Chronic gastritis classification using gastric X-ray images with a semi-supervised learning method based on tri-training. Med Biol Eng Comput. 2020;58:1239-1250. [PMID: 32221796 DOI: 10.1007/s11517-020-02159-z]

Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review

Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.

Self-supervised learning for gastritis detection with gastric X-ray images

PURPOSE

Manual annotation of gastric X-ray images by doctors for gastritis detection is time-consuming and expensive. To solve this, a self-supervised learning method is developed in this study. The effectiveness of the proposed self-supervised learning method in gastritis detection is verified using a few annotated gastric X-ray images.

METHODS

In this study, we develop a novel method that can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. Models trained based on the proposed method were fine-tuned on datasets comprising a few annotated gastric X-ray images. Five self-supervised learning methods, i.e., SimSiam, BYOL, PIRL-jigsaw, PIRL-rotation, and SimCLR, were compared with the proposed method. Furthermore, three previous methods, one pretrained on ImageNet, one trained from scratch, and one semi-supervised learning method, were compared with the proposed method.

RESULTS

The proposed method's harmonic mean score of sensitivity and specificity after fine-tuning with the annotated data of 10, 20, 30, and 40 patients were 0.875, 0.911, 0.915, and 0.931, respectively. The proposed method outperformed all comparative methods, including the five self-supervised learning and three previous methods. Experimental results showed the effectiveness of the proposed method in gastritis detection using a few annotated gastric X-ray images.

CONCLUSIONS

This paper proposes a novel self-supervised learning method based on a teacher-student architecture for gastritis detection using gastric X-ray images. The proposed method can perform explicit self-supervised learning and learn discriminative representations from gastric X-ray images. The proposed method exhibits potential clinical use in gastritis detection using a few annotated gastric X-ray images.

Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation

Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency of the classifier, and the explicit decision mechanism tends to make the training noise degrade the accuracy of the classification model in the prediction stage. This study proposes the Tri-training algorithm for adaptive nearest neighbor density editing and cross-entropy evaluation (TTADEC), which is used to reduce the training noise formed during the classifier iteration and to solve the problem of inaccurate prediction by explicit decision mechanism. First, the TTADEC algorithm uses the nearest neighbor editing to label high-confidence samples. Then, combined with the relative nearest neighbor to define the local density of samples to screen the pre-training samples, and then dynamically expand the training set by adaptive technique. Finally, the decision process uses cross-entropy to evaluate the completed base classifier of training and assign appropriate weights to it to construct a decision function. The effectiveness of the TTADEC algorithm is verified on the UCI dataset, and the experimental results show that compared with the standard Tri-training algorithm and its improvement algorithm, the TTADEC algorithm has better classification performance and can effectively deal with the semi-supervised classification problem where the training set is insufficient.

Research on Anomaly Identification and Screening and Metallogenic Prediction Based on Semisupervised Neural Network

This paper firstly introduces the background of the research on neural network and anomaly identification screening and mineralization prediction under semisupervised learning, then introduces supervised learning, semisupervised learning, unsupervised learning, and reinforcement learning, analyzes and compares their advantages and disadvantages, and concludes that unsupervised learning is the best way to process the data. In the research method, this paper classifies the obtained geochemical data by using semisupervised learning and then trains the obtained samples using the convolutional neural network model to obtain the mineralization prediction model and check its correctness, which finally provides the direction for the subsequent mineralization prediction research.

Adoption of Dexmedetomidine in Different Doses at Different Timing in Perioperative Patients

Dexmedetomidine (Dex) is an alpha-2 agonist used for sedation during various procedures. Dex activates 2-adrenoceptors, and causes the decrease of sympathetic tone, with attenuation of the neuroendocrine and hemodynamic responses to anesthesia and surgery; it reduces anesthetic and opioid requirements; and causes sedation and analgesia.

OBJECTIVE

it was to compare the perioperative effects of different doses of Dex at different timing in patients using Dex during the perioperative period adopting a medical data classification algorithm based on optimized semi-supervised collaborative training (Tri-training).

METHODS

495 patients requiring surgical treatment in Xingtai People's Hospital were randomly selected as the study subjects. The patients were divided into group A (used before induction), group B (used during induction), and group C (used after induction) according to different induction timing, with 165 cases in each group. Then, groups A, B, and C were divided into groups A1, B1, and C1 (0.4 μg/(kg·h) rate), groups A2, B2, and C2 (0.6 μg/(kg·h) rate), and groups A3, B3, and C3 (0.8 μg/(kg·h) rate) according to the dose used, with 55 cases in each group. Intraoperative anesthesia and postoperative adverse reactions were compared among the 9 groups.

RESULTS

the similarity between the Tri-training algorithm optimized by Naive Bayes (NB) classification algorithm and the actual classification (93.49%) was clearly higher than that by decision tree (DT) and K-nearest neighbor (kNN) classification algorithm (76.21%, 74.31%); heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) values decreased obviously after Dex used in groups B1, C1, B2, C2, B3, and C3 (P <0.05), but did not change significantly in groups A1, A2, and A3 (P >0.05); the proportion of patients with satisfactory Ramsay score in group A3 was distinctly superior than that in groups A1, A2, B1, B2, B3, C1, C2, and C3 (P <0.05); the incidence of adverse reactions in group A3 was significantly inferior than that in groups A1, A2, B1, B2, B3, C1, C2, and C3 (P <0.05).

CONCLUSION

the optimization effect of NB classification algorithm was the best, and the injection of Dex at the injection rate of 0.8 μg/(kg·h) before induction of anesthesia could apparently improve the fluctuation of HR, SBP, and DBP during perioperative period, and effectively reduce the occurrence of adverse reactions in patients, with better sedative effect on patients.

Deep learning-based facial image analysis in medical research: a systematic review protocol

INTRODUCTION

Deep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people's medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients' welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.

METHODS

Databases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study.

ETHICS AND DISSEMINATION

As the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.

PROSPERO REGISTRATION NUMBER

CRD42020196473.

Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases

Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.