AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics.

This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making. Methods: We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test. Results: The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p < 0.05). Conclusions: The DenseNet-264 model significantly outperforms the radiomics model in predicting bone metastasis in lung cancer patients. The early and accurate prediction provided by the deep learning model can facilitate timely interventions and personalized treatment planning, potentially improving patient outcomes. Future studies should focus on validating these findings in larger, multi-center cohorts and integrating clinical data to further enhance predictive accuracy.

Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm.

Background and objective: Bone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone tumor image segmentation algorithm based on improved full convolutional neural network which consists fully convolutional neural network (FCNN-4s) and conditional random field (CRF). Methodology: The improved fully convolutional neural network (FCNN-4s) was used to perform coarse segmentation on preprocessed images. Batch normalization layers were added after each convolutional layer to accelerate the convergence speed of network training and improve the accuracy of the trained model. Then, a fully connected conditional random field (CRF) was fused to refine the bone tumor boundary in the coarse segmentation results, achieving the fine segmentation effect. Results: The experimental results show that compared with the traditional convolutional neural network bone tumor image segmentation algorithm, the algorithm has a great improvement in segmentation accuracy and stability, the average Dice can reach 91.56%, the real-time performance is better. Conclusion: Compared with the traditional convolutional neural network segmentation algorithm, the algorithm in this paper has a more refined structure, which can effectively solve the problem of over-segmentation and under-segmentation of bone tumors. The segmentation prediction has better real-time performance, strong stability, and can achieve higher segmentation accuracy.

Deep learning-based image segmentation model using an MRI-based convolutional neural network for physiological evaluation of the heart.

Background and Objective: Cardiovascular disease is a high-fatality health issue. Accurate measurement of cardiovascular function depends on precise segmentation of physiological structure and accurate evaluation of functional parameters. Structural segmentation of heart images and calculation of the volume of different ventricular activity cycles form the basis for quantitative analysis of physiological function and can provide the necessary support for clinical physiological diagnosis, as well as the analysis of various cardiac diseases. Therefore, it is important to develop an efficient heart segmentation algorithm. Methods: A total of 275 nuclear magnetic resonance imaging (MRI) heart scans were collected, analyzed, and preprocessed from Huaqiao University Affiliated Strait Hospital, and the data were used in our improved deep learning model, which was designed based on the U-net network. The training set included 80% of the images, and the remaining 20% was the test set. Based on five time phases from end-diastole (ED) to end-systole (ES), the segmentation findings showed that it is possible to achieve improved segmentation accuracy and computational complexity by segmenting the left ventricle (LV), right ventricle (RV), and myocardium (myo). Results: We improved the Dice index of the LV to 0.965 and 0.921, and the Hausdorff index decreased to 5.4 and 6.9 in the ED and ES phases, respectively; RV Dice increased to 0.938 and 0.860, and the Hausdorff index decreased to 11.7 and 12.6 in the ED and ES, respectively; myo Dice increased to 0.889 and 0.901, and the Hausdorff index decreased to 8.3 and 9.2 in the ED and ES, respectively. Conclusion: The model obtained in the final experiment provided more accurate segmentation of the left and right ventricles, as well as the myocardium, from cardiac MRI. The data from this model facilitate the prediction of cardiovascular disease in real-time, thereby providing potential clinical utility.

Multi-domain medical image translation generation for lung image classification based on generative adversarial networks.

OBJECTIVE: Lung image classification-assisted diagnosis has a large application market. Aiming at the problems of poor attention to existing translation models, the insufficient ability of key transfer and generation, insufficient quality of generated images, and lack of detailed features, this paper conducts research on lung medical image translation and lung image classification based on generative adversarial networks. METHODS: This paper proposes a medical image multi-domain translation algorithm MI-GAN based on the key migration branch. After the actual analysis of the imbalanced medical image data, the key target domain images are selected, the key migration branch is established, and a single generator is used to complete the medical image multi-domain translation. The conversion between domains ensures the attention performance of the medical image multi-domain translation model and the quality of the synthesized images. At the same time, a lung image classification model based on synthetic image data augmentation is proposed. The synthetic lung CT medical images and the original real medical images are used as the training set together to study the performance of the auxiliary diagnosis model in the classification of normal healthy subjects, and also of the mild and severe COVID-19 patients. RESULTS: Based on the chest CT image dataset, MI-GAN has completed the mutual conversion and generation of normal lung images without disease, viral pneumonia and Mild COVID-19 images. The synthetic images GAN-test and GAN-train indicators reached, respectively 92.188% and 85.069%, compared with other generative models in terms of authenticity and diversity, there is a considerable improvement. The accuracy rate of pneumonia diagnosis of the lung image classification model is 93.85%, which is 3.1% higher than that of the diagnosis model trained only with real images; the sensitivity of disease diagnosis is 96.69%, a relative improvement of 7.1%. 1%, the specificity was 89.70%; the area under the ROC curve (AUC) increased from 94.00% to 96.17%. CONCLUSION: In this paper, a multi-domain translation model of medical images based on the key transfer branch is proposed, which enables the translation network to have key transfer and attention performance. It is verified on lung CT images and achieved good results. The required medical images are synthesized by the above medical image translation model, and the effectiveness of the synthesized images on the lung image classification network is verified experimentally.

Na3YB8O15: a new rare-earth borate with an infinite one-dimensional [B8O15]∞ chain and a short ultraviolet cutoff edge.

A new rare-earth borate Na3YB8O15 was fabricated by the flux method. The compound crystallizes in the P1̄ group, and shows a three-dimensional network built by [B8O17] fundamental building blocks (FBBs) with [NaO6], [NaO7], and [YO7] polyhedra. The [B8O17] FBBs are composed of five planar BO3 and three BO4 tetrahedra and connected via corner-sharing to construct a unique one-dimensional (1D) [B8O15]∞ infinity chain that is observed for the first time in the Na2O-Y2O3-B2O3 system compound, enriching the structural diversity of rare-earth borate. The title compound exhibits a short ultraviolet (UV) cutoff edge of ∼195 nm and a wide experimental band gap of 5.50 eV. DFT calculations indicate that Na3YB8O15 is a direct band gap compound with a calculated GGA band gap of 5.14 eV, and the band gap is mainly determined by B 2p and O 2p orbitals. The results update the phase diagram of Na2O-Y2O3-B2O3 and enrich the diversity of rare-earth metal borates.

Application of cascaded GAN based on CT scan in the diagnosis of aortic dissection.

PURPOSE: Currently, Computed Tomography Angiography (CTA) is the most commonly used clinical method for the diagnosis of aortic dissection, which is much better than plain CT. However, CTA examination has some disadvantages such as time-consuming image processing, complicated procedure and injection of developer. CT plain scanning is widely used in the early diagnosis of arterial dissection because of its convenience, speed and popularity. In order not to delay the optimal diagnosis and treatment time of patients, we use deep learning technology and network model to synthesize plain CT images into CTA images. Patients can be timely professional related departments of clinical diagnosis and treatment, and reduce the rate of missed diagnosis. In this paper, we propose a CTA image synthesis technique for cardiac aortic dissection based on the cascaded generative adjunctive network model. METHOD: Firstly, we registered CT images, and then used nnU-Net segmentation network model to obtain CT and CTA paired images containing only the aorta. Then we proposed a CTA image synthesis method for aortic dissection based on cascaded generative adversarial. The core idea is to build a cascade generator and double discriminator network based on DCT channel attention mechanism to further enhance the synthesis effect of CTA. RESULTS: The model is trained and tested on CT plain scan and CTA image data set of aortic dissection. The results show that the proposed model achieves good results in CTA image synthesis. In the CT data set, the nnU-Net model improves 8.63% and reduces 10.87mm errors in the key index DSC and HD, respectively, compared with the benchmark model U-Net. In CTA data set, nnU-Net model improves 10.27% and reduces 6.56mm error in key index DSC and HD, respectively, compared with benchmark model U-Net. In the synthesis task, the cascaded generative adm network is superior to Pix2pix and Pix2pixHD network models in both PSNR and SSIM, which proves that our proposed model has significant advantages. CONCLUSION: This study provides new possibilities for CTA image synthesis of aortic dissection, and improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the diagnosis of aortic dissection.

Femoral image segmentation based on two-stage convolutional network using 3D-DMFNet and 3D-ResUnet.

OBJECTIVE: The femur is a typical human long bone with an irregular spatial structure. Femoral fractures are the most common occurrence in middle-aged and older adults. The structure of human bone tissue is very complex, and there are significant differences between individuals. Segmenting bone tissue is a challenging task and of great practical significance. METHODS: Our research is based on segmenting and the three-dimensional reconstruction of femoral images using X-ray imaging. The currently commonly used two-dimensional fully convolutional network Unet has the problem of ignoring spatial position information and losing too much feature information. The commonly used three-dimensional fully convolutional network 3D Unet has the problem of ignoring spatial position information and losing too much feature information. For the problem of many model parameters, we proposes a two-stage network segmentation model composed of 3D-DMFNet and 3D-ResUnet networks and trains the network in stages to segment the femur. One stage is used to detect the coarse segmentation of the femur range, and one stage is used for the fine segmentation of the femur so that the training speed is fast and the segmentation accuracy is moderate, which is suitable for detecting the femur range. RESULTS: The experimental dataset used in this paper is from The Second Affiliated Hospital of Fujian Medical University, which consists of 30 sets of femur X-ray images. The experiment compares the accuracy and loss value of Unet and the two-stage convolutional network. The image shows that the two-stage convolutional network has higher accuracy. At the same time, this paper shows the effect of the two-stage coarse segmentation and fine segmentation of medical images. Subsequently, this paper applies the model to practice and obtains the model's Dice, Sensitivity, Specificity and Pixel Accuracy values. After comparative analysis, the experimental results show that the two-stage network segmentation model composed of 3D-DMFNet and 3D-ResUnet network designed in this paper has higher accuracy, intuitiveness, and more application value than traditional image segmentation algorithms. CONCLUSION: With the continuous application of X-ray images in clinical diagnosis using femoral images, the method in this paper is expected to become a diagnostic tool that can effectively improve the accuracy and loss of femoral image segmentation and the three-dimensional reconstruction.

Classification of lungs infected COVID-19 images based on inception-ResNet.

OBJECTIVE: Nowadays, COVID-19 is spreading rapidly worldwide, and seriously threatening lives . From the perspective of security and economy, the effective control of COVID-19 has a profound impact on the entire society. An effective strategy is to diagnose earlier to prevent the spread of the disease and prompt treatment of severe cases to improve the chance of survival. METHODS: The method of this paper is as follows: Firstly, the collected data set is processed by chest film image processing, and the bone removal process is carried out in the rib subtraction module. Then, the set preprocessing method performed histogram equalization, sharpening, and other preprocessing operations on the chest film. Finally, shallow and high-level feature mapping through the backbone network extracts the processed chest radiographs. We implement the self-attention mechanism in Inception-Resnet, perform the standard classification, and identify chest radiograph diseases through the classifier to realize the auxiliary COVID-19 diagnosis process at the medical level, all in an effort to further enhance the classification performance of the convolutional neural network. Numerous computer simulations demonstrate that the Inception-Resnet convolutional neural network performs CT image categorization and enhancement with greater efficiency and flexibility than conventional segmentation techniques. RESULTS: The experimental COVID-19 CT dataset obtained in this paper is the new data for CT scans and medical imaging of normal, early COVID-19 patients and severe COVID-19 patients from Jinyintan hospital. The experiment plots the relationship between model accuracy, model loss and epoch, using ACC, TPR, SPE, F1 score and G-mean to measure the image maps of patients with and without the disease. Statistical measurement values are obtained by Inception-Resnet are 88.23%, 83.45%, 89.72%, 95.53% and 88.74%. The experimental results show that Inception-Resnet plays a more effective role than other image classification methods in evaluation indicators, and the method has higher robustness, accuracy and intuitiveness. CONCLUSION: With CT images in the clinical diagnosis of COVID-19 images being widely used and the number of applied samples continuously increasing, the method in this paper is expected to become an additional diagnostic tool that can effectively improve the diagnostic accuracy of clinical COVID-19 images.

NH4IO2F2 and (NH4)3(IO2F2)3·H2O: A Series of Ammonium-Containing Fluoroiodates with Wide Band Gaps.

A series of ammonium-containing fluoroiodates, NH4IO2F2 and (NH4)3(IO2F2)3·H2O, with isolated [IO2F2] units have been fabricated by a fluorine-oxygen substitution strategy from NH4IO3. The two compounds crystallize in the orthorhombic system, but in different space groups, noncentrosymmetric Pca21 for NH4IO2F2 and centrosymmetric Pnma for (NH4)3(IO2F2)3·H2O, and show wide band gaps of 4.53 eV for (NH4IO2F2) and 4.55 eV for ((NH4)3(IO2F2)3·H2O). In addition, NH4IO2F2 exhibits a 1.2 × KDP second harmonic generation response, a short ultraviolet cutoff edge in iodates, and a good crystal growth habit. The crystal of NH4IO2F2 with a size of 11 × 5 × 2 mm3 was obtained by the aqueous solution method. The results enrich the structural diversity of iodate and supply a greater understanding of the design of new functional materials based on the fluoroiodates.

Case Report: Tachycardia, Hypoxemia and Shock in a Severely Burned Pediatric Patient.

Background: Severely burned children are at high risk of secondary intraabdominal hypertension and abdominal compartment syndrome (ACS). ACS is a life-threatening condition with high mortality and requires an effective, minimally invasive treatment to improve the prognosis when the condition is refractory to conventional therapy. Case presentation: A 4.5-year-old girl was admitted to our hospital 30 h after a severe burn injury. Her symptoms of burn shock were relieved after fluid resuscitation. However, her bloating was aggravated, and ACS developed on Day 5, manifesting as tachycardia, hypoxemia, shock, and oliguria. Invasive mechanical ventilation, vasopressors, and percutaneous catheter drainage were applied in addition to medical treatments (such as gastrointestinal decompression, diuresis, sedation, and neuromuscular blockade). These treatments did not improve the patient's condition until she received continuous renal replacement therapy. Subsequently, her vital signs and laboratory data improved, which were accompanied by decreased intra-abdominal pressure, and she was discharged after nutrition support, antibiotic therapy, and skin grafting. Conclusion: ACS can occur in severely burned children, leading to rapid deterioration of cardiopulmonary function. Patients who fail to respond to conventional medical management should be considered for continuous renal replacement therapy.

A hybrid approach to product prototype usability testing based on surface EMG images and convolutional neural network classification.

OBJECTIVE: It is common for employees to complain of muscle fatigue when resting in a reclined position in an office chair. To investigate the physical factors that influence resting comfort in a supine position, a newly designed product was used as the basis for creating a prototype experiment and testing its efficacy in use. Subjective questionnaires were combined with surface EMG measurements and deep learning algorithms were used to identify body part comfort to create a hybrid approach to product usability testing. METHODS: To facilitate the use of sEMG-based CNNs in human factors engineering, a subjective user assessment was first conducted using a combination of body mapping and an impact comfort scale to the screen which body parts have a significant impact effect on comfort when using the prototype. A control group (no used) and an experimental group (used) were then created and the body parts with the most significant effects were measured using sEMG methods. After pre-processing the sEMG signal, sMEG feature maps were obtained by mean power frequency (MPF) and linear regression was used to analyze the comforting effect. Finally, a CNN model is constructed and the sMEG feature maps are trained and tested. RESULTS: The results of the experiment showed that the user's subjective assessment showed that 10 body parts had a significant effect on comfort, with the right and left sides of the neck having the highest effect on comfort (4.78). sEMG measurements were then performed on the sternocleidomastoid (SCM) of the left and right neck. Linear analysis of the measurements showed that the control group had higher SCM fatigue than the experimental group, which could also indicate that the experimental group had better comfort. The final CNN model was able to accurately classify the four datasets with an accuracy of 0.99. CONCLUSION: The results of the study show that the method is effective for the study of physical comfort in the supine sitting position and that it can be used to validate the comfort of similar products and to design iterations of the prototype.

Health classification of Meibomian gland images using keratography 5M based on AlexNet model.

PURPOSE: We aim to present effective diagnostics in the field of ophthalmology and improve eye health. The purpose of this study is to examine the capability of health classification of Meibomian gland dysfunction (MGD) using Keratography 5M and AlexNet method. METHOD: A total of 4,609 meibomian gland images were collected from 2,000 patients using Keratography 5M in the hospital. Then, MGD dataset for eyelid gland health recognition was constructed through image pre-processing, labelling, cropping and augmentation. Furthermore, AlexNet network was used to identify the eyelid gland health. The effects of different optimization methods, different learning rates, Dropout methods and different batch sizes on the recognition accuracy were discussed. RESULTS: The results show that the effect of model recognition is the best when the optimized method is Adam, the number of iterations is 150, the learning rate is 0.001, and the batch size is 80, then, the overall test accuracy of health degree is 94.00%. CONCLUSION: Our research provides a reference to the clinical diagnosis or screening of eyelid gland dysfunction. In future implementations, ophthalmologists can implement more advanced learning algorithms to improve the accuracy of diagnosis.

Electrocardiogram Signal Classification in the Diagnosis of Heart Disease Based on RBF Neural Network.

Heart disease is a common disease affecting human health. Electrocardiogram (ECG) classification is the most effective and direct method to detect heart disease, which is helpful to the diagnosis of most heart disease symptoms. At present, most ECG diagnosis depends on the personal judgment of medical staff, which leads to heavy burden and low efficiency of medical staff. Automatic ECG analysis technology will help the work of relevant medical staff. In this paper, we use the MIT-BIH ECG database to extract the QRS features of ECG signals by using the Pan-Tompkins algorithm. After extraction of the samples, K-means clustering is used to screen the samples, and then, RBF neural network is used to analyze the ECG information. The classifier trains the electrical signal features, and the classification accuracy of the final classification model can reach 98.9%. Our experiments show that this method can effectively detect the abnormality of ECG signal and implement it for the diagnosis of heart disease.

Super-resolution reconstruction of pneumocystis carinii pneumonia images based on generative confrontation network.

OBJECTIVE: Pneumocystis carinii pneumonia, also known as pneumocystis carinii pneumonia (PCP), is an interstitial plasma cell pneumonia caused by pneumocystis spp. It is a conditional lung infectious disease. Because the early and correct diagnosis of PCP has a great influence on the prognosis of patients, the image processing of PCP's high-resolution CT (HRCT) is extremely important. Traditional image super-resolution reconstruction algorithms have difficulties in network training and artifacts in generated images. The super-resolution reconstruction algorithm of generative counter-networks can optimize these two problems well. METHODS: In this paper, the texture enhanced super-resolution generative adversarial network (TESRGAN) is based on a generative confrontation network, which mainly includes a generative network and a discriminant network. In order to improve the quality of image reconstruction, TESRGAN improved the structure of the Super-Resolution Generative Adversarial Network (SRGAN) generation network, removed all BN layers in SRGAN, and replaced the ReLU function with the LeakyReLU function as the nonlinear activation function of the network to avoid the disappearance of the gradient. EXPERIMENTAL RESULTS: The TESRGAN algorithm in this paper is compared with the image reconstruction results of Bicubic, SRGAN, Enhanced Deep Super-Resolution network (EDSR), and ESRGAN. Compared with algorithms such as SRGAN and EDSR, our algorithm has clearer texture details and more accurate brightness information without extending the running time. Our reconstruction algorithm can improve the accuracy of image low-frequency information. CONCLUSION: The texture details of the reconstruction result are clearer and the brightness information is more accurate, which is more in line with the requirements of visual sensory evaluation.

Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation.

BACKGROUND AND OBJECTIVE: Atrial septal defect (ASD) is a common congenital heart disease. During embryonic development, abnormal atrial septal development leads to pores between the left and right atria. ASD accounts for the largest proportion of congenital heart disease. Therefore, the design and implementation of an ASD intelligent auxiliary segmentation system based on deep learning segmentation of the atria has very important practical significance, which we aim to achieve in this paper. METHODS: This study proposes a multi-scale dilated convolution module, which is composed of three parallel dilated convolutions with different expansion coefficients. The original FCN network usually adopts bilinear interpolation or deconvolution methods when upsampling, both of which lead to information loss to a certain extent. In order to make up for the loss of information, it is expected that the final segmentation result can be directly connected to the deep features in the cardiac MRI. This study uses a dense upsampling convolution module, and in order to obtain the shallow position information, the original FCN jump connection module is still retained. In this research, a deep convolutional neural network for multi-scale feature extraction is designed through the multi-scale expansion convolution module. At the same time, this paper also implements two traditional machine learning segmentation methods (K-means and Watershed algorithms) and a deep learning algorithm (U-net) for comparison. RESULTS: The intelligent auxiliary segmentation algorithm for atrial images proposed in this framework based on multi-scale expansion convolution and adversarial learning can achieve superior results. Among them, the segmentation algorithm based on multi-scale expansion convolution can extract the associated features of pixels in multiple ranges, and can obtain deeper feature information when using a limited downsampling layer. According to the experimental results of the multi-scale expanded convolutional network on the data set, the Proportion of Greater Contour (PGC) index of the multi-scale expanded convolutional network is 98.78, the value of Average Perpendicular Distance (ADP) is 1.72mm, and the value of Overlapping Dice Metric (ODM) is 0.935, which are higher than other models. CONCLUSION: The experimental results show that compared with other segmentation models, the model based on multi-scale expansion convolution has significantly improved the accuracy of segmentation. Our technique will be able to assist in the segmentation of ASD, evaluation of the extent of the defect and enhance surgical planning via atrial septal occlusion.

Automatic segmentation of cardiac magnetic resonance images based on multi-input fusion network.

PURPOSE: Using computer-assisted means to process a large amount of heart image data in order to speed up the diagnosis efficiency and accuracy of medical doctors has become a research worthy of investigation. METHOD: Based on the U-Net model, this paper proposes a multi-input fusion network (MIFNet) model based on multi-scale input and feature fusion, which automatically extracts and fuses features of different input scales to realize the detection of Cardiac Magnetic Resonance Images (CMRI). The MIFNet model is trained and verified on the public data set, and then compared with the segmentation models, namely the Fully Convolutional Network (FCN) and DeepLab v1. RESULTS: MIFNet model segmentation of CMRI significantly improved the segmentation accuracy, and the Dice value reached 97.238%. Compared with FCN and DeepLab v1, the average Hausdorff distance (HD) was reduced by 16.425%. The capacity parameter of FCN is 124.86% of MIFNet, DeepLab v1 is 103.22% of MIFNet. CONCLUSION: Our proposed MIFNet model reduces the amount of parameters and improves the training speed while ensuring the simultaneous segmentation of overlapping targets. It can help clinicians to more quickly check the patient's CMRI focus area, and thereby improving the efficiency of diagnosis.

Comparative analysis of pulmonary nodules segmentation using multiscale residual U-Net and fuzzy C-means clustering.

BACKGROUND AND OBJECTIVE: Pulmonary nodules have different shapes and uneven density, and some nodules adhere to blood vessels, pleura and other anatomical structures, which increase the difficulty of nodule segmentation. The purpose of this paper is to use multiscale residual U-Net to accurately segment lung nodules with complex geometric shapes, while comparing it with fuzzy C-means clustering and manual segmentation. METHOD: We selected 58 computed tomography (CT) scan images of patients with different lung nodules for image segmentation. This paper proposes an automatic segmentation algorithm for lung nodules based on multiscale residual U-Net. In order to verify the accuracy of the method, we also conducted comparative experiments, while comparing it with fuzzy C-means clustering. RESULTS: Compared with the other two methods, the segmentation of lung nodules based on multiscale residual U-Net has a higher accuracy, with an accuracy rate of 94.57%. This method not only maintains a high accuracy rate, but also shortens the recognition time significantly with a segmentation time of 3.15 s. CONCLUSIONS: The diagnosis method of lung nodules combined with deep learning has a good market prospect and can improve the efficiency of doctors in diagnosing benign and malignant lung nodules.

Management of medical and health big data based on integrated learning-based health care system: A review and comparative analysis.

PURPOSE: We present a Health Care System (HCS) based on integrated learning to achieve high-efficiency and high-precision integration of medical and health big data, and compared it with an internet-based integrated system. METHOD: The method proposed in this paper adopts the Bagging integrated learning method and the Extreme Learning Machine (ELM) prediction model to obtain a high-precision strong learning model. In order to verify the integration efficiency of the system, we compare it with the Internet-based health big data integration system in terms of integration volume, integration efficiency, and storage space capacity. RESULTS: The HCS based on integrated learning relies on the Internet in terms of integration volume, integration efficiency, and storage space capacity. The amount of integration is proportional to the time and the integration time is between 170-450 ms, which is only half of the comparison system; whereby the storage space capacity reaches 8.3×28TB. CONCLUSION: The experimental results show that the integrated learning-based HCS integrates medical and health big data with high integration volume and integration efficiency, and has high space storage capacity and concurrent data processing performance.

Closure of live bird markets leads to the spread of H7N9 influenza in China.

Following the emergence of H7N9 influenza in March 2013, local animal and public health authorities in China have been closing live bird markets as a measure to try to control the H7N9 influenza epidemic. The role of live bird market (LBM) closure on the spread of N7N9 influenza following the closure of LBMs during March to May 2013 (the first wave) and October 2013 to March 2014 (the second wave) is described in this paper. Different provinces implemented closure actions at different times, and intensive media reports on H7N9 in different provinces started at different times. Local broiler prices dropped dramatically in places with outbreaks and more live chickens were transported to other LBMs in neighboring areas without human cases from infected areas when live bird markets were being closed. There were six clusters of human infection from March to May 2013 and October 2013 to March 2014 and there may have been intensive poultry transportation among cluster areas. These findings provide evidence that the closure of LBMs in early waves of H7N9 influenza had resulted in expansion of H7N9 infection to uninfected areas. This suggests that provincial authorities in inland provinces should be alert to the risks of sudden changes in movement patterns for live birds after LBM closure or increased publicity about LBM closure.

Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster.

BACKGROUND: Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs. RESULTS: We have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24% of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not. CONCLUSIONS: In this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.