Application of Convolutional Neural Networks for Detection of Superficial Nonampullary Duodenal Epithelial Tumors in Esophagogastroduodenoscopic Images. Clin Transl Gastroenterol. 2020;11:e00154. [PMID: 32352719 PMCID: PMC7145048 DOI: 10.14309/ctg.0000000000000154]

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.

Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications

BACKGROUND

Small bowel disorders present a diagnostic challenge due to the limited accessibility of the small intestine. Accurate diagnosis is made with the aid of specific procedures, like capsule endoscopy or double-ballon enteroscopy, but they are not usually solicited and not widely accessible. This study aims to assess and compare the diagnostic effectiveness of enteroscopy and video capsule endoscopy (VCE) when combined with artificial intelligence (AI) algorithms for the automatic detection of small bowel diseases.

MATERIALS AND METHODS

We performed an extensive literature search for relevant studies about AI applications capable of identifying small bowel disorders using enteroscopy and VCE, published between 2012 and 2023, employing PubMed, Cochrane Library, Google Scholar, Embase, Scopus, and ClinicalTrials.gov databases.

RESULTS

Our investigation discovered a total of 27 publications, out of which 21 studies assessed the application of VCE, while the remaining 6 articles analyzed the enteroscopy procedure. The included studies portrayed that both investigations, enhanced by AI, exhibited a high level of diagnostic accuracy. Enteroscopy demonstrated superior diagnostic capability, providing precise identification of small bowel pathologies with the added advantage of enabling immediate therapeutic intervention. The choice between these modalities should be guided by clinical context, patient preference, and resource availability. Studies with larger sample sizes and prospective designs are warranted to validate these results and optimize the integration of AI in small bowel diagnostics.

CONCLUSIONS

The current analysis demonstrates that both enteroscopy and VCE with AI augmentation exhibit comparable diagnostic performance for the automatic detection of small bowel disorders.

Endoscopic diagnosis and treatment of superficial non-ampullary duodenal epithelial tumors: A review

The surface of the small bowel mucosa is covered more than any other section of the digestive canal; however, the overall prevalence of small bowel tumors of the whole gastrointestinal tract is evidently low. Owing to the improvement in endoscopic techniques, the prevalence of small bowel tumors has increased across multiple countries, which is mainly due to an increase in duodenal tumors. Superficial non-ampullary duodenal epithelial tumors (SNADETs) are defined as tumors originating from the non-ampullary region in the duodenum that share similarities and discrepancies with their gastric and colorectal counterparts in the pathogenesis and clinicopathologic characteristics. To date, white light endoscopy (WLE) remains the cornerstone of endoscopic diagnosis for SNADETs. Besides, narrow-band imaging (NBI) techniques and magnifying endoscopy (ME) have been widely used in the clinic and endorsed by multiple guidelines and consensuses for SNADETs' evaluation. Confocal laser endomicroscopy (CLE), endocytoscopy (ECS), and artificial intelligence (AI) are also up-and-coming methods, showing an exceptional value in the diagnosis of SNADETs. Similar to the endoscopic treatment for colorectal polyps, the choices for SNADETs mainly include cold snare polypectomy (CSP), endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD), and laparoscopic endoscopic cooperative surgery (LECS). However, owing to the narrow lumen, rich vascularity, weak muscle layer, abundant Brunner's gland, and the hardship of endoscope control, the duodenum ranks as one of the most dangerous operating areas in the digestive tract. Therefore, endoscopists must anticipate the difficulties in endoscopic maneuverability, remain aware of the increased risk of complications, and then select the appropriate treatment according to the advantages and disadvantages of each method.

Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images

BACKGROUND AND AIM

Recently, artificial intelligence (AI) has been used in endoscopic examination and is expected to help in endoscopic diagnosis. We evaluated the feasibility of AI using convolutional neural network (CNN) systems for evaluating the depth of invasion of early gastric cancer (EGC), based on endoscopic images.

METHODS

This study used a deep CNN model, ResNet152. From patients who underwent treatment for EGC at our hospital between January 2012 and December 2016, we selected 100 consecutive patients with mucosal (M) cancers and 100 consecutive patients with cancers invading the submucosa (SM cancers). A total of 3508 non-magnifying endoscopic images of EGCs, including white-light imaging, linked color imaging, blue laser imaging-bright, and indigo-carmine dye contrast imaging, were included in this study. A total of 2288 images from 132 patients served as the development dataset, and 1220 images from 68 patients served as the testing dataset. Invasion depth was evaluated for each image and lesion. The majority vote was applied to lesion-based evaluation.

RESULTS

The sensitivity, specificity, and accuracy for diagnosing M cancer were 84.9% (95% confidence interval [CI] 82.3%-87.5%), 70.7% (95% CI 66.8%-74.6%), and 78.9% (95% CI 76.6%-81.2%), respectively, for image-based evaluation, and 85.3% (95% CI 73.4%-97.2%), 82.4% (95% CI 69.5%-95.2%), and 83.8% (95% CI 75.1%-92.6%), respectively, for lesion-based evaluation.

CONCLUSIONS

The application of AI using CNN to evaluate the depth of invasion of EGCs based on endoscopic images is feasible, and it is worth investing more effort to put this new technology into practical use.

Application and prospect of artificial intelligence in digestive endoscopy

INTRODUCTION

With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases.

AREAS COVERED

We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy.

EXPERT OPINION

Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine.

A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology

Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.

Artificial intelligence in small intestinal diseases: Application and prospects

The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.

[Application of deep learning assisted electronic laryngoscope in diagnosis of laryngeal leukoplakia]

In recent years, medical imaging technology and computer technology have made great progress. On the one hand, with the development and popularization of electronic laryngoscope, the image of electronic laryngoscope plays a very important role in the diagnosis of vocal cord lesions. On the other hand, deep learning algorithm，especially convolutional neural networkhas gradually become the first choice of medical image recognition since the foundation of deep learning algorithm. So far, deep learning algorithm has made great contributions in many disciplines. In this paper, the basic concept of deep learning, the current status of image recognition of vocal cord lesions, and the prospect of research based on deep learning in vocal cord image lesions recognition are reviewed.