3DFRINet: A Framework for the Detection and Diagnosis of Fracture Related Infection in Low Extremities Based on 18F-FDG PET/CT 3D Images.

Fracture related infection (FRI) is one of the most devastating complications after fracture surgery in the lower extremities, which can lead to extremely high morbidity and medical costs. Therefore, early comprehensive evaluation and accurate diagnosis of patients are critical for appropriate treatment, prevention of complications, and good prognosis. 18Fluoro-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is one of the most commonly used medical imaging modalities for diagnosing FRI. With the development of deep learning, more neural networks have been proposed and become powerful computer-aided diagnosis tools in medical imaging. Therefore, a fully automated two-stage framework for FRI detection and diagnosis, 3DFRINet (Three Dimension FRI Network), is proposed for 18F-FDG PET/CT 3D imaging. The first stage can effectively extract and fuse the features of both modalities to accurately locate the lesion by the dual-branch design and attention module. The second stage reduces the dimensionality of the image by using the maximum intensity projection, which retains the effective features while reducing the computational effort and achieving excellent diagnostic performance. The diagnostic performance of lesions reached 91.55% accuracy, 0.9331 AUC, and 0.9250 F1 score. 3DFRINet has an advantage over six nuclear medicine experts in each classification metric. The statistical analysis shows that 3DFRINet is equivalent or superior to the primary nuclear medicine physicians and comparable to the senior nuclear medicine physicians. In conclusion, this study first proposed a method based on 18F-FDG PET/CT three-dimensional imaging for FRI location and diagnosis. This method shows superior lesion detection rate and diagnostic efficiency and therefore has good prospects for clinical application.

A novel wavelet-transform-based convolution classification network for cervical lymph node metastasis of papillary thyroid carcinoma in ultrasound images.

Preoperative assessment of cervical lymph nodes metastasis (CLNM) for accurate qualitative and locating diagnosis is important for choosing the best treatment option for patients with papillary thyroid cancer. Non-destructive, non-invasive ultrasound is currently the imaging method of choice for lymph node metastatic assessment. For lymph node characteristics and ultrasound images, this paper proposes a multitasking network framework for diagnosing metastatic lymph nodes in ultrasound images, in which localization module not only provides information on the location of lymph nodes to focus on the peripheral and self regions of lymph nodes, but also provides structural features of lymph nodes for subsequent classification module. In the classification module, we design a novel wavelet-transform-based convolution network. Wavelet transform is introduced into the deep learning convolution module to analyze ultrasound images in both spatial and frequency domains, which effectively enriches the feature information and improves the classification performance of the model without increasing the model parameters. We collected 510 patient data (N = 1376) from Shanghai Sixth People's Hospital regarding ultrasound lymph nodes in the neck, as well as used three publicly available ultrasound datasets, including SCUI2020 (N = 2914), DDTI (N = 480), and BUSI (N = 780). Compared to the optimal two-stage model, our model has improved its accuracy and AUC indexes by 5.83% and 4%, which outperforms the two-stage architectures and also surpasses the latest classification networks.

Predicting 18F-FDG SUVs of metastatic pulmonary nodes from CT images in patients with differentiated thyroid cancer by using a convolutional neural network.

Purpose: The aim of this study was to predict standard uptake values (SUVs) from computed tomography (CT) images of patients with lung metastases from differentiated thyroid cancer (DTC-LM). Methods: We proposed a novel SUVs prediction model using 18-layer Residual Network for generating SUVmax, SUVmean, SUVmin of metastatic pulmonary nodes from CT images of patients with DTC-LM. Nuclear medicine specialists outlined the metastatic pulmonary as primary set. The best model parameters were obtained after five-fold cross-validation on the training and validation set, further evaluated in independent test set. Mean absolute error (MAE), mean squared error (MSE), and mean relative error (MRE) were used to assess the performance of regression task. Specificity, sensitivity, F1 score, positive predictive value, negative predictive value and accuracy were used for classification task. The correlation between predicted and actual SUVs was analyzed. Results: A total of 3407 nodes from 74 patients with DTC-LM were collected in this study. On the independent test set, the average MAE, MSE and MRE was 0.3843, 1.0133, 0.3491 respectively, and the accuracy was 88.26%. Our proposed model achieved high metric scores (MAE=0.3843, MSE=1.0113, MRE=34.91%) compared with other backbones. The predicted SUVmax (R2 = 0.8987), SUVmean (R2 = 0.8346), SUVmin (R2 = 0.7373) were all significantly correlated with actual SUVs. Conclusion: The novel approach proposed in this study provides new ideas for the application of predicting SUVs for metastatic pulmonary nodes in DTC patients.

An artificial intelligence framework for the diagnosis of prosthetic joint infection based on 99mTc-MDP dynamic bone scintigraphy.

OBJECTIVES: Dynamic bone scintigraphy (DBS) is the first widely reliable and simple imaging modality in nuclear medicine that can be used to diagnose prosthetic joint infection (PJI). We aimed to apply artificial intelligence to diagnose PJI in patients after total hip or knee arthroplasty (THA or TKA) based on 99mTc-methylene diphosphonate (99mTc-MDP) DBS. METHODS: A total of 449 patients (255 THAs and 194 TKAs) with a final diagnosis were retrospectively enrolled and analyzed. The dataset was divided into a training and validation set and an independent test set. A customized framework composed of two data preprocessing algorithms and a diagnosis model (dynamic bone scintigraphy effective neural network, DBS-eNet) was compared with mainstream modified classification models and experienced nuclear medicine specialists on corresponding datasets. RESULTS: In the fivefold cross-validation test, diagnostic accuracies of 86.48% for prosthetic knee infection (PKI) and 86.33% for prosthetic hip infection (PHI) were obtained using the proposed framework. On the independent test set, the diagnostic accuracies and AUC values were 87.74% and 0.957 for PKI and 86.36% and 0.906 for PHI, respectively. The customized framework demonstrated better overall diagnostic performance compared to other classification models and showed superiority in diagnosing PKI and consistency in diagnosing PHI compared to specialists. CONCLUSION: The customized framework can be used to effectively and accurately diagnose PJI based on 99mTc-MDP DBS. The excellent diagnostic performance of this method indicates its potential clinical practical value in the future. KEY POINTS: • The proposed framework in the current study achieved high diagnostic performance for prosthetic knee infection (PKI) and prosthetic hip infection (PHI) with AUC values of 0.957 and 0.906, respectively. • The customized framework demonstrated better overall diagnostic performance compared to other classification models. • Compared to experienced nuclear medicine physicians, the customized framework showed superiority in diagnosing PKI and consistency in diagnosing PHI.

A Novel Model of Thyroid Nodule Segmentation for Ultrasound Images.

Ultrasonography is regarded as an effective technique for the detection, diagnosis and monitoring of thyroid nodules. Segmentation of thyroid nodules on ultrasound images is important in clinical practice. However, because in ultrasound images there is an unclear boundary between thyroid nodules and surrounding tissues, the accuracy of segmentation remains a challenge. Although the deep learning model provides an accurate and convenient method for thyroid nodule segmentation, it is unsatisfactory of the existing model in segmenting the margin of thyroid nodules. In this study, we developed boundary attention transformer net (BTNet), a novel segmentation network with a boundary attention mechanism combining the advantages of a convolutional neural network and transformer, which could fuse the features of both long and short ranges. Boundary attention is improved to focus on learning the boundary information, and this module enhances the segmentation ability of the network boundary. For features of different scales, we also incorporate a deep supervision mechanism to blend the outputs of different levels to enhance the segmentation effect. As the BTNet model incorporates the long range-short range connectivity effect and the boundary-regional cooperation capability, our model has excellent segmentation performance in thyroid nodule segmentation. The development of BTNet was based on the data set from Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital and the public data set. BTNet achieved good performance in the segmentation of thyroid nodules with an intersection-over-union of 0.810 and Dice coefficient of 0.892 Moreover, our work revealed great improvement in the boundary metrics; for example, the boundary distance was 7.308, the boundary overlap 0.201 and the boundary Dice 0.194, all with p values <0.05.

A Deep Learning Method Approach for Sleep Stage Classification with EEG Spectrogram.

The classification of sleep stages is an important process. However, this process is time-consuming, subjective, and error-prone. Many automated classification methods use electroencephalogram (EEG) signals for classification. These methods do not classify well enough and perform poorly in the N1 due to unbalanced data. In this paper, we propose a sleep stage classification method using EEG spectrogram. We have designed a deep learning model called EEGSNet based on multi-layer convolutional neural networks (CNNs) to extract time and frequency features from the EEG spectrogram, and two-layer bi-directional long short-term memory networks (Bi-LSTMs) to learn the transition rules between features from adjacent epochs and to perform the classification of sleep stages. In addition, to improve the generalization ability of the model, we have used Gaussian error linear units (GELUs) as the activation function of CNN. The proposed method was evaluated by four public databases, the Sleep-EDFX-8, Sleep-EDFX-20, Sleep-EDFX-78, and SHHS. The accuracy of the method is 94.17%, 86.82%, 83.02% and 85.12%, respectively, for the four datasets, the MF1 is 87.78%, 81.57%, 77.26% and 78.54%, respectively, and the Kappa is 0.91, 0.82, 0.77 and 0.79, respectively. In addition, our proposed method achieved better classification results on N1, with an F1-score of 70.16%, 52.41%, 50.03% and 47.26% for the four datasets.

MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification.

The outbreak of COVID-19 has caused a severe shortage of healthcare resources. Ground Glass Opacity (GGO) and consolidation of chest CT scans have been an essential basis for imaging diagnosis since 2020. The similarity of imaging features between COVID-19 and other pneumonia makes it challenging to distinguish between them and affects radiologists' diagnosis. Recently, deep learning in COVID-19 has been mainly divided into disease classification and lesion segmentation, yet little work has focused on the feature correlation between the two tasks. To address these issues, in this study, we propose MultiR-Net, a 3D deep learning model for combined COVID-19 classification and lesion segmentation, to achieve real-time and interpretable COVID-19 chest CT diagnosis. Precisely, the proposed network consists of two subnets: a multi-scale feature fusion UNet-like subnet for lesion segmentation and a classification subnet for disease diagnosis. The features between the two subnets are fused by the reverse attention mechanism and the iterable training strategy. Meanwhile, we proposed a loss function to enhance the interaction between the two subnets. Individual metrics can not wholly reflect network effectiveness. Thus we quantify the segmentation results with various evaluation metrics such as average surface distance, volume Dice, and test on the dataset. We employ a dataset containing 275 3D CT scans for classifying COVID-19, Community-acquired Pneumonia (CAP), and healthy people and segmented lesions in pneumonia patients. We split the dataset into 70% and 30% for training and testing. Extensive experiments showed that our multi-task model framework obtained an average recall of 93.323%, an average precision of 94.005% on the classification test set, and a 69.95% Volume Dice score on the segmentation test set of our dataset.

Visibility characteristics and the impacts of air pollutants and meteorological conditions over Shanghai, China.

In China, visibility condition has become an important issue that concerns both society and the scientific community. In order to study visibility characteristics and its influencing factors, visibility data, air pollutants, and meteorological data during the year 2013 were obtained over Shanghai. The temporal variation of atmospheric visibility was analyzed. The mean value of daily visibility of Shanghai was 19.1 km. Visibility exhibited an obvious seasonal cycle. The maximum and minimum visibility occurred in September and December with the values of 27.5 and 7.7 km, respectively. The relationships between the visibility and air pollutant data were calculated. The visibility had negative correlation with NO2, CO, PM2.5, PM10, and SO2 and weak positive correlation with O3. Meteorological data were clustered into four groups to reveal the joint contribution of meteorological variables to the daily average visibility. Usually, under the meteorological condition of high temperature and wind speed, the visibility of Shanghai reached about 25 km, while visibility decreased to 16 km under the weather type of low wind speed and temperature and high relative humid. Principle component analysis was also applied to identify the main cause of visibility variance. The results showed that the low visibility over Shanghai was mainly due to the high air pollution concentrations associated with low wind speed, which explained the total variance of 44.99 %. These results provide new knowledge for better understanding the variations of visibility and have direct implications to supply sound policy on visibility improvement in Shanghai.

[Determination of trace elements in traditional Chinese medicine from Changbai Mountain by ICP-MS].

The effects of different digestives for the fritillaria and atractylodes were compared. Many trace elements in the planted and wild fritillaria and atractylodes were determined by ICP-MS The results show that the RSD and recovery are better if the planted and wild fritillaria and atractylodes were digested with HNO3-H2O2. Among the many elements determined from the fritillaria and atractylodes, Cu, Zn, Fe, Mg and Mn are the dominant chemicals. The content of Fe was higher in the wild fritillaria and atractylodes than that in the planted fritillaria and atractylodes, while the contents of heavy metal Pb and Cd were lower in the wild fritillaria and atractylodes than those in the planted fritillaria and atractylodes. The wild fritillaria and atractylodes contain Co, which was not determined in the planted fritillaria and atractylodes. The experimental results showed that the detection limits were lower than 0.086 ng x g(-1) with low RSD(n = 7, 4.85%) for most metal chemicals determined, and the standard recoveries (n = 7) ranged from 96.8 to 103.4%.