Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Linked color imaging and upper gastrointestinal neoplasia

White light imaging (WLI) can sometimes miss early upper gastrointestinal (UGI) neoplasms, particularly minimal changes and flat lesions. Moreover, endoscopic diagnosis of UGI neoplasia is strongly influenced by the condition of the surrounding mucosa. Recently, image-enhanced endoscopy techniques have been developed and used in clinical practice; one of which is linked color imaging (LCI), which has an expanded color range for better recognition of slight differences in mucosal color and enables easy diagnosis and differentiation of noncancerous mucosa from carcinoma. LCI does not require magnified observation and can clearly visualize structures using an ultrathin scope; therefore, it is useful for screening and surveillance endoscopy. LCI is particularly useful for detecting gastric cancer after Helicobacter pylori eradication, which accounts for most gastric cancers currently discovered, and displays malignant areas in orange or orange-red surrounded by intestinal metaplasia in lavender. Data on the use of convolutional neural network and computer-aided diagnosis with LCI for UGI neoplasm detection are currently being collected. Further studies are needed to determine the clinical role of LCI and whether it can replace WLI.

Gastric cancer screening in Western countries: A call to action

Gastric cancer is a major cause of cancer-related death worldwide, despite the reduction in its incidence. The disease is still burdened with a poor prognosis, particularly in Western countries. The main risk factor is the infection by Helicobacter pylori, classified as a class I carcinogen by the IARC, and It is well-known that primary prevention of gastric cancer can be achieved with the eradication of the infection. Moreover, non-invasive measurement of pepsinogens (PGI and PGI/PGII ratio) allows the identification of patients that should undergo upper gastrointestinal (GI) endoscopy. Gastric non-cardia adenocarcinoma is indeed preceded by a well-defined precancerous process that involves consecutive stages, described for the first time by Correa et al. more than 40 years ago, and patients with advance stages of gastric atrophy/intestinal metaplasia and with dysplastic changes should be followed-up periodically with upper GI endoscopies. Despite these effective screening and surveillance methods, national-level screening campaigns have been adopted only in few countries in eastern Asia (Japan and South Korea). In this review, we describe primary and secondary preventive measures for gastric cancer, discussing the need to introduce screening also in Western countries. Moreover, we propose a simple algorithm for screening that could be easily applied in clinical practice.

The application of metagenomics, radiomics and machine learning for diagnosis of sepsis

Introduction

Sepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis.

Methods

Blood samples from sepsis patients were sequenced. α-diversity and β-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms.

Results

The results of α-diversity and β-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group (p < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, Mycobacterium canettii, Solanum pennellii, Ralstonia insidiosa, Ananas comosus, Moraxella osloensis, Escherichia coli, Staphylococcus hominis, Camelina sativa, and Cutibacterium acnes. The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation (p < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation (p < 0.05) with Cutibacterium acnes, Ralstonia insidiosa, Moraxella osloensis, and Staphylococcus hominis. Ananas comosus showed a significant positive correlation (p < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation (p < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction.

Discussion

This study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms

BACKGROUND

Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated.

SUMMARY

In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation.

KEY MESSAGES

Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.

Importance of Patient History in Artificial Intelligence-Assisted Medical Diagnosis: Comparison Study

Background

Medical history contributes approximately 80% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis.

Objective

This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided.

Methods

Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses.

Results

ChatGPT accurately diagnosed 76.6% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3% (28/30) when additional information was included.

Conclusions

Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems.

Advanced diagnostic endoscopy in the upper gastrointestinal tract: Review of the Japan Gastroenterological Endoscopic Society core sessions

The Japan Gastroenterological Endoscopy Society (JGES) held four serial symposia between 2021 and 2022 on state-of-the-art issues related to advanced diagnostic endoscopy of the upper gastrointestinal tract. This review summarizes the four core sessions and presents them as a conference report. Eleven studies were discussed in the 101st JGES Core Session, which addressed the challenges and prospects of upper gastroenterological endoscopy. Ten studies were also explored in the 102nd JGES Core Session on advanced upper gastrointestinal endoscopic diagnosis for decision-making regarding therapeutic strategies. Moreover, eight studies were presented during the 103rd JGES Core Session on the development and evaluation of endoscopic artificial intelligence in the field of upper gastrointestinal endoscopy. Twelve studies were also discussed in the 104th JGES Core Session, which focused on the evidence and new developments related to the upper gastrointestinal tract. The endoscopic diagnosis of upper gastrointestinal diseases using image-enhanced endoscopy and AI is one of the most recent topics and has received considerable attention. These four core sessions enabled us to grasp the current state-of-the-art in upper gastrointestinal endoscopic diagnostics and identify future challenges. Based on these studies, we hope that an endoscopic diagnostic system useful in clinical practice is established for each field of upper gastrointestinal endoscopy.

Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy

BACKGROUND

Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis.

METHODS

A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists.

RESULTS

Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach.

CONCLUSIONS

The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.

Coptisine, the Characteristic Constituent from Coptis chinensis, Exhibits Significant Therapeutic Potential in Treating Cancers, Metabolic and Inflammatory Diseases

Naturally derived alkaloids belong to a class of quite significant organic compounds. Coptisine, a benzyl tetrahydroisoquinoline alkaloid, is one of the major bioactive constituents in Coptis chinensis Franch., which is a famous traditional Chinese medicine. C. chinensis possesses many kinds of functions, including the ability to eliminate heat, expel dampness, purge fire, and remove noxious substances. In Asian countries, C. chinensis is traditionally employed to treat carbuncle and furuncle, diabetes, jaundice, stomach and intestinal disorders, red eyes, toothache, and skin disorders. Up to now, there has been plenty of research of coptisine with respect to its pharmacology. Nevertheless, a comprehensive review of coptisine-associated research is urgently needed. This paper was designed to summarize in detail the progress in the research of the pharmacology, pharmacokinetics, safety, and formulation of coptisine. The related studies included in this paper were retrieved from the following academic databases: The Web of Science, PubMed, Google scholar, Elsevier, and CNKI. The cutoff date was January 2023. Coptisine manifests various pharmacological actions, including anticancer, antimetabolic disease, anti-inflammatory disease, and antigastrointestinal disease effects, among others. Based on its pharmacokinetics, the primary metabolic site of coptisine is the liver. Coptisine is poorly absorbed in the gastrointestinal system, and most of it is expelled in the form of its prototype through feces. Regarding safety, coptisine displayed potential hepatotoxicity. Some novel formulations, including the [Formula: see text]-cyclodextrin-based inclusion complex and nanocarriers, could effectively enhance the bioavailability of coptisine. The traditional use of C. chinensis is closely connected with the pharmacological actions of coptisine. Although there are some disadvantages, including poor solubility, low bioavailability, and possible hepatotoxicity, coptisine is still a prospective naturally derived drug candidate, especially in the treatment of tumors as well as metabolic and inflammatory diseases. Further investigation of coptisine is necessary to facilitate the application of coptisine-based drugs in clinical practice.

Endoscopic Imaging for the Diagnosis of Neoplastic and Pre-Neoplastic Conditions of the Stomach

Gastric cancer is an aggressive disease with low long-term survival rates. An early diagnosis is essential to offer a better prognosis and curative treatment. Upper gastrointestinal endoscopy is the main tool for the screening and diagnosis of patients with gastric pre-neoplastic conditions and early lesions. Image-enhanced techniques such as conventional chromoendoscopy, virtual chromoendoscopy, magnifying imaging, and artificial intelligence improve the diagnosis and the characterization of early neoplastic lesions. In this review, we provide a summary of the currently available recommendations for the screening, surveillance, and diagnosis of gastric cancer, focusing on novel endoscopy imaging technologies.

Precision Medicine for Chronic Endometritis: Computer-Aided Diagnosis Using Deep Learning Model

Chronic endometritis (CE) is a localized mucosal infectious and inflammatory disorder marked by infiltration of CD138(+) endometrial stromal plasmacytes (ESPC). CE is drawing interest in the field of reproductive medicine because of its association with female infertility of unknown etiology, endometriosis, repeated implantation failure, recurrent pregnancy loss, and multiple maternal/newborn complications. The diagnosis of CE has long relied on somewhat painful endometrial biopsy and histopathologic examinations combined with immunohistochemistry for CD138 (IHC-CD138). With IHC-CD138 only, CE may be potentially over-diagnosed by misidentification of endometrial epithelial cells, which constitutively express CD138, as ESPCs. Fluid hysteroscopy is emerging as an alternative, less-invasive diagnostic tool that can visualize the whole uterine cavity in real-time and enables the detection of several unique mucosal findings associated with CE. The biases in the hysteroscopic diagnosis of CE; however, are the inter-observer and intra-observer disagreements on the interpretation of the endoscopic findings. Additionally, due to the variances in the study designs and adopted diagnostic criteria, there exists some dissociation in the histopathologic and hysteroscopic diagnosis of CE among researchers. To address these questions, novel dual immunohistochemistry for CD138 and another plasmacyte marker multiple myeloma oncogene 1 are currently being tested. Furthermore, computer-aided diagnosis using a deep learning model is being developed for more accurate detection of ESPCs. These approaches have the potential to contribute to the reduction in human errors and biases, the improvement of the diagnostic performance of CE, and the establishment of unified diagnostic criteria and standardized clinical guidelines for the disease.