Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective

The Power of Positive Reporting: Examining China's Anti-Epidemic National Image in Mainstream Media

Covid-19 out broke gave an extreme impact to the globe, imposing a challenge to health publicly and causing social interruptions. As a result, the role of mainstream media in promoting anti-epidemic measures and disseminating national images has become increasingly important. In this study, we examine the anti-epidemic reports in 2020 from three types of international news sources, identifying 566 samples for content and text analysis. Through our analysis, we found that each component of the anti-epidemic report has a clear focus, and that these reports presented China's national image of anti-epidemic in four dimensions. Notably, the European version of People's Daily exhibited a positive reporting tendency, accounting for 86% of the total, with only 8% of reports being negative. This indicates a relatively comprehensive national image construction and communication strategy amid the COVID-19 pandemic. Overall, our research reveals the important role of media in shaping a nation's image during a global crisis. The positive reporting tendency of the European version of People's Daily reflects an effective strategy for promoting a positive national image, thereby dispelling misunderstandings and prejudices towards China's anti-epidemic measures. Our findings provide inspiration for the dissemination of national images in times of crisis, highlighting the importance of comprehensive and well-coordinated communication strategies to promote a positive image.

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study

BACKGROUNDS

The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients.

METHODS

In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance.

RESULTS

The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428).

CONCLUSIONS

GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

Highlight removal for endoscopic images based on accelerated adaptive non-convex RPCA decomposition

OBJECTIVE

Highlights always occur in endoscopic images due to their special imaging environment. It not only increases the difficulty of observation and diagnosis from surgeons, but also influences the performance of Mixed/Augmented Reality techniques in surgery navigation.

METHODS

In this paper, we propose a novel accelerated adaptive non-convex robust principal component analysis method (AANC-RPCA) to remove highlights in endoscopic images. We first detect absolute highlight pixels of the enhanced endoscopic images with adaptive threshold segmentation. The quasi-convex function is proposed to approximate a new non-convex objective function. With detected highlight pixels and quasi-convex function, it introduces gradient to shrink sparse matrix and obtains a faster speed of convergence. Then we divide the image into multiple blocks and perform the parallel computation to enhance the efficiency. Finally, we design a weighted template that decays outward with dilation and linear filtering to reconstruct the endoscopic images. Our approach is almost independent of hyper-parameters and can achieve adaptive decomposition.

RESULTS

It has been verified on multiple types of endoscopic images through experiments and clinical blind tests. The results demonstrate that our method can obtain the best performance for the recovered images with more details in a shorter time (about 3-5 times).

CONCLUSION

Coupled with the user study, both the quantitative and qualitative results indicate that our approach has the potential to be highly useful in endoscopy images. Compared with the existing highlight removal approaches, our method obtains the SOTA results and has the potential to be applied in the various medical processing processes.

How do people think about the implementation of speech and video recognition technology in emergency medical practice?

Background

Recently, speech and video information recognition technology (SVRT) has developed rapidly. Introducing SVRT into the emergency medical practice process may lead to improvements in health care. The purpose of this study was to evaluate the level of acceptance of SVRT among patients, caregivers and emergency medical staff.

Methods

Structured questionnaires were developed for the patient or caregiver group and the emergency medical staff group. The survey was performed in one tertiary academic hospital emergency department. Questions were optimized for each specific group, and responses were provided mostly using Likert 5-scales. Additional multivariable logistic regression analyses for the whole cohort and subgroups were conducted to calculate odds ratios (OR) and confidence intervals (CI) to examine the association between individual characteristics and SVRT acceptance.

Results

Of 264 participants, respondents demonstrated a positive attitude and acceptance toward SVRT and artificial intelligence (AI) in future; 179 (67.8%) for video recordings, and 190 (72.0%) for speech recordings. A multivariable logistic regression model revealed that several factors were associated with acceptance of SVRT in emergency medical practice: belief in health care improvement by signal analysis technology (OR, 95% CIs: 2.48 (1.15–5.42)) and AI (OR, 95% CIs: 1.70 (0.91–3.17)), reliability of AI application in emergency medicine (OR, 95% CIs: 2.36 (1.28–4.35)) and the security of personal information (OR, 95% CIs: 1.98 (1.10–3.63)).

Conclusion

A high level of acceptance toward SVRT has been shown in patients or caregivers, and it also appears to be associated with positive attitudes toward new technology, AI and security of personal information.

Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions

BACKGROUND AND AIM

Endoscopic ultrasound (EUS) is the most accurate diagnostic modality for polypoid lesions of the gallbladder (GB), but is limited by subjective interpretation. Deep learning-based artificial intelligence (AI) algorithms are under development. We evaluated the diagnostic performance of AI in differentiating polypoid lesions using EUS images.

METHODS

The diagnostic performance of the EUS-AI system with ResNet50 architecture was evaluated via three processes: training, internal validation, and testing using an AI development cohort of 1039 EUS images (836 GB polyps and 203 gallstones). The diagnostic performance was verified using an external validation cohort of 83 patients and compared with the performance of EUS endoscopists.

RESULTS

In the AI development cohort, we developed an EUS-AI algorithm and evaluated the diagnostic performance of the EUS-AI including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, these values for EUS-AI were 57.9%, 96.5%, 77.8%, 91.6%, and 89.8%, respectively. In the external validation cohort, we compared diagnostic performances between EUS-AI and endoscopists. For the differential diagnosis of neoplastic and non-neoplastic GB polyps, the sensitivity and specificity were 33.3% and 96.1% for EUS-AI; they were 74.2% and 44.9%, respectively, for the endoscopists. Besides, the accuracy of the EUS-AI was between the accuracies of mid-level (66.7%) and expert EUS endoscopists (77.5%).

CONCLUSIONS

This newly developed EUS-AI system showed favorable performance for the diagnosis of neoplastic GB polyps, with a performance comparable to that of EUS endoscopists.

Automated Identification of Skull Fractures With Deep Learning: A Comparison Between Object Detection and Segmentation Approach

Objective: Skull fractures caused by head trauma can lead to life-threatening complications. Hence, timely and accurate identification of fractures is of great importance. Therefore, this study aims to develop a deep learning system for automated identification of skull fractures from cranial computed tomography (CT) scans.

Method: This study retrospectively analyzed CT scans of 4,782 patients (median age, 54 years; 2,583 males, 2,199 females; development set: n = 4,168, test set: n = 614) diagnosed with skull fractures between September 2016 and September 2020. Additional data of 7,856 healthy people were included in the analysis to reduce the probability of false detection. Skull fractures in all the scans were manually labeled by seven experienced neurologists. Two deep learning approaches were developed and tested for the identification of skull fractures. In the first approach, the fracture identification task was treated as an object detected problem, and a YOLOv3 network was trained to identify all the instances of skull fracture. In the second approach, the task was treated as a segmentation problem and a modified attention U-net was trained to segment all the voxels representing skull fracture. The developed models were tested using an external test set of 235 patients (93 with, and 142 without skull fracture).

Results: On the test set, the YOLOv3 achieved average fracture detection sensitivity and specificity of 80.64, and 85.92%, respectively. On the same dataset, the modified attention U-Net achieved a fracture detection sensitivity and specificity of 82.80, and 88.73%, respectively.

Conclusion: Deep learning methods can identify skull fractures with good sensitivity. The segmentation approach to fracture identification may achieve better results.

Accurate diagnosis of endoscopic mucosal healing in ulcerative colitis using deep learning and machine learning

BACKGROUND

In clinical applications, mucosal healing is a therapeutic goal in patients with ulcerative colitis (UC). Endoscopic remission is associated with lower rates of colectomy, relapse, hospitalization, and colorectal cancer. Differentiation of mucosal inflammatory status depends on the experience and subjective judgments of clinical physicians. We developed a computer-aided diagnostic system using deep learning and machine learning (DLML-CAD) to accurately diagnose mucosal healing in UC patients.

METHODS

We selected 856 endoscopic colon images from 54 UC patients (643 images with endoscopic score 0-1 and 213 with score 2-3) from the endoscopic image database at Tri-Service General Hospital, Taiwan. Endoscopic grading using the Mayo endoscopic subscore (MES 0-3) was performed by two reviewers. A pretrained neural network extracted image features, which were used to train three different classifiers-deep neural network (DNN), support vector machine (SVM), and k-nearest neighbor (k-NN) network.

RESULTS

DNN classified MES 0 to 1, representing mucosal healing, vs MES 2 to 3 images with 93.8% accuracy (sensitivity 84.6%, specificity 96.9%); SVM had 94.1% accuracy (sensitivity 89.2%, specificity 95.8%); and k-NN had 93.4% accuracy (sensitivity 86.2%, specificity 95.8%). Combined, ensemble learning achieved 94.5% accuracy (sensitivity 89.2%, specificity 96.3%). The system further differentiated between MES 0, representing complete mucosal healing, and MES 1 images with 89.1% accuracy (sensitivity 82.3%, specificity 92.2%).

CONCLUSION

Our DLML-CAD diagnosis achieved 94.5% accuracy for endoscopic mucosal healing and 89.0% accuracy for complete mucosal healing. This system can provide clinical physicians with an accurate auxiliary diagnosis in treating UC.

Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments

Background and purpose - Artificial intelligence has rapidly become a powerful method in image analysis with the use of convolutional neural networks (CNNs). We assessed the ability of a CNN, with a fast object detection algorithm previously identifying the regions of interest, to detect distal radius fractures (DRFs) on anterior-posterior (AP) wrist radiographs. Patients and methods - 2,340 AP wrist radiographs from 2,340 patients were enrolled in this study. We trained the CNN to analyze wrist radiographs in the dataset. Feasibility of the object detection algorithm was evaluated by intersection of the union (IOU). The diagnostic performance of the network was measured by area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity, and Youden Index; the results were compared with those of medical professional groups. Results - The object detection model achieved a high average IOU, and none of the IOUs had a value less than 0.5. The AUC of the CNN for this test was 0.96. The network had better performance in distinguishing images with DRFs from normal images compared with a group of radiologists in terms of the accuracy, sensitivity, specificity, and Youden Index. The network presented a similar diagnostic performance to that of the orthopedists in terms of these variables. Interpretation - The network exhibited a diagnostic ability similar to that of the orthopedists and a performance superior to that of the radiologists in distinguishing AP wrist radiographs with DRFs from normal images under limited conditions. Further studies are required to determine the feasibility of applying our method as an auxiliary in clinical practice under extended conditions.

Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy

Assisted diagnosis using artificial intelligence has been a holy grail in medical research for many years, and recent developments in computer hardware have enabled the narrower area of machine learning to equip clinicians with potentially useful tools for computer assisted diagnosis (CAD) systems. However, training and assessing a computer's ability to diagnose like a human are complex tasks, and successful outcomes depend on various factors. We have focused our work on gastrointestinal (GI) endoscopy because it is a cornerstone for diagnosis and treatment of diseases of the GI tract. About 2.8 million luminal GI (esophageal, stomach, colorectal) cancers are detected globally every year, and although substantial technical improvements in endoscopes have been made over the last 10-15 years, a major limitation of endoscopic examinations remains operator variation. This translates into a substantial inter-observer variation in the detection and assessment of mucosal lesions, causing among other things an average polyp miss-rate of 20% in the colon and thus the subsequent development of a number of post-colonoscopy colorectal cancers. CAD systems might eliminate this variation and lead to more accurate diagnoses. In this editorial, we point out some of the current challenges in the development of efficient computer-based digital assistants. We give examples of proposed tools using various techniques, identify current challenges, and give suggestions for the development and assessment of future CAD systems.