Establishment and validation of an artificial intelligence-based model for real-time detection and classification of colorectal adenoma.

Colorectal cancer (CRC) prevention requires early detection and removal of adenomas. We aimed to develop a computational model for real-time detection and classification of colorectal adenoma. Computationally constrained background based on real-time detection, we propose an improved adaptive lightweight ensemble model for real-time detection and classification of adenomas and other polyps. Firstly, we devised an adaptive lightweight network modification and effective training strategy to diminish the computational requirements for real-time detection. Secondly, by integrating the adaptive lightweight YOLOv4 with the single shot multibox detector network, we established the adaptive small object detection ensemble (ASODE) model, which enhances the precision of detecting target polyps without significantly increasing the model's memory footprint. We conducted simulated training using clinical colonoscopy images and videos to validate the method's performance, extracting features from 1148 polyps and employing a confidence threshold of 0.5 to filter out low-confidence sample predictions. Finally, compared to state-of-the-art models, our ASODE model demonstrated superior performance. In the test set, the sensitivity of images and videos reached 87.96% and 92.31%, respectively. Additionally, the ASODE model achieved an accuracy of 92.70% for adenoma detection with a false positive rate of 8.18%. Training results indicate the effectiveness of our method in classifying small polyps. Our model exhibits remarkable performance in real-time detection of colorectal adenomas, serving as a reliable tool for assisting endoscopists.

Efficacy of COVID-19 vaccines in inflammatory bowel disease patients receiving anti-TNF therapy: A systematic review and meta-analysis.

Background and objectives: There are concerns about the serological responses to Coronavirus disease 2019 (COVID-19) vaccines in inflammatory bowel disease (IBD) patients, particularly those receiving anti-TNF therapy. This study aimed to systematically evaluate the efficacy of COVID-19 vaccines in IBD patients receiving anti-TNF therapy. Methods: Electronic databases were searched to identify relevant studies. We calculated pooled seroconversion rate after COVID-19 vaccination and subgroup analysis for vaccine types and different treatments were performed. Additionally, we estimated pooled rate of T cell response, neutralization response, and breakthrough infections in this population. Results: 32 studies were included in the meta-analysis. IBD patients receiving anti-TNF therapy had relatively high overall seroconversion rate after complete vaccination, with no statistical difference in antibody responses associated with different drug treatments. The pooled positivity rate of T cell response was 0.85 in IBD patients receiving anti-TNF therapy. Compared with healthy controls, the positivity of neutralization assays was significantly lower in IBD patients receiving anti-TNF therapy. The pooled rate of breakthrough infections in IBD patients receiving anti-TNF therapy was 0.04. Conclusions: COVID-19 vaccines have shown good efficacy in IBD patients receiving anti-TNF therapy. However, IBD patients receiving anti-TNF have a relatively high rate of breakthrough infections and a low level of neutralization response.

Effective Immunohistochemistry Pathology Microscopy Image Generation Using CycleGAN.

Immunohistochemistry detection technology is able to detect more difficult tumors than regular pathology detection technology only with hematoxylin-eosin stained pathology microscopy images, - for example, neuroendocrine tumor detection. However, making immunohistochemistry pathology microscopy images costs much time and money. In this paper, we propose an effective immunohistochemistry pathology microscopic image-generation method that can generate synthetic immunohistochemistry pathology microscopic images from hematoxylin-eosin stained pathology microscopy images without any annotation. CycleGAN is adopted as the basic architecture for the unpaired and unannotated dataset. Moreover, multiple instances learning algorithms and the idea behind conditional GAN are considered to improve performance. To our knowledge, this is the first attempt to generate immunohistochemistry pathology microscopic images, and our method can achieve good performance, which will be very useful for pathologists and patients when applied in clinical practice.

Predict Ki-67 Positive Cells in H&E-Stained Images Using Deep Learning Independently From IHC-Stained Images.

OBJECTIVE: To obtain molecular information in slides directly from H&E staining slides, which apparently display morphological information, to show that some differences in molecular level have already encoded in morphology. METHODS: In this paper, we selected Ki-67-expression as the representative of molecular information. We proposed a method that can predict Ki-67 positive cells directly from H&E stained slides by a deep convolutional network model. To train this model, we constructed a dataset containing Ki-67 negative or positive cell images and background images. These images were all extracted from H&E stained WSIs and the Ki-67 expression was acquired from the corresponding IHC stained WSIs. The trained model was evaluated both on classification performance and the ability to quantify Ki-67 expression in H&E stained images. RESULTS: The model achieved an average accuracy of 0.9371 in discrimination of Ki-67 negative cell images, positive cell images and background images. As for evaluation of quantification performance, the correlation coefficient between the quantification results of H&E stained images predicted by our model and that of IHC stained images obtained by color channel filtering is 0.80. CONCLUSION AND SIGNIFICANCE: Our study indicates that the deep learning model has a good performance both on prediction of Ki-67 positive cells and quantification of Ki-67 expression in cancer samples stained by H&E. More generally, this study shows that deep learning is a powerful tool in exploring the relationship between morphological information and molecular information. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/liuyiqing2018/predict_Ki-67_from_HE.