Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors

Deep learning analysis for differential diagnosis and risk classification of gastrointestinal tumors

OBJECTIVES

Recently, artificial intelligence (AI) has been applied to clinical diagnosis. Although AI has already been developed for gastrointestinal (GI) tract endoscopy, few studies have applied AI to endoscopic ultrasound (EUS) images. In this study, we used a computer-assisted diagnosis (CAD) system with deep learning analysis of EUS images (EUS-CAD) and assessed its ability to differentiate GI stromal tumors (GISTs) from other mesenchymal tumors and their risk classification performance.

MATERIALS AND METHODS

A total of 101 pathologically confirmed cases of subepithelial lesions (SELs) arising from the muscularis propria layer, including 69 GISTs, 17 leiomyomas and 15 schwannomas, were examined. A total of 3283 EUS images were used for training and five-fold-cross-validation, and 827 images were independently tested for diagnosing GISTs. For the risk classification of 69 GISTs, including very-low-, low-, intermediate- and high-risk GISTs, 2,784 EUS images were used for training and three-fold-cross-validation.

RESULTS

For the differential diagnostic performance of GIST among all SELs, the accuracy, sensitivity, specificity and area under the receiver operating characteristic (ROC) curve were 80.4%, 82.9%, 75.3% and 0.865, respectively, whereas those for intermediate- and high-risk GISTs were 71.8%, 70.2%, 72.0% and 0.771, respectively.

CONCLUSIONS

The EUS-CAD system showed a good diagnostic yield in differentiating GISTs from other mesenchymal tumors and successfully demonstrated the GIST risk classification feasibility. This system can determine whether treatment is necessary based on EUS imaging alone without the need for additional invasive examinations.

Efficacy of real-time artificial intelligence-aid endoscopic ultrasonography diagnostic system in discriminating gastrointestinal stromal tumors and leiomyomas: a multicenter diagnostic study

Background

Gastrointestinal stromal tumors (GISTs) represent the most prevalent type of subepithelial lesions (SELs) with malignant potential. Current imaging tools struggle to differentiate GISTs from leiomyomas. This study aimed to create and assess a real-time artificial intelligence (AI) system using endoscopic ultrasonography (EUS) images to differentiate between GISTs and leiomyomas.

Methods

The AI system underwent development and evaluation using EUS images from 5 endoscopic centers in China between January 2020 and August 2023. EUS images of 1101 participants with SELs were retrospectively collected for AI system development. A cohort of 241 participants with SELs was recruited for external AI system evaluation. Another cohort of 59 participants with SELs was prospectively enrolled to assess the real-time clinical application of the AI system. The AI system's performance was compared to that of endoscopists. This study is registered with Chictr.org.cn, Number ChiCT2000035787.

Findings

The AI system displayed an area under the curve (AUC) of 0.948 (95% CI: 0.921-0.969) for discriminating GISTs and leiomyomas. The AI system's accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) reached 91.7% (95% CI 87.5%-94.6%), 90.3% (95% CI 83.4%-94.5%), 93.0% (95% CI 87.2%-96.3%), 91.9% (95% CI 85.3%-95.7%), and 91.5% (95% CI 85.5%-95.2%), respectively. Moreover, the AI system exhibited excellent performance in diagnosing ≤20 mm SELs (ACC 93.5%, 95% CI 0.900-0.969). In a prospective real-time clinical application trial, the AI system achieved an AUC of 0.865 (95% CI 0.764-0.966) and 0.864 (95% CI 0.762-0.966) for GISTs and leiomyomas diagnosis, respectively, markedly surpassing endoscopists [AUC 0.698 (95% CI 0.562-0.834) for GISTs and AUC 0.695 (95% CI 0.546-0.825) for leiomyomas].

Interpretation

We successfully developed a real-time AI-assisted EUS diagnostic system. The incorporation of the real-time AI system during EUS examinations can assist endoscopists in rapidly and accurately differentiating various types of SELs in clinical practice, facilitating improved diagnostic and therapeutic decision-making.

Funding

Science and Technology Commission Foundation of Shanghai Municipality, Science and Technology Commission Foundation of the Xuhui District, the Interdisciplinary Program of Shanghai Jiao Tong University and the Research Funds of Shanghai Sixth people's Hospital.

Differentiating Gastrointestinal Stromal Tumors From Leiomyomas of Upper Digestive Tract Using Convolutional Neural Network Model by Endoscopic Ultrasonography

BACKGROUND

Gastrointestinal stromal tumors (GISTs) and leiomyomas are the most common submucosal tumors of the upper digestive tract, and the diagnosis of the tumors is essential for their treatment and prognosis. However, the ability of endoscopic ultrasonography (EUS) which could correctly identify the tumor types is limited and closely related to the knowledge, operational level, and experience of the endoscopists. Therefore, the convolutional neural network (CNN) is used to assist endoscopists in determining GISTs or leiomyomas with EUS.

MATERIALS AND METHODS

A model based on CNN was constructed according to GoogLeNet architecture to distinguish GISTs or leiomyomas. All EUS images collected from this study were randomly sampled and divided into training set (n=411) and testing set (n=103) in a ratio of 4:1. The CNN model was trained by EUS images from the training set, and the testing set was utilized to evaluate the performance of the CNN model. In addition, there were some comparisons between endoscopists and CNN models.

RESULTS

It was shown that the sensitivity and specificity in identifying leiomyoma were 95.92%, 94.44%, sensitivity and specificity in identifying GIST were 94.44%, 95.92%, and accuracy in total was 95.15% of the CNN model. It indicates that the diagnostic accuracy of the CNN model is equivalent to skilled endoscopists, or even higher than them.

CONCLUSION

While identifying GIST or leiomyoma, the performance of CNN model was robust, which is highlighting its promising role in supporting less-experienced endoscopists and reducing interobserver agreement.

Artificial Intelligence-Based Diagnosis of Gastric Mesenchymal Tumors Using Digital Endosonography Image Analysis

Background/Objectives: Artificial intelligence (AI)-assisted endoscopic ultrasonography (EUS) diagnostic tools have shown excellent performance in diagnosing gastric mesenchymal tumors. This study aimed to assess whether incorporating clinical and endoscopic factors into AI-assisted EUS classification models based on digital image analysis could improve the diagnostic performance of AI-assisted EUS diagnostic tools. Methods: We retrospectively analyzed the data of 464 patients who underwent both EUS and surgical resection of gastric mesenchymal tumors, including 294 gastrointestinal stromal tumors (GISTs), 52 leiomyomas, and 41 schwannomas. AI-assisted classification models for GISTs and non-GIST tumors were developed utilizing clinical and endoscopic factors and digital EUS image analysis. Results: Regarding the baseline EUS classification models, the area under the receiver operating characteristic (AUC) values of the logistic regression, decision tree, random forest, K-nearest neighbor (KNN), and support vector machine (SVM) models were 0.805, 0.673, 0.781, 0.740, and 0.791, respectively. Using the new classification models incorporating clinical and endoscopic factors into the baseline classification models, the AUC values of the logistic regression, decision tree, random forest, KNN, and SVM models increased to 0.853, 0.715, 0.896, 0.825, and 0.794, respectively. In particular, the random forest and KNN models exhibited significant improvement in performance in Delong's test (both p < 0.001). Conclusion: The diagnostic performance of the AI-assisted EUS classification models improved when clinical and endoscopic factors were incorporated. Our results provided direction for developing new AI-assisted EUS models for gastric mesenchymal tumors.

Diagnostic Endoscopic Ultrasound (EUS) of the Luminal Gastrointestinal Tract

The purpose of this review is to focus on the diagnostic endoscopic ultrasound of the gastrointestinal tract. In the last decades, EUS has gained a central role in the staging of epithelial and sub-epithelial lesions of the gastrointestinal tract. With the evolution of imaging, the position of EUS in the diagnostic work-up and the staging flow-chart has continuously changed with two extreme positions: some gastroenterologists think that EUS is absolutely indispensable, and some think it is utterly useless. The truth is, as always, somewhere in between the two extremes. Analyzing the most up-to-date and strong evidence, we will try to give EUS the correct position in our daily practice.

Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography

Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.

Endoscopic ultrasound-based application system for predicting endoscopic resection-related outcomes and diagnosing subepithelial lesions: Multicenter prospective study

OBJECTIVES

Subepithelial lesions (SELs) are associated with various endoscopic resection (ER) outcomes and diagnostic challenges. We aimed to establish a tool for predicting ER-related outcomes and diagnosing SELs and to investigate the predictive value of endoscopic ultrasound (EUS).

METHODS

Phase 1 (system development) was performed in a retrospective cohort (n = 837) who underwent EUS before ER for SELs at eight hospitals. Prediction models for five key outcomes were developed using logistic regression. Models with satisfactory internal validation performance were included in a mobile application system, SEL endoscopic resection predictor (SELERP). In Phase 2, the models were externally validated in a prospective cohort of 200 patients.

RESULTS

An SELERP was developed using EUS characteristics, which included 10 models for five key outcomes: post-ER ulcer management, short procedure time, long hospital stay, high medication costs, and diagnosis of SELs. In Phase 1, 10 models were derived and validated (C-statistics, 0.67-0.99; calibration-in-the-large, -0.14-0.10; calibration slopes, 0.92-1.08). In Phase 2, the derived risk prediction models showed convincing discrimination (C-statistics, 0.64-0.73) and calibration (calibration-in-the-large, -0.02-0.05; calibration slopes, 1.01-1.09) in the prospective cohort. The sensitivities and specificities of the five diagnostic models were 68.3-95.7% and 64.1-83.3%, respectively.

CONCLUSION

We developed and prospectively validated an application system for the prediction of ER outcomes and diagnosis of SELs, which could aid clinical decision-making and facilitate patient-physician consultation. EUS features significantly contributed to the prediction.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, http://www.chictr.org.cn (ChiCTR2000040118).

Artificial Intelligence in the Prediction of Gastrointestinal Stromal Tumors on Endoscopic Ultrasonography Images: Development, Validation and Comparison with Endosonographers

Background/Aims

The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation.

Methods

We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals.

Results

A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers.

Conclusions

We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.

A deep learning-based system to identify originating mural layer of upper gastrointestinal submucosal tumors under EUS

Background and Objective

EUS is the most accurate procedure to determine the originating mural layer and subsequently select the treatment of submucosal tumors (SMTs). However, it requires superb technical and cognitive skills. In this study, we propose a system named SMT Master to determine the originating mural layer of SMTs under EUS.

Materials and Methods

We developed 3 models: deep convolutional neural network (DCNN) 1 for lesion segmentation, DCNN2 for mural layer segmentation, and DCNN3 for the originating mural layer classification. A total of 2721 EUS images from 201 patients were used to train the 3 models. We validated our model internally and externally using 283 images from 26 patients and 172 images from 26 patients, respectively. We applied 368 images from 30 patients for the man-machine contest and used 30 video clips to test the originating mural layer classification.

Results

In the originating mural layer classification task, DCNN3 achieved a classification accuracy of 84.43% and 80.68% at internal and external validations, respectively. In the video test, the accuracy was 80.00%. DCNN1 achieved Dice coefficients of 0.956 and 0.776 for lesion segmentation at internal and external validations, respectively, whereas DCNN2 achieved Dice coefficients of 0.820 and 0.740 at internal and external validations, respectively. The system achieved 90.00% accuracy in classification, which is comparable with that of EUS experts.

Conclusions

Our proposed system has the potential to solve difficulties in determining the originating mural layer of SMTs in EUS procedures, which relieves the EUS learning pressure of physicians.

Artificial intelligence in endoscopy: Overview, applications, and future directions

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

Multi-Task Learning With Hierarchical Guidance for Locating and Stratifying Submucosal Tumors

Locating and stratifying the submucosal tumor of the digestive tract from endoscopy ultrasound (EUS) images are of vital significance to the preliminary diagnosis of tumors. However, the above problems are challenging, due to the poor appearance contrast between different layers of the digestive tract wall (DTW) and the narrowness of each layer. Few of existing deep-learning based diagnosis algorithms are devised to tackle this issue. In this article, we build a multi-task framework for simultaneously locating and stratifying the submucosal tumor. And considering the awareness of the DTW is critical to the localization and stratification of the tumor, we integrate the DTW segmentation task into the proposed multi-task framework. Except for sharing a common backbone model, the three tasks are explicitly directed with a hierarchical guidance module, in which the probability map of DTW itself is used to locally enhance the feature representation for tumor localization, and the probability maps of DTW and tumor are jointly employed to locally enhance the feature representation for tumor stratification. Moreover, by means of the dynamic class activation map, probability maps of DTW and tumor are reused to enforce the stratification inference process to pay more attention to DTW and tumor regions, contributing to a reliable and interpretable submucosal tumor stratification model. Additionally, considering the relation with respect to other structures is beneficial for stratifying tumors, we devise a graph reasoning module to replenish non-local relation knowledge for the stratification branch. Experiments on a Stomach-Esophagus and an Intestinal EUS dataset prove that our method achieves very appealing performance on both tumor localization and stratification, significantly outperforming state-of-the-art object detection approaches.

Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases

Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.

Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis

BACKGROUND

Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.

AIM

To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer.

METHODS

Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.

RESULTS

Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819.

CONCLUSION

AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.

EUS-Guided Diagnosis of Gastric Subepithelial Lesions, What Is New?

Gastric subepithelial lesions (SELs) are intramural lesions that arise underneath the gastric mucosa. SELs can be benign, but can also be malignant or have malignant potential. Therefore, correct diagnosis is crucial. Endosonography has been established as the diagnostic gold standard. Although the identification of some of these lesions can be carried out immediately, solely based on their echo characteristics, for certain lesions histological examination is necessary. Sometimes histology can be inconclusive, especially for smaller lesions. Therefore, new methods have been developed in recent years to assist decision making, such as contrast enhanced endosonography, EUS elastography, and artificial intelligence systems. In this narrative review we provide a complete overview of the gastric SELs and summarize the new data of the last ten years concerning the diagnostic advances of endosonography on this topic.

Artificial intelligence in endoscopic ultrasonography: risk stratification of gastric gastrointestinal stromal tumors

Background

Previous studies have identified useful endoscopic ultrasonography (EUS) features to predict the malignant potential of gastrointestinal stromal tumors (GISTs). However, the results of the studies were not consistent. Artificial intelligence (AI) has shown promising results in medicine.

Objectives

We aimed to build a risk stratification EUS-AI model to predict the malignancy potential of GISTs.

Design

This was a retrospective study with external validation.

Methods

We developed two models using EUS images from two hospitals to predict the GIST risk category. Model 1 was the four-category risk EUS-AI model, and Model 2 was the two-category risk EUS-AI model. The diagnostic performance of the models was validated with external cohorts.

Results

A total of 1320 images (880 were very low-risk, 269 were low-risk, 68 were intermediate-risk, and 103 were high-risk) were finally chosen for building the models and test sets, and a total of 656 images (211 were very low-risk, 266 were low-risk, 88 were intermediate-risk, and 91 were high-risk) were chosen for external validation. The overall accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the four-category risk EUS-AI model in the external validation sets by tumor were 74.50%, 55.00%, 79.05%, 53.49%, and 81.63%, respectively. The accuracy, sensitivity, specificity, PPV, and NPV for the two-category risk EUS-AI model for the prediction of very low-risk GISTs in the external validation sets by tumor were 86.25%, 94.44%, 79.55%, 79.07%, and 94.59%, respectively.

Conclusion

We developed a EUS-AI model for the risk stratification of GISTs with promising results, which may complement current clinical practice in the management of GISTs.

Registration

The study has been registered in the Chinese Clinical Trial Registry (No. ChiCTR2100051191).

Development and evaluation of an artificial intelligence system for children intussusception diagnosis using ultrasound images

Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.

Artificial intelligence-assisted endoscopic ultrasound in the diagnosis of gastrointestinal stromal tumors: a meta-analysis

BACKGROUND AND AIMS

Endoscopic ultrasonography (EUS) is useful for the diagnosis of gastrointestinal stromal tumors (GISTs), but is limited by subjective interpretation. Studies on artificial intelligence (AI)-assisted diagnosis are under development. Here, we used a meta-analysis to evaluate the diagnostic performance of AI in the diagnosis of GISTs using EUS images.

METHODS

PubMed, Ovid Medline, Embase, Web of science, and the Cochrane Library databases were searched for studies based on the EUS using AI for the diagnosis of GISTs, and a meta-analysis was performed to examine the accuracy.

RESULTS

Overall, 7 studies were included in our meta-analysis. A total of 2431 patients containing more than 36,186 images were used as the overall dataset, of which 480 patients were used for the final testing. The pooled sensitivity, specificity, positive, and negative likelihood ratio (LR) of AI-assisted EUS for differentiating GISTs from other submucosal tumors (SMTs) were 0.92 (95% confidence interval [CI] 0.89-0.95), 0.82 (95% CI 0.75-0.87), 4.55 (95% CI 2.64-7.84), and 0.12 (95% CI 0.07-0.20), respectively. The summary diagnostic odds ratio (DOR) and the area under the curve were 64.70 (95% CI 23.83-175.69) and 0.950 (Q* = 0.891).

CONCLUSIONS

AI-assisted EUS showed high accuracy for the automatic endoscopic diagnosis of GISTs, which could be used as a valuable complementary method for the differentiation of SMTs in the future.

Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours

Gastrointestinal Stromal Tumors (GISTs) are subepithelial lesions (SELs) that commonly develop in the gastrointestinal tract. GISTs, unlike other SELs, can exhibit malignant behavior, so differential diagnosis is critical to the decision-making process. Endoscopic ultrasound (EUS) is considered the most accurate imaging method for diagnosing and differentiating SELs in the gastrointestinal tract by assessing the lesions precisely and evaluating their malignant risk. Due to their overlapping imaging characteristics, endosonographers may have difficulty distinguishing GISTs from other SELs using conventional EUS alone, and the collection of tissue samples from these lesions may be technically challenging. Even though it appears to be less effective in the case of smaller lesions, histology is now the gold standard for achieving a final diagnosis and avoiding unnecessary and invasive treatment for benign SELs. The use of enhanced EUS modalities and elastography has improved the diagnostic ability of EUS. Furthermore, recent advancements in artificial intelligence systems that use EUS images have allowed them to distinguish GISTs from other SELs, thereby improving their diagnostic accuracy.

Query2: Query over queries for improving gastrointestinal stromal tumour detection in an endoscopic ultrasound

Gastrointestinal stromal tumour (GIST) lesions are mesenchymal neoplasms commonly found in the upper gastrointestinal tract, but non-invasive GIST detection during an endoscopy remains challenging because their ultrasonic images resemble several benign lesions. Techniques for automatic GIST detection and other lesions from endoscopic ultrasound (EUS) images offer great potential to advance the precision and automation of traditional endoscopy and treatment procedures. However, GIST recognition faces several intrinsic challenges, including the input restriction of a single image modality and the mismatch between tasks and models. To address these challenges, we propose a novel Query² (Query over Queries) framework to identify GISTs from ultrasound images. The proposed Query² framework applies an anatomical location embedding layer to break the single image modality. A cross-attention module is then applied to query the queries generated from the basic detection head. Moreover, a single-object restricted detection head is applied to infer the lesion categories. Meanwhile, to drive this network, we present GIST514-DB, a GIST dataset that will be made publicly available, which includes the ultrasound images, bounding boxes, categories and anatomical locations from 514 cases. Extensive experiments on the GIST514-DB demonstrate that the proposed Query² outperforms most of the state-of-the-art methods.

Negligible procedure-related dissemination risk of mucosal incision-assisted biopsy for gastrointestinal stromal tumors versus endoscopic ultrasound-guided fine-needle aspiration/biopsy

BACKGROUND

Mucosal incision-assisted biopsy (MIAB) is a valuable alternative to endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNAB) for sampling gastric subepithelial lesions (SELs). This study aimed to evaluate the potential risk of dissemination and impact on postoperative prognosis associated with MIAB, which has not yet been investigated.

METHODS

Study 1: A prospective observational study was conducted to examine the presence or absence and growth rate of tumor cells in gastric juice before and after the procedure in patients with SELs who underwent MIAB (n = 25) or EUS-FNAB (n = 22) between September 2018 and August 2021. Study 2: A retrospective study was conducted to examine the impact of MIAB on postoperative prognosis in 107 patients with gastrointestinal stromal tumors diagnosed using MIAB (n = 39) or EUS-FNAB (n = 68) who underwent surgery between January 2001 and July 2020.

RESULTS

In study 1, although no tumor cells were observed in gastric juice in MIAB before the procedure, they were observed in 64% of patients after obtaining samples (P < 0.001). In contrast, no tumor cells were observed in the gastric juice in EUS-FNAB before and after the procedure. In study 2, there was no significant difference in 5-year disease-free survival between MIAB (100%) and EUS-FNAB (97.1%) (P = 0.27).

CONCLUSION

MIAB is safe, with little impact on postoperative prognosis, although the procedure releases some tumor cells after damaging the SEL's pseudocapsule.

Artificial intelligence in gastroenterology: A narrative review

Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.

Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors

Gastrointestinal stromal tumors (GISTs) are common subepithelial lesions (SELs) and require treatment considering their malignant potential. We recently developed an endoscopic ultrasound-based artificial intelligence (EUS-AI) system to differentiate GISTs from non-GISTs in gastric SELs, which were used to train the system. We assessed whether the EUS-AI system designed for diagnosing gastric GISTs could be applied to non-gastric GISTs. Between January 2015 and January 2021, 52 patients with non-gastric SELs (esophagus, n = 15; duodenum, n = 26; colon, n = 11) were enrolled. The ability of EUS-AI to differentiate GISTs from non-GISTs in non-gastric SELs was examined. The accuracy, sensitivity, and specificity of EUS-AI for discriminating GISTs from non-GISTs in non-gastric SELs were 94.4%, 100%, and 86.1%, respectively, with an area under the curve of 0.98 based on the cutoff value set using the Youden index. In the subanalysis, the accuracy, sensitivity, and specificity of EUS-AI were highest in the esophagus (100%, 100%, 100%; duodenum, 96.2%, 100%, 0%; colon, 90.9%, 100%, 0%); the cutoff values were determined using the Youden index or the value determined using stomach cases. The diagnostic accuracy of EUS-AI increased as lesion size increased, regardless of lesion location. EUS-AI based on gastric SELs had good diagnostic ability for non-gastric GISTs.

Clinical significance of 18F-FDG PET/CT imaging in 32 cases of gastrointestinal stromal tumors

Objectives

To explore the clinical significance of ¹⁸F-FDG metabolic imaging in the diagnosis and biological risk assessment of gastrointestinal stromal tumors (GIST).

Methods

This study is a clinical retrospective study. The research subjects were patients with GIST who were admitted to our hospital from January 2014 to December 2019 and underwent ¹⁸F-FDG metabolic imaging, and the relationship between biological risk and FDG metabolism was analyzed retrospectively.

Results

A total of 32 patients with GIST were included in this study, of which 17 patients had very low and low-risk lesions, and the FDG metabolism level did not increase; five patients had moderate-risk gastric lesions, and the FDG metabolism level was abnormally increased; 10 patients had high-risk lesions, and except for one patient with multiple lesions, the FDG metabolism level of these patients was increased.

Conclusions

The level of glucose metabolism is abnormally increased in tumor cells with vigorous mitosis and has higher biological risk. The ¹⁸F-FDG metabolism level can determine the biological risk of GIST and whether high-risk lesions involve other tissues and organs, as it more comprehensively reflects the distribution of lesions, the activity of tumor cells and the stage of the disease.

Differentiating Gastrointestinal Stromal Tumors from Leiomyomas Using a Neural Network Trained on Endoscopic Ultrasonography Images

BACKGROUND

Endoscopic ultrasonography (EUS) is crucial to diagnose and evaluate gastrointestinal mesenchymal tumors (GIMTs). However, EUS-guided biopsy does not always differentiate gastrointestinal stromal tumors (GISTs) from leiomyomas. We evaluated the ability of a convolutional neural network (CNN) to differentiate GISTs from leiomyomas using EUS images. The conventional EUS features of GISTs were also compared with leiomyomas.

PATIENTS AND METHODS

Patients who underwent EUS for evaluation of upper GIMTs between 2010 and 2020 were retrospectively reviewed, and 145 patients (73 women and 72 men; mean age 54.8 ± 13.5 years) with GISTs (n = 109) or leiomyomas (n = 36), confirmed by immunohistochemistry, were included. A total of 978 images collected from 100 patients were used to train and test the CNN system, and 384 images from 45 patients were used for validation. EUS images were also evaluated by an EUS expert for comparison with the CNN system.

RESULTS

The sensitivity, specificity, and accuracy of the CNN system for diagnosis of GIST were 92.0%, 64.3%, and 86.98% for the validation dataset, respectively. In contrast, the sensitivity, specificity, and accuracy of the EUS expert interpretations were 60.5%, 74.3%, and 63.0%, respectively. Concerning EUS features, only higher echogenicity was an independent and significant factor for differentiating GISTs from leiomyomas (p < 0.05).

CONCLUSIONS

The CNN system could diagnose GIMTs with higher accuracy than an EUS expert and could be helpful in differentiating GISTs from leiomyomas. A higher echogenicity may also aid in differentiation.

Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

Value of artificial intelligence with novel tumor tracking technology in the diagnosis of gastric submucosal tumors by contrast-enhanced harmonic endoscopic ultrasonography

BACKGROUND AND AIM

Contrast-enhanced harmonic endoscopic ultrasonography (CH-EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH-EUS.

METHODS

This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH-EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH-EUS videos. CH-EUS was evaluated by AI using deep learning involving a residual neural network and leave-one-out cross-validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers.

RESULTS

Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ coefficient between the two reviewers was 0.713.

CONCLUSIONS

The diagnostic ability of CH-EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.

Diagnostic accuracy of endoscopic ultrasound with artificial intelligence for gastrointestinal stromal tumors: A meta-analysis

OBJECTIVES

Gastrointestinal stromal tumors (GISTs) are thought to have a malignant potential. However, utilizing endoscopic ultrasound (EUS) to differentiate GISTs from other types of subepithelial lesions (SELs) remains challenging. Artificial intelligence (AI)-based diagnostic systems for EUS have been reported to have a promising performance, although the results of the previous studies remain controversial. In this meta-analysis, we aimed to assess the diagnostic accuracy of AI-based EUS in distinguishing GISTs from other SELs.

METHODS

A literature search was conducted on MEDLINE and EMBASE databases to identify relevant articles. The sensitivity, specificity, and area under the summary receiver operating characteristic curve (AUROC) of eligible studies were analyzed.

RESULTS

Seven studies were eligible for the final analysis. The combined sensitivity and specificity of AI-based EUS were 0.93 (95% confidence interval [CI] 0.88-0.96) and 0.78 (95% CI 0.67-0.87), respectively. The overall diagnostic odds ratio of AI-based EUS for GISTs was 36.74 (95% CI 17.69-76.30) with an AUROC of 0.94.

CONCLUSIONS

AI-based EUS showed high diagnostic ability and might help better differentiate GISTs from other SELs. More prospective studies on the diagnosis of GISTs using AI-based EUS are warranted in clinical setting.

Endoscopic management of subepithelial lesions including neuroendocrine neoplasms: European Society of Gastrointestinal Endoscopy (ESGE) Guideline

1: ESGE recommends endoscopic ultrasonography (EUS) as the best tool to characterize subepithelial lesion (SEL) features (size, location, originating layer, echogenicity, shape), but EUS alone is not able to distinguish among all types of SEL.Strong recommendation, moderate quality evidence. 2: ESGE suggests providing tissue diagnosis for all SELs with features suggestive of gastrointestinal stromal tumor (GIST) if they are of size > 20 mm, or have high risk stigmata, or require surgical resection or oncological treatment.Weak recommendation, very low quality evidence. 3: ESGE recommends EUS-guided fine-needle biopsy (EUS-FNB) or mucosal incision-assisted biopsy (MIAB) equally for tissue diagnosis of SELs ≥ 20 mm in size.Strong recommendation, moderate quality evidence. 4: ESGE recommends against surveillance of asymptomatic gastrointestinal (GI) tract leiomyomas, lipomas, heterotopic pancreas, granular cell tumors, schwannomas, and glomus tumors, if the diagnosis is clear.Strong recommendation, moderate quality evidence. 5: ESGE suggests surveillance of asymptomatic esophageal and gastric SELs without definite diagnosis, with esophagogastroduodenoscopy (EGD) at 3-6 months, and then at 2-3-year intervals for lesions < 10 mm in size, and at 1-2-year intervals for lesions 10-20 mm in size. For asymptomatic SELs > 20 mm in size that are not resected, ESGE suggests surveillance with EGD plus EUS at 6 months and then at 6-12-month intervals.Weak recommendation, very low quality evidence. 6: ESGE recommends endoscopic resection for type 1 gastric neuroendocrine neoplasms (g-NENs) if they grow larger than 10 mm. The choice of resection technique should depend on size, depth of invasion, and location in the stomach.Strong recommendation, low quality evidence. 7: ESGE suggests considering removal of histologically proven gastric GISTs smaller than 20 mm as an alternative to surveillance. The decision to resect should be discussed in a multidisciplinary meeting. The choice of technique should depend on size, location, and local expertise.Weak recommendation, very low quality evidence. 8: ESGE suggests that, to avoid unnecessary follow-up, endoscopic resection is an option for gastric SELs smaller than 20 mm and of unknown histology after failure of attempts to obtain diagnosis.Weak recommendation, very low quality evidence. 9: ESGE recommends basing the surveillance strategy on the type and completeness of resection. After curative resection of benign SELs no follow-up is advised, except for type 1 gastric NEN for which surveillance at 1-2 years is advised.Strong recommendation, low quality evidence. 10: For lower or upper GI NEN with a positive or indeterminate margin at resection, ESGE recommends repeating endoscopy at 3-6 months and another attempt at endoscopic resection in the case of residual disease.Strong recommendation, low quality evidence.

Artificial intelligence-based diagnosis of upper gastrointestinal subepithelial lesions on endoscopic ultrasonography images

BACKGROUND

Endoscopic ultrasonography (EUS) is useful for the differential diagnosis of subepithelial lesions (SELs); however, not all of them are easy to distinguish. Gastrointestinal stromal tumors (GISTs) are the commonest SELs, are considered potentially malignant, and differentiating them from benign SELs is important. Artificial intelligence (AI) using deep learning has developed remarkably in the medical field. This study aimed to investigate the efficacy of an AI system for classifying SELs on EUS images.

METHODS

EUS images of pathologically confirmed upper gastrointestinal SELs (GIST, leiomyoma, schwannoma, neuroendocrine tumor [NET], and ectopic pancreas) were collected from 12 hospitals. These images were divided into development and test datasets in the ratio of 4:1 using random sampling; the development dataset was divided into training and validation datasets. The same test dataset was diagnosed by two experts and two non-experts.

RESULTS

A total of 16,110 images were collected from 631 cases for the development and test datasets. The accuracy of the AI system for the five-category classification (GIST, leiomyoma, schwannoma, NET, and ectopic pancreas) was 86.1%, which was significantly higher than that of all endoscopists. The sensitivity, specificity, and accuracy of the AI system for differentiating GISTs from non-GISTs were 98.8%, 67.6%, and 89.3%, respectively. Its sensitivity and accuracy were significantly higher than those of all the endoscopists.

CONCLUSION

The AI system, classifying SELs, showed higher diagnostic performance than that of the experts and may assist in improving the diagnosis of SELs in clinical practice.

Artificial Intelligence in Endoscopy

Background

Owing to their rapid development, artificial intelligence (AI) technologies offer a great promise for gastroenterology practice and research. At present, AI-guided image interpretation has already been used with success for endoscopic detection of early malignant lesions. Nonetheless, there are complex challenges and possible shortcomings that must be considered before full implementation can be realized.

Summary

In this review, the current status of AI in endoscopy is summarized. Future perspectives and open questions for further studies are stressed.

Key Messages

The usage of AI algorithms for polyp detection in screening colonoscopy results in a significant increase in the adenoma detection rate, mainly attributed to the identification of diminutive polyps. Computer-aided characterization of colorectal polyps accompanies the detection, but further studies are needed to evaluate the clinical benefit. In contrast to colonoscopy, usage of AI in gastroscopy is currently rather limited. Regarding other fields of endoscopic imaging, capsule endoscopy is the ideal imaging platform for AI, due to the potential of saving time in the video analysis.

A Hybrid Machine Learning Model Based on Semantic Information Can Optimize Treatment Decision for Naïve Single 3-5-cm HCC Patients

BACKGROUND

Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment.

PURPOSE

The aim of the study was to build a hybrid machine learning model to recommend optimized first treatment (laparoscopic hepatectomy [LH] or microwave ablation [MWA]) for naïve single 3-5-cm HCC patients based on early recurrence (ER, ≤2 years) probability.

METHODS

This retrospective study collected 20 semantic variables of 582 patients (LH: 300, MWA: 282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation, and test set, respectively. Five algorithms (logistics regression, random forest, neural network, stochastic gradient boosting, and eXtreme Gradient Boosting [XGB]) were used for model building. A model with highest area under the receiver operating characteristic curve (AUC) in a validation set of LH and MWA was selected to connect as a hybrid model which made decision based on ER probability. Model testing was performed in a comprehensive set comprising LH and MWA test sets.

RESULTS

Four variables in each group were selected to build LH and MWA models, respectively. LH-XGB model (AUC = 0.744) and MWA-stochastic gradient method (AUC = 0.750) model were selected for model building. In the comprehensive set, a treatment confusion matrix was established based on recommended and actual treatment. The predicted ER probabilities were comparable with the actual ER rates for various types of patients in matrix (p > 0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH: 21.2% vs. 46.2%, p = 0.042; MWA: 26.3% vs. 54.1%, p = 0.048). By recommending optimal treatment, the hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients (p < 0.001).

CONCLUSIONS

The hybrid model can accurately predict ER probability of different treatments and thereby provide reliable evidence to make optimal treatment decision for patients with single 3-5-cm HCC.

Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images

BACKGROUND AND AIM

We aimed to develop a convolutional neural network (CNN)-based object detection model for the discrimination of gastric subepithelial tumors, such as gastrointestinal stromal tumors (GISTs), and leiomyomas, in endoscopic ultrasound (EUS) images.

METHODS

We used 376 images from 114 patients with histologically confirmed gastric GIST or leiomyoma to train the EUS-CNN. We constructed the EUS-CNN using an EfficientNet CNN model for feature extraction and a weighted bi-directional feature pyramid network for object detection. We assessed the performance of our EUS-CNN by calculating its accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC) using a validation set of 170 images from 54 patients. Four EUS experts and 15 EUS trainees were asked to judge the same validation dataset, and the diagnostic yields were compared between the EUS-CNN and human assessments.

RESULTS

In the per-image analysis, the sensitivity, specificity, accuracy, and AUC of our EUS-CNN were 95.6%, 82.1%, 91.2%, and 0.9234, respectively. In the per-patient analysis, the sensitivity, specificity, accuracy, and AUC for our object detection model were 100.0%, 85.7%, 96.3%, and 0.9929, respectively. The EUS-CNN outperformed human assessment in terms of accuracy, sensitivity, and negative predictive value.

CONCLUSIONS

We developed the EUS-CNN system, which demonstrated high diagnostic ability for gastric GIST prediction. This EUS-CNN system can be helpful not only for less-experienced endoscopists but also for experienced ones. Additional EUS image accumulation and prospective studies are required alongside validation in a large multicenter trial.

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.

Advancements in the Diagnosis of Gastric Subepithelial Tumors

A diagnosis of subepithelial tumors (SETs) is sometimes difficult due to the existence of overlying mucosa on the lesions, which hampers optical diagnosis by conventional endoscopy and tissue sampling with standard biopsy forceps. Imaging modalities, by using computed tomography and endoscopic ultrasonography (EUS) are mandatory to noninvasively collect the target's information and to opt candidates for further evaluation. Particularly, EUS is an indispensable diagnostic modality for assessing the lesions precisely and evaluating the possibility of malignancy. The diagnostic ability of EUS appears increased by the combined use of contrast-enhancement or elastography. Histology is the gold standard for obtaining the final diagnosis. Tissue sampling requires special techniques to break the mucosal barrier. Although EUS-guided fine-needle aspiration (EUS-FNA) is commonly applied, mucosal cutting biopsy and mucosal incision-assisted biopsy are comparable methods to definitively obtain tissues from the exposed surface of lesions and seem more useful than EUS-FNA for small SETs. Recent advancements in artificial intelligence (AI) have a potential to drastically change the diagnostic strategy for SETs. Development and establishment of noninvasive methods including AI-assisted diagnosis are expected to provide an alternative to invasive, histological diagnosis.

The evolving role of EUS-guided tissue acquisition

The introduction of endoscopic ultrasound-guided fine-needle aspiration into clinical practice was a pivotal moment for diagnostic gastrointestinal endoscopy. It facilitates the ease of tissue acquisition from previously inaccessible sites. The performance characteristics of cytological diagnosis are excellent. However, there remain areas of inadequacies. These include procedural inefficiencies such as the need for rapid on-site cytological evaluation or macroscopic on-site evaluation, the crucial role of histology for diagnosis in specific conditions, the issue of sampling errors and the need for repeat procedures, and the shift towards personalized medicine, which requires histology, immunohistochemical studies, and molecular analysis. The original Trucut biopsy needle had been cumbersome to use, but the recent introduction of newer-generation biopsy needles has transformed the landscape, such that there is now a greater focus on tissue acquisition for histological assessment. Concomitant technological advances of endoscopic ultrasound processors enabled higher-resolution imaging, and facilitated image enhancement using contrast harmonic endoscopic ultrasound and endoscopic ultrasound elastography. These techniques can be used as an adjunct to guide tissue acquisition in challenging situations. There is ongoing research on the use of artificial intelligence to complement diagnostic endoscopic ultrasound and the early data are promising. Artificial intelligence may be especially important to guide clinical decision-making if biopsy results are nondiagnostic.

Interobserver Reliability of Endoscopic Ultrasonography: Literature Review

Endoscopic ultrasonography (EUS) has been applied to the diagnosis of various digestive disorders. Although it has been widely accepted and its diagnostic value is high, the dependence of EUS diagnosis on image interpretation done by the endosonographer has persisted as an important difficulty. Consequently, high interobserver reliability (IOR) in EUS diagnosis is important to demonstrate the reliability of EUS diagnosis. We reviewed the literature on the IOR of EUS diagnosis for various diseases such as chronic pancreatitis, pancreatic solid/cystic mass, lymphadenopathy, and gastrointestinal and subepithelial lesions. The IOR of EUS diagnosis differs depending on the disease; moreover, EUS findings with high IOR and those with IOR that was not necessarily high were used as diagnostic criteria. Therefore, to further increase the value of EUS diagnosis, EUS diagnostic criteria with high diagnostic characteristics based on EUS findings with high IOR must be established.