A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound

A deep learning-based, real-time image report system for linear EUS

BACKGROUND AND AIMS

The integrity of image acquisition is critical for biliopancreatic EUS reporting, significantly affecting the quality of EUS examinations and disease-related decision-making. However, the quality of EUS reports varies among endoscopists. To address this issue, we developed a deep learning-based EUS automatic image report system (EUS-AIRS), aiming to achieve automatic photodocumentation in real-time during EUS, including capturing standard stations, lesions, and puncture procedures.

METHODS

Eight deep learning models trained and tested using 235,784 images were integrated to construct the EUS-AIRS. The performance of EUS-AIRS was tested through man-machine comparisons at 2 levels: a retrospective test (include internal and external testing) and a prospective test. From May 2023 to October 2023, a total of 114 patients undergoing EUS at Renmin Hospital of Wuhan University were consecutively recruited for prospective testing. The primary outcome was the completeness of the EUS-AIRS for capturing standard stations.

RESULTS

In terms of completeness in capturing biliopancreatic standard stations, EUS-AIRS exceeded the capabilities of endoscopists at all levels of expertise in retrospective internal testing (90.8% [95% confidence interval (CI), 88.7%-92.9%] vs 70.5% [95% CI, 67.2%-73.8%]; P < .001) and external testing (91.4% [95% CI, 88.4%-94.4%] vs 68.2% [95% CI, 63.3%-73.2%]; P < .001). EUS-AIRS exhibited high accuracy and completeness in capturing standard station images. The completeness of the EUS-AIRS significantly outperformed manual endoscopist reports (91.4% [95% CI, 89.4%-93.4%] vs 78.1% [95% CI, 75.1%-81.0%); P < .001).

CONCLUSIONS

EUS-AIRS exhibits exceptional capabilities in real-time, capturing high-quality and high-integrity biliopancreatic EUS images. This showcases the potential of applying an artificial intelligence image report system in the EUS field.

Artificial Intelligence in Biliopancreatic Disorders: Applications in Cross-Imaging and Endoscopy

This review explores the transformative potential of artificial intelligence (AI) in the diagnosis and management of biliopancreatic disorders. By leveraging cutting-edge techniques, such as deep learning and convolutional neural networks, AI has significantly advanced gastroenterology, particularly in endoscopic procedures, such as colonoscopy; upper endoscopy; and capsule endoscopy. These applications enhance adenoma detection rates and improve lesion characterization and diagnostic accuracy. AI's integration in cross-sectional imaging modalities, such as computed tomography and magnetic resonance imaging, has remarkable potential. Models have demonstrated high accuracy in identifying pancreatic ductal adenocarcinoma; pancreatic cystic lesions; and pancreatic neuroendocrine tumors, aiding in early diagnosis; resectability assessment; and personalized treatment planning. In advanced endoscopic procedures, such as digital single-operator cholangioscopy and endoscopic ultrasound, AI enhances anatomic recognition and improves lesion classification, with a potential for reduction in procedural variability, enabling more consistent diagnostic and therapeutic outcomes. Promising applications in biliopancreatic endoscopy include the detection of biliary stenosis, classification of dysplastic precursor lesions, and assessment of pancreatic abnormalities. This review aims to capture the current state of AI application in biliopancreatic disorders, summarizing the results of early studies and paving the path for future directions.

Endoscopic treatment of unresectable perihilar cholangiocarcinoma: beyond biliary drainage

Perihilar cholangiocarcinoma (PHCC) is an aggressive biliary malignancy originating from the epithelial cells of the bile duct, typically located in the extrahepatic biliary tree, proximal to the cystic duct. PHCC often presents with a rapid onset of jaundice. While radical surgical resection remains the only curative treatment, only a minority of patients are eligible due to early metastasis and challenges associated with preoperative evaluations. Comprehensive treatments, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy, are crucial for managing PHCC. However, in advanced stages, complications such as cholestatic liver injury, malnutrition, and biliary infections pose significant obstacles to these treatments. Therefore, biliary drainage (BD) is essential in the management of PHCC. In addition to external drainage methods like percutaneous transhepatic biliary drainage (PTBD), endoscopic biliary drainage (EBD), particularly endoscopic retrograde cholangiopancreatography (ERCP), offer an effective option for internal drainage, which is more physiologically compatible and better tolerated. Furthermore, the integration of various endoscopic techniques has expanded the management of PHCC beyond mere drainage. Techniques such as radiofrequency ablation (RFA), photodynamic therapy (PDT), and endoscopic ultrasound (EUS) based methods present new therapeutic avenues, albeit with variable results. This review aims to summarize current advancements and ongoing debates in the field of endoscopic treatment for unresectable PHCC.

Role of Artificial Intelligence for Endoscopic Ultrasound

Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal tract diseases, but it is one of the most operator-dependent endoscopic techniques, requiring a long and complex learning curve. The role of artificial intelligence (AI) in EUS is growing as AI algorithms can assist in lesion detection and characterization by analyzing EUS images. Deep learning (DL) techniques, such as convolutional neural networks, have shown great potential for tumor identification; the application of AI models can increase the EUS diagnostic accuracy, provide faster diagnoses, and provide more information that can be helpful also for a training program.

Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence

Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new methods for diagnosing AIP. The development of artificial intelligence (AI) is evident, and it is believed to have potential in the clinical diagnosis of AIP. This article aims to list the current diagnostic difficulties of AIP, describe existing AI applications, and suggest directions for future AI usages in AIP diagnosis.

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and management of biliopancreatic lesions. This review highlights the current state of AI-enhanced EUS (AI-EUS) for diagnosing solid and cystic pancreatic lesions, as well as biliary diseases. AI-driven models, including machine learning (ML) and deep learning (DL), have shown significant improvements in diagnostic accuracy, particularly in distinguishing pancreatic ductal adenocarcinoma (PDAC) from benign conditions and in the characterization of pancreatic cystic neoplasms. Advanced algorithms, such as convolutional neural networks (CNNs), enable precise image analysis, real-time lesion classification, and integration with clinical and genomic data for personalized care. In biliary diseases, AI-assisted systems enhance bile duct visualization and streamline diagnostic workflows, minimizing operator dependency. Emerging applications, such as AI-guided EUS fine-needle aspiration (FNA) and biopsy (FNB), improve diagnostic yields while reducing errors. Despite these advancements, challenges remain, including data standardization, model interpretability, and ethical concerns regarding data privacy. Future developments aim to integrate multimodal imaging, real-time procedural support, and predictive analytics to further refine the diagnostic and therapeutic potential of AI-EUS. AI-driven innovation in EUS stands poised to revolutionize pancreatico-biliary diagnostics, facilitating earlier detection, enhancing precision, and paving the way for personalized medicine in gastrointestinal oncology and beyond.

Diagnostic Approach to Biliary Strictures

Biliary strictures represent a narrowing of the bile ducts, leading to obstruction that may result from benign or malignant etiologies. Accurate diagnosis is crucial but challenging due to overlapping features between benign and malignant strictures. This review presents a comprehensive diagnostic approach that integrates biochemical markers, imaging modalities, and advanced endoscopic techniques to distinguish between these causes. Imaging tools such as ultrasound, MRI/MRCP, and CECT are commonly used, each with distinct advantages and limitations. Furthermore, endoscopic procedures such as ERCP and EUS are key in tissue acquisition, enhancing diagnostic accuracy, especially for indeterminate or complex strictures. Recent innovations, including artificial intelligence and new endoscopic techniques, hold promise in enhancing precision and reducing diagnostic challenges. This review emphasizes a multidisciplinary strategy to improve diagnostic pathways, ensuring timely management for patients with biliary strictures.

Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy

Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions

The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain multiple imaging modes. Different imaging modes and different frames can reflect the different characteristics of lesions. Compared with previous studies, the proposed model can efficiently extract and integrate the spatiotemporal relationships between different modes and does not require manual selection of representative frames. In recent years, Vision Transformer has received much attention in the field of computer vision. Combined with convolutional neural networks, hybrid transformers can also perform well on small datasets. This study designed a novel deep learning architecture based on hybrid transformer called TransEBUS. By adding learnable parameters in the temporal dimension, TransEBUS was able to extract spatiotemporal features from insufficient data. In addition, we designed a two-stream module to integrate information from three different imaging modes of EBUS. Furthermore, we applied contrastive learning when training TransEBUS, enabling it to learn discriminative representation of benign and malignant mediastinal lesions. The results show that TransEBUS achieved a diagnostic accuracy of 82% and an area under the curve of 0.8812 in the test dataset, outperforming other methods. It also shows that several models can improve performance by incorporating two-stream module. Our proposed system has shown its potential to help physicians distinguishing benign and malignant mediastinal lesions, thereby ensuring the accuracy of EBUS examination.

Deep learning for segmentation of colorectal carcinomas on endoscopic ultrasound

BACKGROUND

Bowel-preserving local resection of early rectal cancer is less successful if the tumor infiltrates the muscularis propria as opposed to submucosal infiltration only. Magnetic resonance imaging currently lacks the spatial resolution to provide a reliable estimation of the infiltration depth. Endoscopic ultrasound (EUS) has better resolution, but its interpretation is investigator dependent. We hypothesize that automated image segmentation of EUS could be a way to standardize EUS interpretation.

METHODS

EUS media and outcome data were collected prospectively. Based on 373 expert manual segmentations, a convolutional neural network was developed to perform segmentation of the submucosa, muscularis propria, and tumors. The mean surface distance (MSD), maximal distance between segmentations (Hausdorff distance; HDD), and overlap (Dice similarity index; DSI) were calculated.

RESULTS

The median MSD and HDD values were 3.2 and 17.7 pixels for the tumor, 3.4 and 24.7 pixels for the submucosa, and 2.6 and 20.0 pixels for the muscularis propria, respectively. The median DSI values for the tumor, submucosa, and muscularis propria were 0.82, 0.57, and 0.59, respectively. These values reflect good agreement between manual and deep learning segmentation.

CONCLUSIONS

This study found encouraging results of using automated analysis of EUS images of early rectal cancer, supporting further exploration in clinical practice.

Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques.

OBJECTIVE

This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct.

MATERIALS AND METHODS

In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model's robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca.

RESULTS

The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation.

CONCLUSIONS

This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.

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.

Current and Emerging Applications of Artificial Intelligence (AI) in the Management of Pancreatobiliary (PB) disorders

PURPOSE OF REVIEW: In this review, we aim to summarize the existing literature and future directions on the use of artificial intelligence (AI) for the diagnosis and treatment of PB (pancreaticobiliary) disorders. RECENT FINDINGS: AI models have been developed to aid in the diagnosis and management of PB disorders such as pancreatic adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (pNETs), acute pancreatitis, chronic pancreatitis, autoimmune pancreatitis, choledocholithiasis, indeterminate biliary strictures, cholangiocarcinoma and endoscopic procedures such as ERCP, EUS, and cholangioscopy. Recent studies have integrated radiological, endoscopic and pathological data to develop models to aid in better detection and prognostication of these disorders. AI is an indispensable proponent in the future practice of medicine. It has been extensively studied and approved for use in the detection of colonic polyps. AI models based on clinical, laboratory, and radiomics have been developed to aid in the diagnosis and management of various PB disorders and its application is ever expanding. Despite promising results, these AI-based models need further external validation to be clinically applicable.

Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine

The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.

Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions

Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.

Applications of artificial intelligence in biliary tract cancers

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.

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.

Artificial intelligence in gastrointestinal endoscopy: a comprehensive review

Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI's capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist's arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.

Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study

BACKGROUND

There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images.

METHODS

The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared.

RESULTS

In the internal test cohort, the accuracy of CNN model was 92.1-100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45-99.92% and 93.35-99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84-0.98).

CONCLUSIONS

The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.

The application of artificial intelligence in EUS

Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial

BACKGROUND

Endoscopic ultrasonography (EUS) is a key procedure for the diagnosis of biliopancreatic diseases. However, the performance among EUS endoscopists varies greatly and leads to blind spots during the operation, which can impair the health outcomes of patients. We previously developed an artificial intelligence (AI) device that accurately identified EUS standard stations and significantly reduced the difficulty of ultrasonography image interpretation. In this study, we updated the device (named EUS-IREAD) and validated its performance in improving the quality of EUS procedures.

METHODS

In this single-centre, randomised, controlled trial, we updated EUS-IREAD so it consisted of five learning models to identify eight EUS stations and 24 anatomical structures. The trial was done at the Renmin Hospital of Wuhan University (Wuhan, China) and included patients aged 18 years or older with suspected biliopancreatic (pancreas and biliopancreatic duct) lesions due to clinical symptoms, radiological findings, or laboratory findings, and with a high risk of pancreatic cancer. Patients were randomly assigned (1:1) by a dedicated research assistant using a computer-generated random number series (with a block size of four) to undergo the EUS procedure with or without the assistance of EUS-IREAD. Endoscopists in the EUS-IREAD-assisted group were required to observe all standard stations and anatomical structures according to the prompts by the AI device. Data collectors, the independent data anaylsis team, and patients were masked to group allocation. The primary outcome was the missed scanning rate of standard stations between the two groups, which was assessed in patients who underwent EUS procedure in accordance with the assigned intervention (per protocol). This trial is registered with ClinicalTrials.gov, NCT05457101.

FINDINGS

Between July 9, 2022, and Feb 28, 2023, 290 patients (mean age 55·93 years [SD 14·06], 152 [52%] male, and 138 [48%] female) were randomly assigned and analysed, including 144 in the EUS-IREAD-assisted group and 146 in the control group. The EUS-IREAD-assisted group had a lower missed scanning rate of stations than the control group (4·5% [SD 0·8] vs 14·3% [1·0], -9·8% [95% CI -12·2 to -7·5]; odds ratio 3·6 [95% Cl 2·6 to 4·9]; p<0·0001). No significant adverse event was found during the study.

INTERPRETATION

Our study confirms the capability of EUS-IREAD to monitor the blind spots and reduce the missed rate of stations and structures during EUS procedures. The EUS-IREAD has the potential to play an essential part in EUS quality control.

FUNDING

Innovation Team Project of Health Commission of Hubei Province and College-enterprise Deepening Reform Project of Wuhan University.

Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

BACKGROUND

Total hip replacement (THR) is considered the gold standard of treatment for refractory degenerative hip disorders. Identifying patients who should receive THR in the short term is important. Some conservative treatments, such as intra-articular injection administered a few months before THR, may result in higher odds of arthroplasty infection. Delayed THR after functional deterioration may result in poorer outcomes and longer waiting times for those who have been flagged as needing THR. Deep learning (DL) in medical imaging applications has recently obtained significant breakthroughs. However, the use of DL in practical wayfinding, such as short-term THR prediction, is still lacking.

OBJECTIVE

In this study, we will propose a DL-based assistant system for patients with pelvic radiographs to identify the need for THR within 3 months.

METHODS

We developed a convolutional neural network-based DL algorithm to analyze pelvic radiographs, predict the hip region of interest (ROI), and determine whether or not THR is required. The data set was collected from August 2008 to December 2017. The images included 3013 surgical hip ROIs that had undergone THR and 1630 nonsurgical hip ROIs. The images were split, using split-sample validation, into training (n=3903, 80%), validation (n=476, 10%), and testing (n=475, 10%) sets to evaluate the algorithm performance.

RESULTS

The algorithm, called SurgHipNet, yielded an area under the receiver operating characteristic curve of 0.994 (95% CI 0.990-0.998). The accuracy, sensitivity, specificity, and F1-score of the model were 0.977, 0.920, 0932, and 0.944, respectively.

CONCLUSIONS

The proposed approach has demonstrated that SurgHipNet shows the ability and potential to provide efficient support in clinical decision-making; it can assist physicians in promptly determining the optimal timing for THR.

A Comprehensive Guide to Artificial Intelligence in Endoscopic Ultrasound

BACKGROUND

Endoscopic Ultrasound (EUS) is widely used for the diagnosis of bilio-pancreatic and gastrointestinal (GI) tract diseases, for the evaluation of subepithelial lesions, and for sampling of lymph nodes and solid masses located next to the GI tract. The role of Artificial Intelligence in healthcare in growing. This review aimed to provide an overview of the current state of AI in EUS from imaging to pathological diagnosis and training.

METHODS

AI algorithms can assist in lesion detection and characterization in EUS by analyzing EUS images and identifying suspicious areas that may require further clinical evaluation or biopsy sampling. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great potential for tumor identification and subepithelial lesion (SEL) evaluation by extracting important features from EUS images and using them to classify or segment the images.

RESULTS

AI models with new features can increase the accuracy of diagnoses, provide faster diagnoses, identify subtle differences in disease presentation that may be missed by human eyes, and provide more information and insights into disease pathology.

CONCLUSIONS

The integration of AI in EUS images and biopsies has the potential to improve the diagnostic accuracy, leading to better patient outcomes and to a reduction in repeated procedures in case of non-diagnostic biopsies.

Artificial intelligence in endoscopic imaging for detection of malignant biliary strictures and cholangiocarcinoma: a systematic review

Background

Artificial intelligence (AI), when applied to computer vision using a convolutional neural network (CNN), is a promising tool in "difficult-to-diagnose" conditions such as malignant biliary strictures and cholangiocarcinoma (CCA). The aim of this systematic review is to summarize and review the available data on the diagnostic utility of endoscopic AI-based imaging for malignant biliary strictures and CCA.

Methods

In this systematic review, PubMed, Scopus and Web of Science databases were reviewed for studies published from January 2000 to June 2022. Extracted data included type of endoscopic imaging modality, AI classifiers, and performance measures.

Results

The search yielded 5 studies involving 1465 patients. Of the 5 included studies, 4 (n=934; 3,775,819 images) used CNN in combination with cholangioscopy, while one study (n=531; 13,210 images) used CNN with endoscopic ultrasound (EUS). The average image processing speed of CNN with cholangioscopy was 7-15 msec per frame while that of CNN with EUS was 200-300 msec per frame. The highest performance metrics were observed with CNN-cholangioscopy (accuracy 94.9%, sensitivity 94.7%, and specificity 92.1%). CNN-EUS was associated with the greatest clinical performance application, providing station recognition and bile duct segmentation; thus reducing procedure length and providing real-time feedback to the endoscopist.

Conclusions

Our results suggest that there is increasing evidence to support a role for AI in the diagnosis of malignant biliary strictures and CCA. CNN-based machine leaning of cholangioscopy images appears to be the most promising, while CNN-EUS has the best clinical performance application.

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 using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

Artificial intelligence in the endoscopic approach of biliary tract diseases: A current review

In recent years there have been major developments in the field of artificial intelligence. The different areas of medicine have taken advantage of this tool to make various diagnostic and therapeutic methods more effective, safe, and user-friendly. In this way, artificial intelligence has been an increasingly present reality in medicine. In the field of Gastroenterology, the main application has been in the detection and characterization of colonic polyps, but an increasing number of studies have been published on the application of deep learning systems in other pathologies of the gastrointestinal tract. Evidence of the application of artificial intelligence in the assessment of biliary tract is still scarce. Some studies support the usefulness of these systems in the investigation and treatment of choledocholithiasis, demonstrating that they have the potential to be integrated into clinical practice and endoscopic procedures, such as endoscopic retrograde cholangiopancreatography. Its application in cholangioscopy for the investigation of undetermined biliary strictures also seems to be promising. Assessing the bile duct through endoscopic ultrasound can be challenging, especially for less experienced operators, thus becoming an area of potential interest for artificial intelligence. In this review, we summarize the state of the art of artificial intelligence in the endoscopic diagnosis and treatment of biliary diseases.

A systematic review on application of deep learning in digestive system image processing

With the advent of the big data era, the application of artificial intelligence represented by deep learning in medicine has become a hot topic. In gastroenterology, deep learning has accomplished remarkable accomplishments in endoscopy, imageology, and pathology. Artificial intelligence has been applied to benign gastrointestinal tract lesions, early cancer, tumors, inflammatory bowel diseases, livers, pancreas, and other diseases. Computer-aided diagnosis significantly improve diagnostic accuracy and reduce physicians' workload and provide a shred of evidence for clinical diagnosis and treatment. In the near future, artificial intelligence will have high application value in the field of medicine. This paper mainly summarizes the latest research on artificial intelligence in diagnosing and treating digestive system diseases and discussing artificial intelligence's future in digestive system diseases. We sincerely hope that our work can become a stepping stone for gastroenterologists and computer experts in artificial intelligence research and facilitate the application and development of computer-aided image processing technology in gastroenterology.