Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas. Clin Transl Gastroenterol. 2019;10:1-8. [PMID: 31117111 PMCID: PMC6602761 DOI: 10.14309/ctg.0000000000000045]

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.

Improvements and future perspective in diagnostic tools for neuroendocrine neoplasms

INTRODUCTION

Neuroendocrine neoplasms (NENs) represent a complex group of tumors arising from neuroendocrine cells, characterized by heterogeneous behavior and challenging diagnostics. Despite advancements in medical technology, NENs present a major challenge in early detection, often leading to delayed diagnosis and variable outcomes. This review aims to provide an in-depth analysis of current diagnostic methods as well as the evolving and future directions of diagnostic strategies for NENs.

AREA COVERED

The review extensively covers the evolution of diagnostic tools for NENs, from traditional imaging and biochemical tests to advanced genomic profiling and next-generation sequencing. The emerging role of technologies such as artificial intelligence, machine learning, and liquid biopsies could improve diagnostic precision, as could the integration of imaging modalities such as positron emission tomography (PET)/magnetic resonance imaging (MRI) hybrids and innovative radiotracers.

EXPERT OPINION

Despite progress, there is still a significant gap in the early diagnosis of NENs. Bridging this diagnostic gap and integrating advanced technologies and precision medicine are crucial to improving patient outcomes. However, challenges such as low clinical awareness, limited possibility of noninvasive diagnostic tools and funding limitations for rare diseases like NENs are acknowledged.

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.

SSM-Net: Semi-supervised multi-task network for joint lesion segmentation and classification from pancreatic EUS images

Pancreatic cancer does not show specific symptoms, which makes the diagnosis of early stages difficult with established image-based screening methods and therefore has the worst prognosis among all cancers. Although endoscopic ultrasonography (EUS) has a key role in diagnostic algorithms for pancreatic diseases, B-mode imaging of the pancreas can be affected by confounders such as chronic pancreatitis, which can make both pancreatic lesion segmentation and classification laborious and highly specialized. To address these challenges, this work proposes a semi-supervised multi-task network (SSM-Net) to leverage unlabeled and labeled EUS images for joint pancreatic lesion classification and segmentation. Specifically, we first devise a saliency-aware representation learning module (SRLM) on a large number of unlabeled images to train a feature extraction encoder network for labeled images by computing a contrastive loss with a semantic saliency map, which is obtained by our spectral residual module (SRM). Moreover, for labeled EUS images, we devise channel attention blocks (CABs) to refine the features extracted from the pre-trained encoder on unlabeled images for segmenting lesions, and then devise a merged global attention module (MGAM) and a feature similarity loss (FSL) for obtaining a lesion classification result. We collect a large-scale EUS-based pancreas image dataset (LS-EUSPI) consisting of 9,555 pathologically proven labeled EUS images (499 patients from four categories) and 15,500 unlabeled EUS images. Experimental results on the LS-EUSPI dataset and a public thyroid gland lesion dataset show that our SSM-Net clearly outperforms state-of-the-art methods.

Advancements in biliopancreatic endoscopy: a comprehensive review of artificial intelligence in EUS and ERCP

The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

Computed tomography-based prediction of pancreatitis following biliary metal stent placement with the convolutional neural network

Background and study aims Pancreatitis is a potentially lethal adverse event of endoscopic transpapillary placement of a self-expandable metal stent (SEMS) for malignant biliary obstruction (MBO). Deep learning-based image recognition has not been investigated in predicting pancreatitis in this setting. Patients and methods We included 70 patients who underwent endoscopic placement of a SEMS for nonresectable distal MBO. We constructed a convolutional neural network (CNN) model for pancreatitis prediction using a series of pre-procedure computed tomography images covering the whole pancreas (≥ 120,960 augmented images in total). We examined the additional effects of the CNN-based probabilities on the following machine learning models based on clinical parameters: logistic regression, support vector machine with a linear or RBF kernel, random forest classifier, and gradient boosting classifier. Model performance was assessed based on the area under the curve (AUC) in the receiver operating characteristic analysis, positive predictive value (PPV), accuracy, and specificity. Results The CNN model was associated with moderate levels of performance metrics: AUC, 0.67; PPV, 0.45; accuracy, 0.66; and specificity, 0.63. When added to the machine learning models, the CNN-based probabilities increased the performance metrics. The logistic regression model with the CNN-based probabilities had an AUC of 0.74, PPV of 0.85, accuracy of 0.83, and specificity of 0.96, compared with 0.72, 0.78, 0.77, and 0.96, respectively, without the probabilities. Conclusions The CNN-based model may increase predictability for pancreatitis following endoscopic placement of a biliary SEMS. Our findings support the potential of deep learning technology to improve prognostic models in pancreatobiliary therapeutic endoscopy.

Early detection of pancreatic cancer in the era of precision medicine

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer

RATIONALE AND OBJECTIVES

According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features.

MATERIALS AND METHODS

Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort.

RESULTS

Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity.

CONCLUSION

Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

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.

Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning

Objectives

Localized autoimmune pancreatitis is difficult to differentiate from pancreatic ductal adenocarcinoma on endoscopic ultrasound images. In recent years, deep learning methods have improved the diagnosis of diseases. Hence, we developed a special cross-validation framework to search for effective methodologies of deep learning in distinguishing autoimmune pancreatitis from pancreatic ductal adenocarcinoma on endoscopic ultrasound images.

Methods

Data from 24 patients diagnosed with localized autoimmune pancreatitis (8751 images) and 61 patients diagnosed with pancreatic ductal adenocarcinoma (20,584 images) were collected from 2016 to 2022. We applied transfer learning to a convolutional neural network called ResNet152, together with our innovative imaging method contributing to data augmentation and temporal data process. We divided patients into five groups according to different factors for 5-fold cross-validation, where the ordered and balanced datasets were created for the performance evaluations.

Results

ResNet152 surpassed the endoscopists in all evaluation metrics with almost all datasets. Interestingly, when the dataset is balanced according to the factor of the endoscopists' diagnostic accuracy, the area under the receiver operating characteristic curve and accuracy were highest at 0.85 and 0.80, respectively.

Conclusions

It is deduced that image features useful for ResNet152 correlate with those used by endoscopists for their diagnoses. This finding may contribute to sample-efficient dataset preparation to train convolutional neural networks for endoscopic ultrasonography-imaging diagnosis.

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.

Association of high-risk stigmata and worrisome features with advanced neoplasia in intraductal papillary mucinous neoplasms (IPMN): A systematic review

BACKGROUND

This systematic review aimed to assess the diagnostic accuracy of the International Consensus Fukuoka Guidelines (ICG2017) in identifying high-risk lesions of Intraductal Papillary Mucinous Neoplasms (IPMNs).

METHODS

The ICG2017 revision committee conducted a comprehensive literature review to establish evidence-based statements on IPMNs. The review focused on articles examining the diagnostic value of imaging features (e.g., cyst or main pancreatic duct diameter), clinical symptoms associated with IPMN, and serum biomarkers. Five clinical questions regarding high-risk stigmata (HRS) and worrisome features (WF) in the ICG2017 guidelines were addressed.

RESULTS

A total of 210 articles were reviewed. The findings revealed a significant association between the presence of mural nodules ≥5 mm in diameter or solid components with contrast enhancement and the diagnosis of high-grade dysplasia or invasive carcinoma. Contrast-enhanced diagnostic tools, such as CT, MRI, or EUS, demonstrated the highest prediction rate and were recommended. Positive cytology was identified as an HRS, while symptoms like acute pancreatitis and cyst diameter growth ≥2.5 mm per year were considered WFs. The use of nomograms and multiple diagnostic factors was recommended for optimal IPMN management.

CONCLUSIONS

This systematic review provides evidence supporting the improved diagnostic accuracy of ICG2017 in identifying high-risk lesions of IPMN. The multidisciplinary incorporation of HRS and WF based on imaging findings and clinical symptoms is crucial. These findings should inform the revision of ICG2017, enhancing the evaluation and management of IPMN patients. By implementing these recommendations, clinicians can make more informed decisions, leading to better diagnosis and treatment outcomes for high-risk IPMN cases.

Deep Learning Applications in Pancreatic Cancer

Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.

Care Delivery Interventions for Individuals with Cancer: A Literature Review and Focus on Gastrointestinal Malignancies

BACKGROUND

Gastrointestinal malignancies represent a particularly challenging condition, often requiring a multidisciplinary approach to management in order to meet the unique needs of these individuals and their caregivers.

PURPOSE

In this literature review, we sought to describe care delivery interventions that strive to improve the quality of life and care for patients with a focus on gastrointestinal malignancies.

CONCLUSION

We highlight patient-centered care delivery interventions, including patient-reported outcomes, hospital-at-home interventions, and other models of care for individuals with cancer. By demonstrating the relevance and utility of these different care models for patients with gastrointestinal malignancies, we hope to highlight the importance of developing and testing new interventions to address the unique needs of this population.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.

Bronchial balloon dilatation combined with cryotherapy for tuberculous cicatricial central airway stenosis, with Adobe Photoshop for the degree measurement: A multicenter, retrospective study

Objective

To explore bronchial balloon dilation (BD) combined with cryotherapy and Photoshop (PS)-base image processing technology in the interventional treatment of tuberculous cicatricial central airway stenosis (CCAS).

Methods

This multicenter, retrospective study analyzed the clinical data of patients with CCAS from six hospitals in Henan, China between June 2019 and October 2022.

Results

A total of 307 patients were included, including 152 (62 males) treated with BD alone and 155 (54 males) treated with BD combined with cryotherapy. One month after treatment, compared with the BD group, the total response rate [133 (85.8) vs. 105 (69.1), P < 0.001] and occurrence of restenosis at 1-monthaftertreatment [22 (14.2 %) vs. 47 (30.9 %), P < 0.001] in BD & cryotherapy group were significantly higher; furthermore, the inner diameter of the airway stenosis (8.1 ± 0.9 mm vs. 6.4 ± 1.5 mm, P < 0.001), the cross-sectional area of the airway stenosis (51.6 ± 7.8 mm2 vs. 33.1 ± 11.6 mm2, P < 0.001), FEV1 (5.26 ± 0.42 L vs. 4.32 ± 0.31 L, P < 0.001), and PEF (5.72 ± 0.36 L/s vs. 4.56 ± 0.42 L/s, P < 0.001) in BD & cryotherapy group showed significantly more improvements. Moreover, Pearson's correlation analysis showed significantly strong positive correlation (r = 0.818，P < 0.001) between the cross-sectional area of airway stenosis measured by PS-base image processing technology and the inner diameter of airway stenosis measured by chest CT.

Conclusion

BD combined with cryotherapy for the interventional treatment of CCAS might be superior to BD alone. PS-base image processing technology might be used as a supplement strategy for measuring the degree of central airway stenosis.

The adoption of artificial intelligence assisted endoscopy in the Middle East: challenges and future potential

The use of artificial intelligence (AI) in endoscopy has shown immense potential to enhance diagnostic accuracy, streamline procedures, and improve patient outcomes. There are potential uses in every field of endoscopy, from improving adenoma detection rate (ADR) in colonoscopy to reducing read time in capsule endoscopy or minimizing blind spots in gastroscopy. Indeed, some of these systems are already licensed and in commercial use across the world. In the Middle East, where healthcare systems are rapidly evolving, there is a growing interest in adopting AI technologies to revolutionise endoscopic practices. This article provides an overview of the advancements, potential opportunities and challenges associated with the implementation of AI in endoscopy within the Middle East region. Our aim is to contribute to the ongoing dialogue surrounding the implementation of AI in endoscopy and consider some of the factors that are particularly relevant in the Middle Eastern context, including the need to train the models for local populations, cost and training, as well as trying to ensure equity of access for patients. It provides valuable insights for healthcare professionals, policymakers, and researchers interested in leveraging AI to enhance endoscopic procedures, improve patient care, and address the unique healthcare needs of the Middle East population.

Radiomics Boosts Deep Learning Model for IPMN Classification

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.

A primer on artificial intelligence in pancreatic imaging

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

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.

A comparative study between computed tomography and endoscopic ultrasound in the detection of a mural nodule in intraductal papillary mucinous neoplasm -Multicenter observational study in Japan

BACKGROUND/OBJECTIVES

The detection of malignancy is a major concern in the management of intraductal papillary mucinous neoplasm (IPMN). The height of the mural nodule (MN), estimated using endoscopic ultrasound (EUS) and computed tomography (CT), has been considered crucial for predicting malignant IPMN. Currently, whether surveillance using CT or EUS alone is sufficient for detecting MNs remains unclear. This study aimed to compare the ability of CT and EUS to detect MNs in IPMN.

METHODS

This multicenter, retrospective observational study was conducted in 11 Japanese tertiary institutions. Patients who underwent surgical resection of IPMN with MN after CT and EUS examinations were eligible to participate. The MN detection rates between CT and EUS were examined.

RESULTS

Two-hundred-and-forty patients who underwent preoperative EUS and CT had pathologically confirmed MNs. The MN detection rates of EUS and CT were 83% and 53%, respectively (p < 0.001). Additionally, the MN detection rate of EUS was significantly higher than that of CT regardless of morphological type (76% vs. 47% in branch-duct-type IPMN; 90% vs. 54% in mixed IPMN; 98% vs. 56% in main-duct-type IPMN; p < 0.001). Further, pathologically confirmed MNs ≥5 mm were more frequently observed on EUS than on CT (95% vs. 76%, p < 0.001).

CONCLUSIONS

EUS was superior to CT for the detection of MN in IPMN. EUS surveillance is essential for the detection of MNs.

Spatiotemporal analysis of small bowel capsule endoscopy videos for outcomes prediction in Crohn's disease

Background

Deep learning techniques can accurately detect and grade inflammatory findings on images from capsule endoscopy (CE) in Crohn's disease (CD). However, the predictive utility of deep learning of CE in CD for disease outcomes has not been examined.

Objectives

We aimed to develop a deep learning model that can predict the need for biological therapy based on complete CE videos of newly-diagnosed CD patients.

Design

This was a retrospective cohort study. The study cohort included treatment-naïve CD patients that have performed CE (SB3, Medtronic) within 6 months of diagnosis. Complete small bowel videos were extracted using the RAPID Reader software.

Methods

CE videos were scored using the Lewis score (LS). Clinical, endoscopic, and laboratory data were extracted from electronic medical records. Machine learning analysis was performed using the TimeSformer computer vision algorithm developed to capture spatiotemporal characteristics for video analysis.

Results

The patient cohort included 101 patients. The median duration of follow-up was 902 (354-1626) days. Biological therapy was initiated by 37 (36.6%) out of 101 patients. TimeSformer algorithm achieved training and testing accuracy of 82% and 81%, respectively, with an Area under the ROC Curve (AUC) of 0.86 to predict the need for biological therapy. In comparison, the AUC for LS was 0.70 and for fecal calprotectin 0.74.

Conclusion

Spatiotemporal analysis of complete CE videos of newly-diagnosed CD patients achieved accurate prediction of the need for biological therapy. The accuracy was superior to that of the human reader index or fecal calprotectin. Following future validation studies, this approach will allow for fast and accurate personalization of treatment decisions in CD.

Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study

The aim of this study was to develop a novel deep learning (DL) model without requiring large-annotated training datasets for detecting pancreatic cancer (PC) using computed tomography (CT) images. This retrospective diagnostic study was conducted using CT images collected from 2004 and 2019 from 4287 patients diagnosed with PC. We proposed a self-supervised learning algorithm (pseudo-lesion segmentation (PS)) for PC classification, which was trained with and without PS and validated on randomly divided training and validation sets. We further performed cross-racial external validation using open-access CT images from 361 patients. For internal validation, the accuracy and sensitivity for PC classification were 94.3% (92.8-95.4%) and 92.5% (90.0-94.4%), and 95.7% (94.5-96.7%) and 99.3 (98.4-99.7%) for the convolutional neural network (CNN) and transformer-based DL models (both with PS), respectively. Implementing PS on a small-sized training dataset (randomly sampled 10%) increased accuracy by 20.5% and sensitivity by 37.0%. For external validation, the accuracy and sensitivity were 82.5% (78.3-86.1%) and 81.7% (77.3-85.4%) and 87.8% (84.0-90.8%) and 86.5% (82.3-89.8%) for the CNN and transformer-based DL models (both with PS), respectively. PS self-supervised learning can increase DL-based PC classification performance, reliability, and robustness of the model for unseen, and even small, datasets. The proposed DL model is potentially useful for PC diagnosis.

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

Deep-Learning-Enabled Computer-Aided Diagnosis in the Classification of Pancreatic Cystic Lesions on Confocal Laser Endomicroscopy

Accurate classification of pancreatic cystic lesions (PCLs) is important to facilitate proper treatment and to improve patient outcomes. We utilized the convolutional neural network (CNN) of VGG19 to develop a computer-aided diagnosis (CAD) system in the classification of subtypes of PCLs in endoscopic ultrasound-guided needle-based confocal laser endomicroscopy (nCLE). From a retrospectively collected 22,424 nCLE video frames (50 videos) as the training/validation set and 11,047 nCLE video frames (18 videos) as the test set, we developed and compared the diagnostic performance of three CNNs with distinct methods of designating the region of interest. The diagnostic accuracy for subtypes of PCLs by CNNs with manual, maximal rectangular, and U-Net algorithm-designated ROIs was 100%, 38.9%, and 66.7% on a per-video basis and 88.99%, 73.94%, and 76.12% on a per-frame basis, respectively. Our per-frame analysis suggested differential levels of diagnostic accuracy among the five subtypes of PCLs, where non-mucinous PCLs (serous cystic neoplasm: 93.11%, cystic neuroendocrine tumor: 84.31%, and pseudocyst: 98%) had higher diagnostic accuracy than mucinous PCLs (intraductal papillary mucinous neoplasm: 84.43% and mucinous cystic neoplasm: 86.1%). Our CNN demonstrated superior specificity compared to the state-of-the-art for the classification of mucinous PCLs (IPMN and MCN), with high specificity (94.3% and 92.8%, respectively) but low sensitivity (46% and 45.2%, respectively). This suggests the complimentary role of CNN-enabled CAD systems, especially for clinically suspected mucinous PCLs.

Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

Pancreatic cancer (PC) has a low incidence rate but a high mortality, with patients often in the advanced stage of the disease at the time of the first diagnosis. If detected, early neoplastic lesions are ideal for surgery, offering the best prognosis. Preneoplastic lesions of the pancreas include pancreatic intraepithelial neoplasia and mucinous cystic neoplasms, with intraductal papillary mucinous neoplasms being the most commonly diagnosed. Our study focused on predicting PC by identifying early signs using noninvasive techniques and artificial intelligence (AI). A systematic English literature search was conducted on the PubMed electronic database and other sources. We obtained a total of 97 studies on the subject of pancreatic neoplasms. The final number of articles included in our study was 44, 34 of which focused on the use of AI algorithms in the early diagnosis and prediction of pancreatic lesions. AI algorithms can facilitate diagnosis by analyzing massive amounts of data in a short period of time. Correlations can be made through AI algorithms by expanding image and electronic medical records databases, which can later be used as part of a screening program for the general population. AI-based screening models should involve a combination of biomarkers and medical and imaging data from different sources. This requires large numbers of resources, collaboration between medical practitioners, and investment in medical infrastructures.

Potential of artificial intelligence in the risk stratification for and early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is the third most lethal cancer in the United States, with a 5-year life expectancy of 11%. Most symptoms manifest at an advanced stage of the disease when surgery is no longer appropriate. The dire prognosis of PDAC warrants new strategies to improve the outcomes of patients, and early detection has garnered significant attention. However, early detection of PDAC is most often incidental, emphasizing the importance of developing new early detection screening strategies. Due to the low incidence of the disease in the general population, much of the focus for screening has turned to individuals at high risk of PDAC. This enriches the screening population and balances the risks associated with pancreas interventions. The cancers that are found in these high-risk individuals by MRI and/or EUS screening show favorable 73% 5-year overall survival. Even with the emphasis on screening in enriched high-risk populations, only a minority of incident cancers are detected this way. One strategy to improve early detection outcomes is to integrate artificial intelligence (AI) into biomarker discovery and risk models. This expert review summarizes recent publications that have developed AI algorithms for the applications of risk stratification of PDAC using radiomics and electronic health records. Furthermore, this review illustrates the current uses of radiomics and biomarkers in AI for early detection of PDAC. Finally, various challenges and potential solutions are highlighted regarding the use of AI in medicine for early detection purposes.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review

BACKGROUND AND AIMS

Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases.

METHODS

Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist.

RESULTS

A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points.

CONCLUSIONS

DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

The effectiveness of endoscopic ultrasonography findings to distinguish benign and malignant intraductal papillary mucinous neoplasm

BACKGROUND AND AIMS

Accurate evaluation of intraductal papillary mucinous neoplasm (IPMN) is necessary to inform clinical decision-making. But it is still difficult to distinguish benign and malignant IPMN preoperatively. This study aims to evaluate the utility of EUS to predict the pathology of IPMN.

METHODS

Patients with IPMN who underwent endoscopic ultrasound within 3 months before surgery were collected from six centers. Logistic regression model and random forest model were used to determine risk factors associated with malignant IPMN. In both models, 70% and 30% of patients were randomly assigned to the exploratory group and validation group, respectively. Sensitivity, specificity, and ROC were used in model assessment.

RESULTS

Of the 115 patients, 56 (48.7%) had low-grade dysplasia (LGD), 25 (21.7%) had high-grade dysplasia (HGD), and 34 (29.6%) had invasive cancer (IC). Smoking history (OR = 6.95, 95%CI: 1.98-24.44, p = 0.002), lymphadenopathy (OR = 7.91, 95%CI: 1.60-39.07, p = 0.011), MPD > 7 mm (OR = 4.75, 95%CI: 1.56-14.47, p = 0.006) and mural nodules > 5 mm (OR = 8.79, 95%CI: 2.40-32.24, p = 0.001) were independent risk factors predicting malignant IPMN according to the logistic regression model. The sensitivity, specificity, and AUC were 0.895, 0.571, and 0.795 in the validation group. In the random forest model, the sensitivity, specificity, and AUC were 0.722, 0.823, and 0.773, respectively. In patients with mural nodules, random forest model could reach a sensitivity of 0.905 and a specificity of 0.900.

CONCLUSIONS

Using random forest model based on EUS data is effective to differentiate benign and malignant IPMN in this cohort, especially in patients with mural nodules.

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.

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs): an MRI-based radiomic model to determine the malignant degeneration potential

BACKGROUND

Branch duct-intraductal papillary mucinous neoplasms (BD-IPMNs) are the most common pancreatic cystic tumors and have a low risk of malignant transformation. Features able to early identify high-risk BD-IPMNs are lacking, and guidelines currently rely on the occurrence of worrisome features (WF) and high-risk stigmata (HRS).

AIM

In our study, we aimed to use a magnetic resonance imaging (MRI) radiomic model to identify features linked to a higher risk of malignant degeneration, and whether these appear before the occurrence of WF and HRS.

METHODS

We retrospectively evaluated adult patients with a known BD-IPMN who had had at least two contrast-enhanced MRI studies at our center and a 24-month minimum follow-up time. MRI acquisition protocol for the two examinations included pre- and post-contrast phases and diffusion-weighted imaging (DWI)/apparent diffusion coefficient (ADC) map. Patients were divided into two groups according to the development of WF or HRS at the end of the follow-up (Group 0 = no WF or HRS; Group 1 = WF or HRS). We segmented the MRI images and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model.

RESULTS

We included 50 patients: 31 in Group 0 and 19 in Group 1. No patients in this cohort developed HRS. At baseline, 47, 67, 38, and 68 SF were identified for pre-contrast T1-weighted (T1-W) sequence, post-contrast T1-W sequence, T2-weighted (T2- W) sequence, and ADC map, respectively. At the end of follow-up, we found 69, 78, 53, and 91 SF, respectively. The radiomic-based predictive model identified 16 SF: more particularly, 5 SF for pre-contrast T1-W sequence, 6 for post-contrast T1-W sequence, 3 for T2-W sequence, and 2 for ADC.

CONCLUSION

We identified radiomic features that correlate significantly with WF in patients with BD-IPMNs undergoing contrast-enhanced MRI. Our MRI-based radiomic model can predict the occurrence of WF.

Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers

Diagnosing distal bile duct obstruction remains challenging. This study aimed to examine the diagnostic ability of artificial intelligence (AI) based on clinical biomarkers in diagnosing malignant distal bile duct obstruction. A total of 206 patients with distal bile duct obstruction were included in this study. Clinical laboratory parameters were collected from the patients and evaluated using AI. All clinical parameters were input into the AI algorithm, and the AI value for malignant distal bile duct obstruction was calculated. The benign and malignant diagnostic capabilities of AI and other factors (alkaline phosphatase [ALP], intrahepatic bile duct [IHBD] diameters, and total bile duct [CBD] diameters) were compared. Benign and malignant bile duct obstruction were diagnosed in 142 and 64 patients, respectively. The median AI value of malignant distal bile duct obstruction was significantly greater than that of benign distal bile duct obstruction (0.991 vs. 0.002, p < 0.001). The area under the receiver operating characteristic curve of AI, ALP, IHBD diameter, and CBD diameter were 0.908, 0.795, 0.794, and 0.775, respectively. AI showed a sensitivity, specificity, and accuracy of 83.1%, 87.2%, and 85.9%. AI-based on clinical biomarkers could serve as an auxiliary for diagnosing malignant bile duct obstruction.

Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning

BACKGROUND

 Risk stratification and recommendation for surgery for intraductal papillary mucinous neoplasm (IPMN) are currently based on consensus guidelines. Risk stratification from presurgery histology is only potentially decisive owing to the low sensitivity of fine-needle aspiration. In this study, we developed and validated a deep learning-based method to distinguish between IPMN with low grade dysplasia and IPMN with high grade dysplasia/invasive carcinoma using endoscopic ultrasound (EUS) images.

METHODS

 For model training, we acquired a total of 3355 EUS images from 43 patients who underwent pancreatectomy from March 2015 to August 2021. All patients had histologically proven IPMN. We used transfer learning to fine-tune a convolutional neural network and to classify "low grade IPMN" from "high grade IPMN/invasive carcinoma." Our test set consisted of 1823 images from 27 patients, recruiting 11 patients retrospectively, 7 patients prospectively, and 9 patients externally. We compared our results with the prediction based on international consensus guidelines.

RESULTS

 Our approach could classify low grade from high grade/invasive carcinoma in the test set with an accuracy of 99.6 % (95 %CI 99.5 %-99.9 %). Our deep learning model achieved superior accuracy in prediction of the histological outcome compared with any individual guideline, which have accuracies between 51.8 % (95 %CI 31.9 %-71.3 %) and 70.4 % (95 %CI 49.8-86.2).

CONCLUSION

 This pilot study demonstrated that deep learning in IPMN-EUS images can predict the histological outcome with high accuracy.

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses

BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]). METHODS : Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated. RESULTS : 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84-0.97), 0.94 (0.88-0.98), 0.82 (0.68-0.92), and 0.91 (0.85-0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90-0.99), PASC 1.00 (0.05-1.00), ACC 1.00 (0.22-1.00), MPT 0.33 (0.01-0.91), NEC 1.00 (0.22-1.00), NET 0.93 (0.66-1.00), SPN 1.00 (0.22-1.00), chronic pancreatitis 0.78 (0.52-0.94), and AIP 0.73 (0.39-0.94). CONCLUSIONS : Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.

Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis

BACKGROUND AND AIMS

Distinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning-based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS-guided fine-needle aspiration (EUS-FNA) in real time.

METHODS

This is a prospective study. The CH-EUS MASTER system is composed of Model 1 (real-time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH-EUS examinations followed by EUS-FNA were recruited. All patients underwent CH-EUS and were diagnosed both by endoscopists and CH-EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS-FNA with or without CH-EUS MASTER guidance.

RESULTS

Compared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH-EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH-EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH-EUS MASTER guided EUS-FNA, and were not significantly different compared to the control group. CH-EUS MASTER-guided EUS-FNA significantly improved the first-pass diagnostic yield.

CONCLUSION

CH-EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time.

TRIAL REGISTRATION NUMBER

NCT04607720.

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.

Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?

Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.

Key research questions for implementation of artificial intelligence in capsule endoscopy

BACKGROUND

Artificial intelligence (AI) is rapidly infiltrating multiple areas in medicine, with gastrointestinal endoscopy paving the way in both research and clinical applications. Multiple challenges associated with the incorporation of AI in endoscopy are being addressed in recent consensus documents.

OBJECTIVES

In the current paper, we aimed to map future challenges and areas of research for the incorporation of AI in capsule endoscopy (CE) practice.

DESIGN

Modified three-round Delphi consensus online survey.

METHODS

The study design was based on a modified three-round Delphi consensus online survey distributed to a group of CE and AI experts. Round one aimed to map out key research statements and challenges for the implementation of AI in CE. All queries addressing the same questions were merged into a single issue. The second round aimed to rank all generated questions during round one and to identify the top-ranked statements with the highest total score. Finally, the third round aimed to redistribute and rescore the top-ranked statements.

RESULTS

Twenty-one (16 gastroenterologists and 5 data scientists) experts participated in the survey. In the first round, 48 statements divided into seven themes were generated. After scoring all statements and rescoring the top 12, the question of AI use for identification and grading of small bowel pathologies was scored the highest (mean score 9.15), correlation of AI and human expert reading-second (9.05), and real-life feasibility-third (9.0).

CONCLUSION

In summary, our current study points out a roadmap for future challenges and research areas on our way to fully incorporating AI in CE reading.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

Ultrasound-based artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

Deep Learning for Automatic Differentiation of Mucinous versus Non-Mucinous Pancreatic Cystic Lesions: A Pilot Study

Endoscopic ultrasound (EUS) morphology can aid in the discrimination between mucinous and non-mucinous pancreatic cystic lesions (PCLs) but has several limitations that can be overcome by artificial intelligence. We developed a convolutional neural network (CNN) algorithm for the automatic diagnosis of mucinous PCLs. Images retrieved from videos of EUS examinations for PCL characterization were used for the development, training, and validation of a CNN for mucinous cyst diagnosis. The performance of the CNN was measured calculating the area under the receiving operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. A total of 5505 images from 28 pancreatic cysts were used (3725 from mucinous lesions and 1780 from non-mucinous cysts). The model had an overall accuracy of 98.5%, sensitivity of 98.3%, specificity of 98.9% and AUC of 1. The image processing speed of the CNN was 7.2 ms per frame. We developed a deep learning algorithm that differentiated mucinous and non-mucinous cysts with high accuracy. The present CNN may constitute an important tool to help risk stratify PCLs.

Deep learning-based diagnosis from endobronchial ultrasonography images of pulmonary lesions

Endobronchial ultrasonography with a guide sheath (EBUS-GS) improves the accuracy of bronchoscopy. The possibility of differentiating benign from malignant lesions based on EBUS findings may be useful in making the correct diagnosis. The convolutional neural network (CNN) model investigated whether benign or malignant (lung cancer) lesions could be predicted based on EBUS findings. This was an observational, single-center cohort study. Using medical records, patients were divided into benign and malignant groups. We acquired EBUS data for 213 participants. A total of 2,421,360 images were extracted from the learning dataset. We trained and externally validated a CNN algorithm to predict benign or malignant lung lesions. Test was performed using 26,674 images. The dataset was interpreted by four bronchoscopists. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CNN model for distinguishing benign and malignant lesions were 83.4%, 95.3%, 53.6%, 83.8%, and 82.0%, respectively. For the four bronchoscopists, the accuracy rate was 68.4%, sensitivity was 80%, specificity was 39.6%, PPV was 76.8%, and NPV was 44.2%. The developed EBUS-computer-aided diagnosis system is expected to read EBUS findings that are difficult for clinicians to judge with precision and help differentiate between benign lesions and lung cancers.

Artificial intelligence for the detection of pancreatic lesions

PURPOSE

Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions.

METHODS

We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs).

RESULTS

We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer.

CONCLUSION

AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists' tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.

Robust Descriptor of Pancreatic Tissue for Automatic Detection of Pancreatic Cancer in Endoscopic Ultrasonography

Pancreatic cancer (PC) has a reported mortality of 98% and a 5-y survival rate of 6.7%. Experienced gastroenterologists detect 80% of those with early-stage PC by endoscopic ultrasonography (EUS). Here we propose an automatic second reader strategy to detect PC in an entire EUS procedure, rather than focusing on pre-selected frames, as the state-of-the-art methods do. The method unmasks echo tumoral patterns in frames with a high probability of tumor. First, speeded up robust features define a set of interest points with correlated heterogeneities among different filtering scales. Afterward, intensity gradients of each interest point are summarized by 64 features at certain locations and scales. A frame feature vector is built by concatenating statistics of each feature of the 15 groups of scales. Then, binary classification is performed by Support Vector Machine and Adaboost models. Evaluation was performed using a data set comprising 55 participants, 18 of PC class (16,585 frames) and 37 subjects of non-PC class (49,664 frames), randomly splitting 10 times. The proposed method reached an accuracy of 92.1%, sensitivity of 96.3% and specificity of 87.8.3%. The observed results are also stable in noisy experiments while deep learning approaches fail to maintain similar performance.

Advances in the Diagnosis of Pancreatic Cystic Lesions

Pancreatic cystic lesions (PCLs) are a heterogenous group of lesions ranging from benign to malignant. There has been an increase in PCLs prevalence in recent years, mostly due to advances in imaging techniques, increased awareness of their existence and population aging. Reliable discrimination between neoplastic and non-neoplastic cystic lesions is paramount to ensuring adequate treatment and follow-up. Although conventional diagnostic techniques such as ultrasound (US), magnetic resonance imaging (MRI) and computer tomography (CT) can easily identify these lesions, assessing the risk of malignancy is limited. Endoscopic ultrasound (EUS) is superior to cross-sectional imaging in identifying potentially malignant lesions due to its high resolution and better imaging characteristics, and the advantage of allowing for cyst fluid sampling via fine-needle aspiration (FNA). More complex testing, such as cytological and histopathological analysis and biochemical and molecular testing of the aspirated fluid, can ensure an accurate diagnosis.