monoenergetic compared to conventional images, significantly at 60-150 keV. CNR between ischemic gray and normal white matter was higher in monoenergetic images at 40-70 keV compared to conventional images. Virtual monoenergetic images received higher scores in overall image quality. The sensitivity for diagnosing acute ischemia was 93% and 97%, respectively, for the reviewers, compared to 55% of the original report based on conventional images. CONCLUSION: Virtual monoenergetic reconstructions of spectral CIs may improve image quality and diagnostic ability in stroke assessment.

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images.

Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled 'black-box' by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.

[Texture filtering based unsupervised registration methods and its application in liver computed tomography images].

Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.

[An automatic pulmonary nodules detection algorithm with multi-scale information fusion].

Lung nodules are the main manifestation of early lung cancer. So accurate detection of lung nodules is of great significance for early diagnosis and treatment of lung cancer. However, the rapid and accurate detection of pulmonary nodules is a challenging task due to the complex background, large detection range of pulmonary computed tomography (CT) images and the different sizes and shapes of pulmonary nodules. Therefore, this paper proposes a multi-scale feature fusion algorithm for the automatic detection of pulmonary nodules to achieve accurate detection of pulmonary nodules. Firstly, a three-layer modular lung nodule detection model was designed on the deep convolutional network (VGG16) for large-scale image recognition. The first-tier module of the network is used to extract the features of pulmonary nodules in CT images and roughly estimate the location of pulmonary nodules. Then the second-tier module of the network is used to fuse multi-scale image features to further enhance the details of pulmonary nodules. The third-tier module of the network was fused to analyze the features of the first-tier and the second-tier module of the network, and the candidate box of pulmonary nodules in multi-scale was obtained. Finally, the candidate box of pulmonary nodules under multi-scale was analyzed with the method of non-maximum suppression, and the final location of pulmonary nodules was obtained. The algorithm is validated by the data of pulmonary nodules on LIDC-IDRI common data set. The average detection accuracy is 90.9%.

Cancer-induced spiculation on computed tomography: a significant preoperative prognostic factor for colorectal cancer.

PURPOSE: Cancer-induced spiculation (CIS) on computed tomography, which is reticular or linear opacification of the pericolorectal fat tissues around the cancer site, is generally regarded as cancer infiltration into T3 or T4, but its clinicopathological significance is unknown. This study examines the correlation between CIS and clinicopathological findings to establish its prognostic value. METHODS: The subjects of this retrospective study were 335 patients with colorectal cancer (CRC), who underwent curative surgery between January, 2010 and December, 2011, at the National Defense Medical College Hospital in Saitama Prefecture, Japan. RESULTS: The level of interobserver agreement in the evaluation of CIS was substantial (83%; kappa value, 0.65). The presence of CIS was specific for T3/T4 disease (positive predictive value, 88.3%), and was significantly associated with tumor size and venous invasion. The 5-year relapse-free survival rate was significantly lower in patients with CIS than in those without CIS (68.6% and 84.0%, respectively, p = 0.001). Subgroup analysis revealed remarkable prognostic differences in patients with stage III and T3 disease. Multivariate analysis revealed that CIS was a significant independent prognostic factor. CONCLUSIONS: CIS was a significant preoperative prognostic factor and could be useful in the selection of preoperative therapy for patients with CRC.

Suitability of foramen magnum measurements in sex determination and their clinical significance.

BACKGROUND: The foramen magnum provides a transition between fossa cranii posterior and canalis vertebralis. Medulla oblongata, arteria vertebralis and nervus accessorius spinal part pass through the foramen magnum. In this study, we aimed to make the morphometric measurements of the foramen magnum on computed tomography (CT) and to determine the feasibility of sex determination based on these measurements. Besides sex determination, from a clinical aspect, it is important to know the measurements of the foramen magnum in the normal population in terms of diseases characterised by displacement of the posterior fossa structures through foramen magnum to upper cervical spinal canal such as Chiari malformations and syringomyelia. MATERIALS AND METHODS: All the data for our study was obtained retrospectively from 100 patients (50 males, 50 females) who had a CT scan of the head and neck region in Adnan Menderes University Hospital, Department of Radiology. To examine the foramen magnum in each and every occipital bone, we measured the foramen magnum's anteroposterior diameter, transverse diameter, the area of the foramen magnum and its circumference. RESULTS: We found that men have a higher average value than women in our study. According to Student's t-test results; in all measured parameters, there is significant difference between the genders (p < 0.05). When multivariate discriminant function test is performed for all four measurements, the discrimination rate is 64% for all women, 70% for all men and 67% for both genders. CONCLUSIONS: As a result of our study, the metric data we obtained will be useful in cases where the skeletons' sex could not be determined by any other methods. We believe that, our study may be useful for other studies in determining of sex from foramen magnum. Our measurements could give some information of the normal ranges of the foramen magnum in normal population, so that this can contribute to the diagnosis process of some diseases by imaging. (Folia Morphol 2018; 77, 1: 99-104).

An integrated radiopathomics machine learning model to predict pathological response to preoperative chemotherapy in gastric cancer.

RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of patients with gastric cancer (GC). METHODS: We retrospectively enrolled 151 patients diagnosed with GC who underwent preoperative chemotherapy and surgical resection at the Affiliated Hospital of Qingdao University between January 2015 and June 2023. Both pretreatment-enhanced computer technology images and whole slide images of pathological hematoxylin and eosin-stained sections were available for each patient. The image features were extracted and used to construct an ensemble radiopathomics machine learning model. In addition, a nomogram was developed by combining the imaging features and clinical characteristics. RESULTS: In total, 962 radiomics and 999 pathomics signatures were extracted from 106 patients in the training cohort. A fusion radiopathomics model was constructed using 13 radiomics and 5 pathomics signatures. The fusion model showed favorable performance compared to single-omics models, with an area under the curve (AUC) of 0.789 in the validation cohort. Moreover, a combined radiopathomics nomogram (RPN) was developed based on radiopathomics features and the Borrmann type, which is a classification method for advanced GC according to tumor growth pattern and gross morphology. The RPN showed superior predictive performance in the training (AUC 0.880) and validation cohorts (AUC 0.797). The decision curve analysis showed that RPN could provide favorable clinical benefits to patients with GC. CONCLUSIONS: RPN was able to predict the pathological response to preoperative chemotherapy with high accuracy, and therefore provides a novel tool for personalized treatment of GC.

Construction and validation of a clinical differentiation model between peripheral lung cancer and solitary pulmonary tuberculosis.

OBJECTIVE: To establish and validate a clinical model for differentiating peripheral lung cancer (PLC) from solitary pulmonary tuberculosis (SP-TB) based on clinical and imaging features. MATERIALS AND METHODS: Retrospectively, 183 patients (100 PLC, 83 SP-TB) in our hospital were randomly divided into a training group and an internal validation group (ratio 7:3), and 100 patients (50 PLC, 50 SP-TB) in Sichuan Provincial People's Hospital were identified as an external validation group. The collected qualitative and quantitative variables were used to determine the independent feature variables for distinguishing between PLC and SP-TB through univariate logistic regression, multivariate logistic regression. Then, traditional logistic regression models and machine learning algorithm models (decision tree, random forest, xgboost, support vector machine, k-nearest neighbors, light gradient boosting machine) were established using the independent feature variables. The model with the highest AUC value in the internal validation group was used for subsequent analysis. The receiver operating characteristic curve (ROC), calibration curve, and decision curves analysis (DCA) were used to assess the model's discrimination, calibration, and clinical usefulness. RESULT: Age, smoking history, maximum diameter of lesion, lobulation, spiculation, calcification, and vascular convergence sign were independent characteristic variables to differentiate PLC from SP-TB. The logistic regression model had the highest AUC value of 0.878 for the internal validation group, based on which a quantitative visualization nomogram was constructed to discriminate the two diseases. The area under the ROC curve (AUC) of the model in the training, internal validation, and external validation groups were 0.915 (95 % CI: 0.866-0.965), 0.878 (95 % CI: 0.784-0.971), and 0.912 (95 % CI: 0.855-0.969), respectively, and the calibration curves fitted well. Decision curves analysis (DCA) confirmed the good clinical benefit of the model. CONCLUSION: The model constructed based on clinical and imaging features can accurately differentiate between PLC and SP-TB, providing potential value for developing reasonable clinical plans.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography.

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders.

This study aimed to assess the feasibility of diagnosing secondary pulmonary fungal infections (PFIs) in patients with hematological malignancies (HM) using computerized tomography (CT) imaging and a support vector machine (SVM) algorithm. A total of 100 patients with HM complicated by secondary PFI underwent CT scans, and they were included in the training group. Concurrently, 80 patients with the same underlying disease who were treated at our institution were included in the test group. The types of pathogens among different PFI patients and the CT imaging features were compared. Radiomic features were extracted from the CT imaging data of patients, and a diagnostic SVM model was constructed by integrating these features with clinical characteristics. Aspergillus was the most common pathogen responsible for PFIs, followed by Candida, Pneumocystis jirovecii, Mucor, and Cryptococcus, in descending order of occurrence. Patients typically exhibited bilateral diffuse lung lesions. Within the SVM algorithm model, six radiomic features, namely the square root of the inverse covariance of the gray-level co-occurrence matrix (square root IV), the square root of the inverse covariance of the gray-level co-occurrence matrix, and small dependency low gray-level emphasis, significantly influenced the diagnosis of secondary PFIs in patients with HM. The area under the curve values for the training and test sets were 0.902 and 0.891, respectively. Therefore, CT images based on the SVM algorithm demonstrated robust predictive capability in diagnosing secondary PFIs in conjunction with HM.

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules.

Background: Invasive puncture biopsy is currently the main method of identifying benign and malignant pulmonary nodules (PNs). This study aimed to investigate the application effect of chest computed tomography (CT) images, tumor markers (TMs), and metabolomics in the identification of benign and malignant PNs (MPNs). Methods: A total of 110 patients with PNs who were hospitalized in Dongtai Hospital of Traditional Chinese Medicine from March 2021 to March 2022 were selected as the study cohort. A retrospective analysis study of chest CT imaging, serum TMs testing, and plasma fatty acid (FA) metabolomics was performed on all participants. Results: According to the pathological results, participants were divided into a MPN group (n=72) and a benign PN (BPN) group (n=38). The morphological signs of CT images, the levels and positive rate of serum TMs, and the plasma FA indicator were compared between groups. There were significant differences between the MPN group and the BPN group in the CT morphological signs, including location of PN and the number of patients with or without lobulation sign, spicule sign, and vessel convergence sign (P<0.05). Serum carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC-Ag) were not significantly different between the 2 groups. The serum contents of CEA and CYFRA 21-1 in the MPN group were remarkably higher than those in the BPN group (P<0.05). The plasma levels of palmitic acid, total omega-3 polyunsaturated FA (W3), nervonic acid, stearic acid, docosatetraenoic acid, linolenic acid, eicosapentaenoic acid, total saturated FA, and total FA were much higher in the MPN group than the BPN group (P<0.05). Conclusions: In conclusion, chest CT images and TMs, combined with metabolomics, has a good application effect in the diagnosis of BPNs and MPNs, and is worthy of further promotion.

Application of bone alkaline phosphatase and 25-oxhydryl-vitamin D in diagnosis and prediction of osteoporotic vertebral compression fractures.

BACKGROUND: Osteoporosis is a bone metabolic disease that usually causes fracture. The improvement of the clinical diagnostic efficiency of osteoporosis is of great significance for the prevention of fracture. The predictive and diagnostic values of bone alkaline phosphatase (B-ALP) and 25-oxhydryl-vitamin D (25-OH-VD) for osteoporotic vertebral compression fractures (OVCFs) were evaluated. METHODS: 110 OVCFs patients undergoing percutaneous vertebroplasty were included as subjects and their spinal computed tomography (CT) images were collected. After that, deep convolutional neural network model was employed for intelligent fracture recognition. Next, the patients were randomly enrolled into Ctrl group (65 cases receiving postoperative routine treatment) and VD2 group (65 cases injected with vitamin D2 into muscle after the surgery). In addition, 100 healthy people who participated in physical examination were included in Normal group. The differences in Oswestry dysfunction indexes (ODI), imaging parameters, B-ALP and 25-OH-VD expressions, and quality of life (QOL) scores of patients among the three groups were compared. The values of B-ALP and 25-OH-VD in predicting and diagnosing OVCFs and their correlation with bone density were analyzed. RESULTS: It was demonstrated that computer intelligent medical image technique was more efficient in fracture CT recognition than artificial recognition. In contrast to those among patients in Normal group, B-ALP rose while 25-OH-VD declined among patients in Ctrl and VD2 groups (P < 0.05). Versus those among patients in Ctrl group, ODI, Cobb angle, and B-ALP reduced, while bone density, the height ratio of the injured vertebrae, 25-OH-VD, and QOL score increased among patients in VD2 group after the treatment (P < 0.05). The critical values, accuracy, and areas under the curve (AUC) of the diagnosis of OVCFs by B-ALP and 25-OH-VD amounted to 87.8 µg/L versus 30.3 nmol/L, 86.7% versus 83.3%, and 0.86 versus 0.82, respectively. B-ALP was apparently negatively correlated with bone density (r = - 0.602, P < 0.05), while 25-OH-VD was remarkably positively correlated with bone density (r = 0.576, P < 0.05). CONCLUSION: To sum up, deep learning-based computer CT image intelligent detection technique could improve the diagnostic efficacy of fracture. B-ALP rose while 25-OH-VD declined among patients with OVCFs and OVCFs could be predicted and diagnosed based on B-ALP and 25-OH-VD. Postoperative intramuscular injection of VD2 could effectively improve the therapeutic effect on patients with OVCFs and QOL.

Biodynamic responses of adolescent idiopathic scoliosis exposed to vibration.

Patients with adolescent idiopathic scoliosis suffer severe health issues. The unclear dynamic biomechanical characteristics of scoliosis were needed to be explored to improve the prevention and treatment in clinics. Validated 3D finite element (FE) models of thoracolumbosacral spine (T1-S1) both with and without scoliosis were developed from computed tomography (CT) images. Modal and harmonic analyses were performed to investigate the biomechanical responses of the spinal models to vibration. Resonant frequencies of the scoliotic model were lower than those of the model without scoliosis. Peak amplitudes occurred at vibrational frequencies close to the modal resonant frequencies, which caused the deformed thoracic segment in scoliosis suffered the maximum amplitude. The stresses on vertebrae and intervertebral discs in the scoliotic model derived from vibrations were significantly larger than those in the non-scoliosis model, and heterogeneously concentrated on the scoliotic thoracic segment. In conclusion, the scoliotic spine in the patients with Lenke 1BN scoliosis is more prone to injuries than the non-scoliotic spine while vibrating. Scoliotic thoracic segments in patients with Lenke 1BN scoliosis were the more vulnerable and sensitive component of the T1-S1 spine to vibration than lumbar spines. This study suggested that vibration would impair the scoliotic spines, and patients with Lenke 1BN scoliosis should avoid exposure to vibration, especially the low-frequency vibration.

DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data.

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. Code is available at https://github.com/JiayuanWang-JW/DC-cycleGAN.

Role of epicardial adipose tissue in human atrial fibrillation.

A recent meta-analysis among which four reports were conducted in Japan demonstrated that epicardial adipose tissue (EAT) is closely associated with an increased risk of atrial fibrillation (AF) recurrence after catheter ablation. We previously investigated the role of EAT in AF in humans. Left atrial (LA) appendage samples were obtained from AF patients during cardiovascular surgery. Histologically, the severity of fibrotic EAT remodeling was associated with LA myocardial fibrosis. Total collagen in the LA myocardium (i.e., LA myocardial fibrosis) was positively correlated with proinflammatory and profibrotic cytokines/chemokines, including interleukin-6, monocyte chemoattractant protein-1, and tumor necrosis factor-α, in EAT. Human peri-LA EAT and abdominal subcutaneous adipose tissue (SAT) were obtained by autopsy. EAT- or SAT-derived conditioned medium was applied to the rat LA epicardial surface using an organo-culture system. EAT-conditioned medium induced atrial fibrosis in organo-cultured rat atrium. The profibrotic effect of EAT was greater than that of SAT. The fibrotic area of the organo-cultured rat atrium treated with EAT from patients with AF was greater than in patients without AF. Treatment with human recombinant angiopoietin-like protein 2 (Angptl2) induced fibrosis in organo-cultured rat atrium, which was suppressed by concomitant treatment with anti-Angptl2 antibody. Finally, we attempted to detect fibrotic EAT remodeling on computed tomography (CT) images, which demonstrated that the percent change in EAT fat attenuation was positively correlated with EAT fibrosis. Based on these findings, we conclude that the percent change in EAT fat attenuation determined using CT non-invasively detects EAT remodeling.

Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection.

This work aimed to explore the diagnostic value of a deep convolutional neural network (CNN) combined with computed tomography (CT) images in patients with severe pneumonia complicated with pulmonary infection. A total of 120 patients with severe pneumonia complicated by pulmonary infection admitted to the hospital were selected as research subjects and underwent CT imaging scans. The empty convolution (EC) and U-net phase were combined to construct an EC-U-net, which was applied to process the CT images. The results showed that the learning rate of the EC-U-net model decreased substantially with increasing training times until it stabilized and reached zero after 40 training times. The segmentation result of the EC-U-net model for the CT image was very similar to that of the mask image, except for some deviations in edge segmentation. The EC-U-net model exhibited a significantly smaller cross-entropy loss function (CELF) and a higher Dice coefficient than the CNN algorithm. The diagnostic accuracy of CT images based on the EC-U-net model for severe pneumonia complicated with pulmonary infection was substantially higher than that of CT images alone, while the false negative rate (FNR) and false positive rate (FPR) were substantially lower (P < 0.05). Moreover, the true positive rates (TPRs) of CT images based on the EC-U-net model for patchy high-density shadows, diffuse ground glass density shadows, pleural effusion, and lung consolidation were obviously higher than those of the original CT images (P < 0.05). In short, the EC-U-net model was superior to the traditional algorithm regarding the overall performance of CT image segmentation, which can be clinically applied. CT images based on the EC-U-net model can clearly display pulmonary infection lesions, improve the clinical diagnosis of severe pneumonia complicated with pulmonary infection, and help to screen early pulmonary infection and carry out symptomatic treatment.