Currently, prehospital emergency medical care procedures and quality vary among different medical institutions, and there is no effective implementation strategy for improving medical quality. Acute cardiovascular disease imposes major burdens to all involved parties and is associated with high morbidity and mortality. Prehospital care for acute cardiovascular disease requires scientific implementation strategies. The use of implementation science research to explore new strategies may improve the quality of prehospital care and reduce the burden of acute cardiovascular disease. The research design is a stepped-wedge cluster randomized controlled trial. A digital intelligent system has been created previously. Researchers will develop a new implementation strategy based on improving ambulance equipment and integration of the intelligent software systems to drive evidence-based prehospital interventions and will select emergency teams in public hospitals to practice it randomly. Data from the entire emergency treatment process will be collected to analyze the impact of strategies in terms of outcomes tailored according to the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework. To identify possible areas of refinement and examine the feasibility of the research, the first phase will be carried out in five hospitals. This study integrates digital and intelligent technologies with implementation science in the design of a prehospital emergency care strategy on the basis of a literature review and expert recommendations, addressing limitations in previous prehospital emergency care approaches. The digital-intelligent-system-based strategy will undergo iterative optimization according to practical feedback and are expected to facilitate the implementation of evidence-based interventions, thereby improving the quality of patient care. This study was prospectively registered on the Chinese Clinical Trial Registry (ChiCTR2400088833; Version 1.0; Date of registration: 27 August 2024; URL: https://www.chictr.org.cn/ ).

Background: The evaluation of autonomic nervous system (ANS) function and coronary physiology through quantitative flow ratio (QFR) analysis provides a precise method for assessing the severity and prognosis of acute coronary syndrome (ACS). Aims: This study aimed to develop and validate a risk score model for predicting the long-term prognosis of non-ST-elevation ACS (NSTE-ACS) patients who underwent complete and successful percutaneous coronary intervention (PCI). Methods: NSTE-ACS patients who underwent complete and successful PCI with preoperative and postoperative QFR measurements between January 2018 and December 2020 in our medical center were included. 24-hour Holter monitoring was performed to assess deceleration capacity (DC) and heart rate variability (HRV) parameters. The primary endpoint was the occurrence of major adverse cardiac events (MACEs). Results: The training cohort consisted of 271 patients, while the testing cohort consisted of 119 patients. The nomogram considered diabetes, normalized low-frequency (nLF) power/normalized high-frequency (nHF) power, DC, cardiac troponin I (cTnI), post-PCI QFR of the target vessel. The model demonstrated excellent discriminative ability, with area under the curve (AUC) values of 0.874 (95% CI: 0.809-0.939) for 1-year MACE prediction in the training cohort and 0.893 (95% CI: 0.808-0.978) in the testing cohort. For 2-year MACE prediction, the AUC values were 0.882 (95% CI: 0.822-0.942) and 0.842 (95% CI: 0.724-0.960) in the training and testing cohorts. Conclusions: We successfully developed and validated a risk stratification system that integrates baseline clinical characteristics (diabetes, cTnI levels), ANS parameters (nLF/nHF ratio, DC), and coronary physiological assessment (post-PCI QFR). This model effectively predicts MACEs in NSTE-ACS patients following PCI, providing valuable prognostic information for clinical decision-making.

The appearance of stains in digital pathological images is prone to be affected by variations in imaging protocols, dyes, scanners, and illumination conditions. This inconsistency will prevent robustness and generalization of computer-aided diagnostic algorithms. Thus, many researchers have proposed efficient methods to normalize different stained images, among which the CycleGAN method prevails. In practice, we have found that this method can cause the background region to be mistaken for the foreground region or can result in the nucleus being considered cytoplasm, a phenomenon we refer to as Stain Region Inversion (SRI). To address the problem and improve its structure-preserving performance in stain normalization tasks, this paper proposes a novel stain normalization method called Structure-Preserving Self-Attention CycleGAN (SPSA-CycleGAN), which enhances the performance of CycleGAN in processing histological and cytological images. We demonstrate how to utilize multi-head self-attention to capture local features and use grey-scaled images to address the issue of SRI, enhancing the pixel-level structure-preserving capability of the original CycleGAN model. Our method is then verified in five experiments and compared with six other state-of-the-art stain normalization methods. The experimental results demonstrated that our SPSA-CycleGAN has better or comparable performance compared to all the other methods. Code available at: https://github.com/Smile-We/SPSA-CycleGAN

The molecular subtype of breast cancer plays an important role in the prognosis of patients and guides physicians to develop scientific therapeutic regimes. In clinical practice, physicians classify molecular subtypes of breast cancer with immunohistochemistry(IHC) technology, which requires a long cycle for diagnosis, resulting in a delay in effective treatment of patients with breast cancer. To improve the diagnostic rate, we proposed a machine learning method that predicted molecular subtypes of breast cancer from H E-stained histopathological images. Although some molecular subtype prediction methods have been suggested, they are noisy and lack clinical evidence. To address these issues, we introduced a patch filter-based molecular subtype prediction (PFMSP) method using spatial transcriptomics data, training a patch filter with spatial transcriptomics data first, and then the trained filter was used to select valuable patches for molecular subtype prediction in other H E-stained histopathological images. These valuable patches contained one or more genes expressed of ESR1, ESR2, PGR, and ERBB2. We evaluated the performance of our method on the spatial transcriptomics(ST) dataset and the TCGA-BRCA dataset, and the patches filtered by the patch filter achieved accuracies of 80

Thyroid cytology whole slide images (WSIs) hold vital information essential for precise diagnosis. Given the huge size of WSIs, multiple instance learning (MIL) is an effective solution for the WSI classification task when only slide-level labels are accessible. The embedding-based MIL uses a feature extractor pretrained with the self-supervised contrastive learning framework to eliminate the dependence on patch-level labels. However, the distribution of class in thyroid patches is unbalanced, and most existing self-supervised contrastive learning methods take little account of the data imbalance, which makes the features not discriminative enough. To address this problem, we propose a novel balanced self-supervised contrastive learning (BS2CL) framework for pretraining. It first clusters the patches to preserve the class structure of the patches and then assigns the clustering centers to a set of pre-computed uniformly distributed optimal locations. This constraint creates a more uniform distribution of different classes in the feature space which leads to clearer class boundaries between different classes, an unbiased feature space, and more discriminative features. Furthermore, a bag-level data augmentation strategy is introduced to increase bag quantities and improve classification performance. Extensive experiments show that the proposed method outperforms other latest methods on the thyroid cytology WSI dataset.

Cervical cytologic whole slide image (WSI) multiple classificaton (grading) is a challenging task. Current studies typically ignore the unbalanced data distribution and require multi-class annotations to learn cell features for WSI grading, which largely suffers from label noise. In this paper, we design a three-stage framework to solve these problems. The first stage uses a binary detector and classifier to screen abnormal cells from the gigapixel WSI. By focusing on binary tasks, we alleviate the effects of label noise and data imbalance. To explore the intrinsic characteristics of cervical cells, we use self-supervised learning to acquire comprehensive cell features for subsequent analysis. In the third stage, we propose a well-designed supervised contrastive learning (SCL) framework for WSI grading. To handle the data-imbalance problem, we pre-compute the optimal positions of class centers which are uniformly distributed on the feature space. During training, we perform SCL whilst matching WSIs to their corresponding class centers, which fosters a class-balanced feature space for WSI representations. Extensive experiments on a large-scale dataset demonstrate that our method achieves state-of-the-art performance.

Histopathology image analysis plays a crucial role in cancer diagnosis. However, training a clinically applicable segmentation algorithm requires pathologists to engage in labour-intensive labelling. In contrast, weakly supervised learning methods, which only require coarse-grained labels at the image level, can significantly reduce the labeling efforts. Unfortunately, while these methods perform reasonably well in slide-level prediction, their ability to locate cancerous regions, which is essential for many clinical applications, remains unsatisfactory. Previously, we proposed CAMEL, which achieves comparable results to those of fully supervised baselines in pixel-level segmentation. However, CAMEL requires 1,280x1,280 image-level binary annotations for positive WSIs. Here, we present CAMEL2, by introducing a threshold of the cancerous ratio for positive bags, it allows us to better utilize the information, consequently enabling us to scale up the image-level setting from 1,280x1,280 to 5,120x5,120 while maintaining the accuracy. Our results with various datasets, demonstrate that CAMEL2, with the help of 5,120x5,120 image-level binary annotations, which are easy to annotate, achieves comparable performance to that of a fully supervised baseline in both instance- and slide-level classifications.

Objectives The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes. Design For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists. Results The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions. Conclusions The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists.

Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 80 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future research in automated cervical cytology screening.

Breast cancer histopathological image classification has made great progress with the use of Convolutional Neural Networks (CNNs). However, due to the limited receptive field, CNNs have difficulty in learning the global information of breast cancer histopathological images, hindering the further improvement of this task. To solve this problem, we reasonably apply self-attention mechanism to this task and propose a new network called Local-Global Vision Transformer (LGViT) which utilizes CNNs to capture local features and self-attention mechanism to learn global features of histopathological images. LGViT has several advantages: (1) We propose Local-Global Multi-head Self-attention, a new mechanism that models long-range dependencies with low computational cost. In this mechanism, self-attention is first performed separately within each window. Then, Multiple Instance Learning scheme is utilized to obtain a representative token for each window. Finally, we compute self-attention among these representative tokens to capture global information. (2) We propose Ghost Feed-forward Network, which compensates for the deficiency of Vision Transformer in capturing local features via a locality mechanism. (3) We use a CNN stem to effectively capture low-level information. Experiments on the PatchCamelyon dataset show that LGViT is better than other state-of-the-art methods.

Convolutional Neural Networks (CNNs) have become the de facto approach for medical image classification in recent years. However, the deficiency of convolutional operations in extracting global features has limited the further improvement of this task. Vision Transformers (ViTs) can model long-range dependencies via self-attention mechanism but unfortunately lose local feature details. In this paper, we propose a dynamic aggregation enhanced dual-stream network termed DDN to take the advantage of ViT and CNN to enrich the feature representation of medical images. Specifically, our proposed DDN is built by stacking several Dynamic Dual-stream Units (DDU). In DDU, local and global features are learned by the CNN branch and Transformer branch respectively whilst complementing each other via a bi-directional propagation strategy, then features of both branches are aggregated in a dynamic manner and the integrated information is used to enhance the feature representations of the two branches simultaneously. Extensive experiments show that our proposed DDN performs best compared with other state-of-the-art models on the public Kvasir dataset and ISIC2018 dataset.

Tumor mutation burden(TMB) is a biomarker for predicting immunotherapy responses, which can be used to filter out Triple-Negative Breast Cancer(TNBC) patients who benefit from immunotherapy. It is generally measured by whole-exome sequencing (WES) in clinical practice. However, WES has the disadvantage of being expensive, time-consuming, and operational complexity so that it is not available in most hospitals. To solve these issues, we developed a machine learning algorithm that predicts the TMB of TNBC based on the nuclei score of histopathological images, which can obtain high accuracy without manually labeling tumor regions. We verify the effectiveness of patches filtered by nuclei score to TMB classifier by comparing the performance of the model that trained with all patches and trained with selected patches. Experiments results show that the accuracy of the model trained by patches selected with nuclei score is 87.5% and F1 is 80%, which are much higher than training with all patches(87.5% vs 56.25%, 80% vs 58.82%). The time of testing a sample using our approach is only 1 in 26, compared with the test time with all patches. To the best of our knowledge, this is the first research to predict TMB from TNBC histopathological images. The proposed approach has the potential to provide immunotherapy to a much broader subset of patients with TNBC.

Previous studies on deep learning (DL) applications in pathology have focused on pathologist-versus-algorithm comparisons. However, DL will not replace the breadth and contextual knowledge of pathologists; rather, only through their combination may the benefits of DL be achieved. A fully crossed multireader multicase study was conducted to evaluate DL assistance with pathologists' diagnosis of gastric cancer. A total of 110 whole-slide images (WSI) (50 malignant and 60 benign) were interpreted by 16 board-certified pathologists with or without DL assistance, with a washout period between sessions. DL-assisted pathologists achieved a higher area under receiver operating characteristic curve (ROC-AUC) (0.911 vs. 0.863, P = 0.003) than unassisted in interpreting the 110 WSIs. Pathologists with DL assistance demonstrated higher sensitivity in detection of gastric cancer than without (90.63% vs. 82.75%, P = 0.010). No significant difference was observed in specificity with or without deep learning assistance (78.23% vs. 79.90%, P = 0.468). The average review time per WSI was shortened with DL assistance than without (22.68 vs. 26.37 second, P = 0.033). Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis and further boosted the acceptance of this new technique.

The cervical histopathology analysis result is the gold standard for cervical cancer diagnosis. Conventional histopathological examination depends on pathologists’ observation under microscope, which is notoriously labor-intensive and subjective. The popularization of digital pathology technology makes the collection of the cervical histopathological whole slide images (WSIs) more convenient, so it has become possible to develop computer-aided diagnosis methods for cervical cancer. In this work, we first collected the cervical histopathological WSIs from 917 patients with pathological diagnosis through a retrospective study, of which 286 WSIs contained annotations of several lesion areas that were manually outlined by the pathologists. Then we proposed a method for classifying cervical histopathological WSIs by combining deep multi-instance transfer learning (DMITL) and support vector machine (SVM). The DMITL aimed for learning the representations of the WSIs, and the SVM was used for building the classification model of the WSIs. We generated the training and test sets based on our collected WSIs to train and evaluate our method. The validation results have shown that the good performance of our proposed method.

Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, the traditional diagnostic process of FNAC is time-consuming, and its accuracy is highly related to the experience of the cytopathologist. Computer-aided diagnostic (CAD) systems are rapidly evolving to provide objective diagnostic recommendations. So far, most studies have used fixed-size patches and usually hand-select patches for model training. In this study, we develop a CAD system to address these challenges. In order to be consistent with the diagnostic working mode of cytopathologists, the system is mainly composed of two task modules: the detecting module that is responsible for detecting the regions of interest (ROIs) from the whole slide image of the FNAC, and the classification module that identifies ROIs having positive lesions. The system can then output the top-k ROIs with the highest positive probabilities for the cytopathologists to review. In order to obtain the overall good performance of the system, we compared different object detection and classification models, and used a combination of the YOLOV4 and EfficientNet networks in our system.

Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time. Thus, making good use of limited labeled samples in a small dataset to build a high-performance model is the key to medical image classification problem. In this paper, we propose a deeply supervised Layer Selective Attention Network (LSANet), which comprehensively uses label information in feature-level and prediction-level supervision. For feature-level supervision, in order to better fuse the low-level features and high-level features, we propose a novel visual attention module, Layer Selective Attention (LSA), to focus on the feature selection of different layers. LSA introduces a weight allocation scheme which can dynamically adjust the weighting factor of each auxiliary branch during the whole training process to further enhance deeply supervised learning and ensure its generalization. For prediction-level supervision, we adopt the knowledge synergy strategy to promote hierarchical information interactions among all supervision branches via pairwise knowledge matching. Using the public dataset, MedMNIST, which is a large-scale benchmark for biomedical image classification covering diverse medical specialties, we evaluate LSANet on multiple mainstream CNN architectures and various visual attention modules. The experimental results show the substantial improvements of our proposed method over its corresponding counterparts, demonstrating that LSANet can provide a promising solution for label-efficient learning in the field of medical image classification.

Breast cancer has become one of the most common cancers in the world, and it is also the most lethal cancer in women. As a non-invasive imaging modality, ultrasonography can diagnose the degree of breast lesions and be used for large-scale screening. However, since the lesions in breast ultrasound(BUS) images are morphologically diverse, accompanied by relatively low contrast and complex textures, BUS image recognition faces greater challenges than natural images. In this study, We propose a novel network architecture that combines convolutional neural network(CNN) with vision transformer(ViT) to aggregate local feature details and long-range feature dependencies. Moreover, in order to perform multi-scale feature fusion, we introduce cross attention between the deep feature map and the shallow feature map in the network block to carry out the interaction between the deep feature and the shallow feature information. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 85.33%, under the comparable parameter complexity, which outperforms most convolutional neural networks(CNNs) and vision transformers (ViTs).

The accurate pathological diagnosis of endometrial cancer (EC) improves the curative effect and reduces the mortality rate. Deep learning has demonstrated expert-level performance in pathological diagnosis of a variety of organ systems using whole-slide images (WSIs). It is urgent to build the deep learning system for endometrial cancer detection using WSIs. The deep learning model was trained and validated using a dataset of 601 WSIs from PUPH. The model performance was tested on three independent datasets containing a total of 1,190 WSIs. For the retrospective test, we evaluated the model performance on 581 WSIs from PUPH. In the prospective study, 317 consecutive WSIs from PUPH were collected from April 2022 to May 2022. To further evaluate the generalizability of the model, 292 WSIs were gathered from PLAHG as part of the external test set. The predictions were thoroughly analyzed by expert pathologists. The model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.928, 0.924, and 0.801, respectively, on 1,190 WSIs in classifying EC and non-EC. On the retrospective dataset from PUPH/PLAGH, the model achieved an AUC, sensitivity, and specificity of 0.948/0.971, 0.928/0.947, and 0.80/0.938, respectively. On the prospective dataset, the AUC, sensitivity, and specificity were, in order, 0.933, 0.934, and 0.837. Falsely predicted results were analyzed to further improve the pathologists' confidence in the model. The deep learning model achieved a high degree of accuracy in identifying EC using WSIs. By pre-screening the suspicious EC regions, it would serve as an assisted diagnostic tool to improve working efficiency for pathologists.

Background and aim Our previous study found carotid baroreceptor stimulation (CBS) reduces body weight and white adipose tissue (WAT) weight, restores abnormal secretion of adipocytokines and inflammation factors, decreases systolic blood pressure (SBP) by inhibiting activation of sympathetic nervous system (SNS) and renin-angiotensin system (RAS) in obese rats. In this study, we explore effects of CBS on aortic remodeling in obese rats. Methods and results Rats were fed high-fat diet (HFD) for 16 weeks to induce obesity and underwent either CBS device implantation and stimulation or sham operation at 8 weeks. BP and body weight were measured weekly. RAS activity of WAT, histological, biochemical and functional profiles of aortas were detected after 16 weeks. CBS effectively decreased BP in obese rats, downregulated mRNA expression of angiotensinogen (AGT) and renin in WAT, concentrations of AGT, renin, angiotensin II (Ang II), protein levels of Ang II receptor 1 (AT1R) and Ang II receptor 2 (AT2R) in WAT were declined. CBS inhibited reactive oxygen species (ROS) generation, inflammatory response and endoplasmic reticulum (ER) stress in aortas of obese rats, restrained vascular wall thickening and vascular smooth muscle cells (VSMCs) phenotypic switching, increased nitric oxide (NO) synthesis, promoted endothelium-dependent vasodilatation by decreasing protein expression of AT1R and leptin receptor (LepR), increasing protein expression of adiponectin receptor 1 (AdipoR1) in aortic VSMCs. Conclusion CBS reduced BP and reversed aortic remodeling in obese rats, the underlying mechanism might be related to the suppressed SNS activity, restored adipocytokine secretion and restrained RAS activity of WAT.

Breast cancer is the most common cancer among women worldwide. The human epidermal growth factor receptor 2 (HER2) with immunohistochemical (IHC) is widely used for pathological evaluation to provide the appropriate therapy for patients with breast cancer. However, the deficiency of pathologists and subjective and susceptible to inter-observer variation of visual diagnosis are the main challenges. Recently, with the rapid development of artificial intelligence (AI) in disease diagnosis, several automated HER2 scoring methods using traditional computer vision or machine learning methods indicate the improvement of the HER2 diagnostic accuracy, but the unreasonable interpretation in pathology, as well as the expensive and ethical issues for annotation, make these methods still have a long way to deploy in hospitals to ease pathologists' burden in real. In this paper, we propose a HER2 automated scoring system that strictly follows the HER2 scoring guidelines simulating the real workflow of HER2 scores diagnosis by pathologists. Unlike the previous work, our method considers the positive control of HER2 to make sure the assay performance for each slide, eliminating work for repeated comparison between the current field of view (FOV) and positive control FOV, especially for the borderline cases. Besides, for each selected FOV under the microscope, our system provides real-time HER2 scores analysis and visualizations of the membrane staining intensity and completeness corresponding with the cell classifications. Our rigorous workflow along with the flexible interactive adjustion in demand substantially assists pathologists to finish the HER2 diagnosis faster and improves the robustness and accuracy. The proposed system will be embedded in our Thorough Eye platform for deployment in hospitals.