A novel deep learning method to segment parathyroid glands on intraoperative videos of thyroid surgery.

Introduction: The utilization of artificial intelligence (AI) augments intraoperative safety and surgical training. The recognition of parathyroid glands (PGs) is difficult for inexperienced surgeons. The aim of this study was to find out whether deep learning could be used to auxiliary identification of PGs on intraoperative videos in patients undergoing thyroid surgery. Methods: In this retrospective study, 50 patients undergoing thyroid surgery between 2021 and 2023 were randomly assigned (7:3 ratio) to a training cohort (n = 35) and a validation cohort (n = 15). The combined datasets included 98 videos with 9,944 annotated frames. An independent test cohort included 15 videos (1,500 frames) from an additional 15 patients. We developed a deep-learning model Video-Trans-U-HRNet to segment parathyroid glands in surgical videos, comparing it with three advanced medical AI methods on the internal validation cohort. Additionally, we assessed its performance against four surgeons (2 senior surgeons and 2 junior surgeons) on the independent test cohort, calculating precision and recall metrics for the model. Results: Our model demonstrated superior performance compared to other AI models on the internal validation cohort. The DICE and accuracy achieved by our model were 0.760 and 74.7% respectively, surpassing Video-TransUnet (0.710, 70.1%), Video-SwinUnet (0.754, 73.6%), and TransUnet (0.705, 69.4%). For the external test, our method got 89.5% precision 77.3% recall and 70.8% accuracy. In the statistical analysis, our model demonstrated results comparable to those of senior surgeons (senior surgeon 1: χ2 = 0.989, p = 0.320; senior surgeon 2: χ2 = 1.373, p = 0.241) and outperformed 2 junior surgeons (junior surgeon 1: χ2 = 3.889, p = 0.048; junior surgeon 2: χ2 = 4.763, p = 0.029). Discussion: We introduce an innovative intraoperative video method for identifying PGs, highlighting the potential advancements of AI in the surgical domain. The segmentation method employed for parathyroid glands in intraoperative videos offer surgeons supplementary guidance in locating real PGs. The method developed may have utility in facilitating training and decreasing the learning curve associated with the use of this technology.

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.

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.

INTRODUCTION: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs. METHODS: Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model. RESULTS: Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05). CONCLUSIONS: This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis.

Over the past 35 years, studies conducted worldwide have revealed a threefold increase in the incidence of thyroid cancer. Strain elastography is a new imaging technique to identify benign and malignant thyroid nodules due to its sensitivity to tissue stiffness. However, there are certain limitations of this technique, particularly in terms of standardization of the compression process, evaluation of results and several assumptions used in commercial strain elastography modes for the purpose of simplifying imaging analysis. In this work, we propose a novel conditional generative adversarial network (TSE-GAN) for automatically generating thyroid strain elastograms, which adopts a global-to-local architecture to improve the ability of extracting multi-scale features and develops an adaptive deformable U-net structure in the sub-generator to apply effective deformation. Furthermore, we introduce a Lab-based loss function to induce the networks to generate realistic thyroid elastograms that conform to the probability distribution of the target domain. Qualitative and quantitative assessments are conducted on a clinical dataset provided by Shanghai Sixth People's Hospital. Experimental results demonstrate that thyroid elastograms generated by the proposed TSE-GAN outperform state-of-the-art image translation methods in meeting the needs of clinical diagnostic applications and providing practical value.

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.

Automated classification of cervical lymph-node-level from ultrasound using Depthwise Separable Convolutional Swin Transformer.

There are few studies on cervical ultrasound lymph-node-level classification which is very important for qualitative diagnosis and surgical treatment of diseases. Currently, ultrasound examination relies on the subjective experience of physicians to judge the level of the cervical lymph nodes, which is easily misclassified. Unlike other automated diagnostic tasks, lymph-node-level classification needs to focus on global structural information. Besides, there is a large range of sternocleidomastoid muscles in levels II, III and IV, which leads to small inter-class differences in these levels, so it also needs to focus on key local areas to extract strong distinguishable features. In this paper, we propose the Depthwise Separable Convolutional Swin Transformer, introducing the deepwise separable convolution branch into the self-attention mechanism to capture discriminative local features. Meanwhile, to address the problem of data imbalance, a new loss function is proposed to improve the performance of the classification network. In addition, for the ultrasound data collected by different devices, low contrast and blurring problems of ultrasound imaging, a unified pre-processing algorithm is designed. The model was validated on 1146 cases of cervical ultrasound lymph node collected from the Sixth People's Hospital of Shanghai. The average accuracy precision, sensitivity, specificity, and F1 value of the model for the valid dataset after five-fold cross-validation were 80.65%, 80.68%, 78.73%, 95.99% and 79.42%, respectively. It has been verified by visualization methods that the Region of Interest (ROI) of the model is similar or consistent with the observed region of the experts.

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.

AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network.

PROBLEM: Ultrasonography is recommended as the first choice for evaluation of thyroid nodules, however, conventional ultrasound features may not be able to adequately predict malignancy. Ultrasound elastography, adjunct to conventional B-mode ultrasound, can effectively improve the diagnostic accuracy of thyroid nodules. However, this technology requires professional elastography equipment and experienced physicians. AIM: in the field of computational medicine, Generative Adversarial Networks (GANs) were proven to be a powerful tool for generating high-quality images. This work therefore utilizes GANs to generate ultrasound elastography images. METHODS: this paper proposes a new automated generation method of ultrasound elastography (AUE-net) to generate elastography images from conventional ultrasound images. The AUE-net was based on the U-Net architecture and optimized by attention modules and feature residual blocks, which could improve the adaptability of feature extraction for nodules of different sizes. The additional color loss function was used to balance color distribution. In this network, we first attempted to extract the tissue features of the ultrasound image in the latent space, then converted the attributes by modeling the strain, and finally reconstructed them into the corresponding elastography image. RESULTS: a total of 726 thyroid ultrasound elastography images with corresponding conventional images from 397 patients were obtained between 2019 and 2021 as the dataset (646 in training set and 80 in testing set). The mean rating accuracy of the AUE-net generated elastography images by ultrasound specialists was 84.38%. Compared with that of the existing models in the visual aspect, the presented model generated relatively higher quality elastography images. CONCLUSION: the AUE-net generated ultrasound elastography images showed natural appearance and retained tissue information. Accordingly, it seems that B-mode ultrasound harbors information that can link to tissue elasticity. This study may pave the way to generate ultrasound elastography images readily without the need for professional equipment.

The Application Effect of Traditional Chinese Medicine Nursing on General Anesthesia Combined with Epidural Anesthesia and Electric Resection for the Treatment of Bladder Cancer and Its Influence on Tumor Markers.

OBJECTIVE: To explore the effects of traditional Chinese medicine nursing on general anesthesia combined with epidural anesthesia and electric resection to treat bladder cancer and its influence on tumor markers. METHODS: A total of 160 patients with non-muscle-invasive bladder cancer who underwent general anesthesia combined with epidural anesthesia and resection were included in this study. The patients were divided into control group (n = 80) and study group (n = 80) according to the random number table method. The control group received hydroxycamptothecin bladder perfusion therapy, and the study group received traditional Chinese medicine nursing combined with hydroxycamptothecin bladder perfusion therapy. The clinical efficacy, three-year cumulative survival rate, and postoperative recurrence rate of the two groups of patients were detected. The levels of tumor markers including vascular endothelial growth factor (VECF) and bladder tumor antigen (BTA) before and after treatment were also tested. The immune function, inflammatory factor levels, and quality of life of the two groups before and after treatment were evaluated. RESULTS: The total effective rate of the study group (83.75%) was significantly higher than that of the control group (58.75%). After treatment, the serum VEGF and BTA levels, inflammatory factors interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α) levels of the two groups of patients decreased, and the decrease in the study group was more significant than that in the control group (P < 0.05). After treatment, the levels of CD3+, CD4+, and CD4+/CD8+ in the two groups increased (P < 0.05), and the increase in the study group was more significant than that in the control group (P < 0.05). After treatment, the CD8+ levels of the two groups of patients decreased (P < 0.05), and the decrease in the study group was more significant than that in the control group (P < 0.05). After treatment, the quality-of-life scores in both groups increased (P < 0.05), and the increase in the study group was even more significant (P < 0.05). CONCLUSION: Traditional Chinese medicine nursing has significant clinical effects on the treatment of bladder cancer with general anesthesia combined with epidural anesthesia and electric resection. It can more effectively prevent the risk of recurrence of bladder cancer after surgery, significantly improve the quality of life, improve immune system function, regulate the levels of VECF and BTA, effectively reduce the level of serum inflammatory factors, inhibit tumor progression, and reduce tumor viability.