Real-time automated diagnosis of precancerous lesions and early esophageal squamous cell carcinoma using a deep learning model (with videos)

Effects of artificial intelligence assistance on endoscopist performance: Comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video-based models

Objectives

Superficial esophageal squamous cell carcinoma (ESCC) detection is crucial. Although narrow-band imaging improves detection, its effectiveness is diminished by inexperienced endoscopists. The effects of artificial intelligence (AI) assistance on ESCC detection by endoscopists remain unclear. Therefore, this study aimed to develop and validate an AI model for ESCC detection using endoscopic video analysis and evaluate diagnostic improvements.

Methods

Endoscopic videos with and without ESCC lesions were collected from May 2020 to January 2022. The AI model trained on annotated videos and 18 endoscopists (eight experts, 10 non-experts) evaluated their diagnostic performance. After 4 weeks, the endoscopists re-evaluated the test data with AI assistance. Sensitivity, specificity, and accuracy were compared between endoscopists with and without AI assistance.

Results

Training data comprised 280 cases (140 with and 140 without lesions), and test data, 115 cases (52 with and 63 without lesions). In the test data, the median lesion size was 14.5 mm (range: 1-100 mm), with pathological depths ranging from high-grade intraepithelial to submucosal neoplasia. The model's sensitivity, specificity, and accuracy were 76.0%, 79.4%, and 77.2%, respectively. With AI assistance, endoscopist sensitivity (57.4% vs. 66.5%) and accuracy (68.6% vs. 75.9%) improved significantly, while specificity increased slightly (87.0% vs. 91.6%). Experts demonstrated substantial improvements in sensitivity (59.1% vs. 70.0%) and accuracy (72.1% vs. 79.3%). Non-expert accuracy increased significantly (65.8% vs. 73.3%), with slight improvements in sensitivity (56.1% vs. 63.7%) and specificity (81.9% vs. 89.2%).

Conclusions

AI assistance enhances ESCC detection and improves endoscopists' diagnostic performance, regardless of experience.

Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review)

Esophageal cancer (EC) is one of the leading causes of cancer-related mortality worldwide, still faces significant challenges in early diagnosis and prognosis. Early EC lesions often present subtle symptoms and current diagnostic methods are limited in accuracy due to tumor heterogeneity, lesion morphology and variable image quality. These limitations are particularly prominent in the early detection of precancerous lesions such as Barrett's esophagus. Traditional diagnostic approaches, such as endoscopic examination, pathological analysis and computed tomography, require improvements in diagnostic precision and staging accuracy. Deep learning (DL), a key branch of artificial intelligence, shows great promise in improving the detection of early EC lesions, distinguishing benign from malignant lesions and aiding cancer staging and prognosis. However, challenges remain, including image quality variability, insufficient data annotation and limited generalization. The present review summarized recent advances in the application of DL to medical images obtained through various imaging techniques for the diagnosis of EC at different stages. It assesses the role of DL in tumor pathology, prognosis prediction and clinical decision support, highlighting its advantages in EC diagnosis and prognosis evaluation. Finally, it provided an objective analysis of the challenges currently facing the field and prospects for future applications.

Past, Present, and Future: A History Lesson in Artificial Intelligence

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.

Multimodal convolutional neural network-based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video)

BACKGROUND AND AIMS

Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.

METHODS

Based on dSOC videos and images of 111 patients (total of 15,158 still frames), a real-time CNN-based algorithm for CADe and CADx was developed and validated. We established an image-only model and metadata injection approach. In addition, frame-wise and case-based predictions on complete dSOC video sequences were validated. Model embeddings were visualized, and class activation maps highlighted relevant image regions.

RESULTS

The concatenation-based CADx approach achieved a per-frame area under the receiver-operating characteristic curve of .871, sensitivity of .809 (95% CI, .784-.832), specificity of .773 (95% CI, .761-.785), positive predictive value of .450 (95% CI, .423-.467), and negative predictive value of .946 (95% CI, .940-.954) with respect to malignancy on 5715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, 6 of 8 malignant cases and all 12 benign cases were identified correctly.

CONCLUSIONS

Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both CADe and CADx. The integration of non-image data can improve CNN-based support systems, targeting current challenges in the assessment of biliary strictures.

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

Role of Artificial Intelligence in the Detection and Management of Premalignant and Malignant Lesions of the Esophagus and Stomach

The advent of artificial intelligence (AI) and deep learning algorithms, particularly convolutional neural networks, promises to address pitfalls, bridging the care for patients at high risk with improved detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of lesions. This review describes the available artificial intelligence (AI) technology and the current data on AI tools for screening esophageal squamous cell cancer, Barret's esophagus-related neoplasia, and gastric cancer. These tools outperformed endoscopists in many situations. Recent randomized controlled trials have demonstrated the successful application of AI tools in clinical practice with improved outcomes.

Attention-Enhanced Multi-Task Deep Learning Model for Classification and Segmentation of Esophageal Lesions

Accurate detection and segmentation of esophageal lesions are crucial for diagnosing and treating gastrointestinal diseases. However, early detection of esophageal cancer remains challenging, contributing to a reduced five-year survival rate among patients. This paper introduces a novel multitask deep learning model for automatic diagnosis that integrates classification and segmentation tasks to assist endoscopists effectively. Our approach leverages the MobileNetV2 deep learning architecture enhanced with a mutual attention module, significantly improving the model's performance in determining the locations of esophageal lesions. Unlike traditional models, the proposed model is designed not to replace endoscopists but to empower them to correct false predictions when provided with additional Supporting Information. We evaluated the proposed model on three well-known data sets: Early Esophageal Cancer (EEC), CVC-ClinicDB, and KVASIR. The experimental results demonstrate promising performance, achieving high classification accuracies of 98.72% (F1-score: 98.08%) on CVC-ClinicDB, 98.95% (F1-score: 98.32%) on KVASIR, and 99.12% (F1-score: 99.00%) on our generated EEC data set. Compared to state-of-the-art models, our classification results show significant improvement. For the segmentation task, the model attained a Dice coefficient of 92.73% and an Intersection over Union (IoU) of 91.54%. These findings suggest that the proposed multitask deep learning model can effectively assist endoscopists in evaluating esophageal lesions, thereby alleviating their workload and enhancing diagnostic precision.

Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists

A computer-aided detection (CAD) system for early esophagus carcinoma identification during endoscopy with narrow-band imaging (NBI) was evaluated in a large-scale, prospective, tandem, randomized controlled trial to assess its effectiveness. The study was registered at the Chinese Clinical Trial Registry (ChiCTR2100050654, 2021/09/01). Involving 3400 patients were randomly assigned to either routine (routine-first) or CAD-assisted (CAD-first) NBI endoscopy, followed by the other procedure, with targeted biopsies taken at the end of the second examination. The primary outcome was the diagnosis of 1 or more neoplastic lesion of esophagus during the first examination. The CAD-first group demonstrated a significantly higher neoplastic lesion detection rate (3.12%) compared to the routine-first group (1.59%) with a relative detection ratio of 1.96 (P = 0.0047). Subgroup analysis revealed a higher detection rate in junior endoscopists using CAD-first, while no significant difference was observed for senior endoscopists. The CAD system significantly improved esophageal neoplasm detection, particularly benefiting junior endoscopists.

Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.

Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images

There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamous cell carcinoma (ESCC). Previously, we found that specific soft palate morphologies are strongly associated with increased ESCC risk. However, there is currently no artificial intelligence (AI) system that utilizes oral images for ESCC risk assessment. Here, we evaluated three AI models and three fine-tuning approaches with regard to their ESCC predictive power. Our dataset contained 539 cases, which were subdivided into 221 high-risk cases (2491 images) and 318 non-high-risk cases (2524 images). We used 480 cases (4295 images) for the training dataset, and the rest for validation. The Bilinear convolutional neural network (CNN) model (especially when pre-trained on fractal images) demonstrated diagnostic precision that was comparable to or better than other models for distinguishing between high-risk and non-high-risk groups. In addition, when tested with a small number of images containing soft palate data, the model showed high precision: the best AUC model had 0.91 (sensitivity 0.86, specificity 0.79). This study presents a significant advance in the development of an AI-based non-invasive screening tool for the identification of high-risk ESCC patients. The approach may be particularly suitable for institutes with limited medical imaging resources.

Artificial intelligence and early cancer of the digestive tract: New challenges and new futures

Early gastrointestinal tumors have a good prognosis, but they have insidious onset and no specific manifestations, making their diagnosis difficult. With the rapid development of artificial intelligence technology in the medical field, it has shown great potential in clinical work such as diagnosis and prognosis prediction of early gastrointestinal cancer. In this paper, we systematically review the relevant studies on AI in early esophageal cancer, early gastric cancer, early colon cancer, and hepatobiliary pancreatic cancer, and discuss the challenges and futures of AI application in early gastrointestinal cancer.

Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology

BACKGROUND AND AIMS

The American Society for Gastrointestinal Endoscopy (ASGE) AI Task Force along with experts in endoscopy, technology space, regulatory authorities, and other medical subspecialties initiated a consensus process that analyzed the current literature, highlighted potential areas, and outlined the necessary research in artificial intelligence (AI) to allow a clearer understanding of AI as it pertains to endoscopy currently.

METHODS

A modified Delphi process was used to develop these consensus statements.

RESULTS

Statement 1: Current advances in AI allow for the development of AI-based algorithms that can be applied to endoscopy to augment endoscopist performance in detection and characterization of endoscopic lesions. Statement 2: Computer vision-based algorithms provide opportunities to redefine quality metrics in endoscopy using AI, which can be standardized and can reduce subjectivity in reporting quality metrics. Natural language processing-based algorithms can help with the data abstraction needed for reporting current quality metrics in GI endoscopy effortlessly. Statement 3: AI technologies can support smart endoscopy suites, which may help optimize workflows in the endoscopy suite, including automated documentation. Statement 4: Using AI and machine learning helps in predictive modeling, diagnosis, and prognostication. High-quality data with multidimensionality are needed for risk prediction, prognostication of specific clinical conditions, and their outcomes when using machine learning methods. Statement 5: Big data and cloud-based tools can help advance clinical research in gastroenterology. Multimodal data are key to understanding the maximal extent of the disease state and unlocking treatment options. Statement 6: Understanding how to evaluate AI algorithms in the gastroenterology literature and clinical trials is important for gastroenterologists, trainees, and researchers, and hence education efforts by GI societies are needed. Statement 7: Several challenges regarding integrating AI solutions into the clinical practice of endoscopy exist, including understanding the role of human-AI interaction. Transparency, interpretability, and explainability of AI algorithms play a key role in their clinical adoption in GI endoscopy. Developing appropriate AI governance, data procurement, and tools needed for the AI lifecycle are critical for the successful implementation of AI into clinical practice. Statement 8: For payment of AI in endoscopy, a thorough evaluation of the potential value proposition for AI systems may help guide purchasing decisions in endoscopy. Reliable cost-effectiveness studies to guide reimbursement are needed. Statement 9: Relevant clinical outcomes and performance metrics for AI in gastroenterology are currently not well defined. To improve the quality and interpretability of research in the field, steps need to be taken to define these evidence standards. Statement 10: A balanced view of AI technologies and active collaboration between the medical technology industry, computer scientists, gastroenterologists, and researchers are critical for the meaningful advancement of AI in gastroenterology.

CONCLUSIONS

The consensus process led by the ASGE AI Task Force and experts from various disciplines has shed light on the potential of AI in endoscopy and gastroenterology. AI-based algorithms have shown promise in augmenting endoscopist performance, redefining quality metrics, optimizing workflows, and aiding in predictive modeling and diagnosis. However, challenges remain in evaluating AI algorithms, ensuring transparency and interpretability, addressing governance and data procurement, determining payment models, defining relevant clinical outcomes, and fostering collaboration between stakeholders. Addressing these challenges while maintaining a balanced perspective is crucial for the meaningful advancement of AI in gastroenterology.

Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers

One of the foremost causes of global healthcare burden is cancer of the gastrointestinal tract. The medical records, lab results, radiographs, endoscopic images, tissue samples, and medical histories of patients with gastrointestinal malignancies provide an enormous amount of medical data. There are encouraging signs that the advent of artificial intelligence could enhance the treatment of gastrointestinal issues with this data. Deep learning algorithms can swiftly and effectively analyze unstructured, high-dimensional data, including texts, images, and waveforms, while advanced machine learning approaches could reveal new insights into disease risk factors and phenotypes. In summary, artificial intelligence has the potential to revolutionize various features of gastrointestinal cancer care, such as early detection, diagnosis, therapy, and prognosis. This paper highlights some of the many potential applications of artificial intelligence in this domain. Additionally, we discuss the present state of the discipline and its potential future developments.

Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms

INTRODUCTION

The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer.

METHODS

In this study, we expanded upon traditional red-green-blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC).

RESULTS

An additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively.

CONCLUSION

The HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer.

Multi-modal networks for real-time monitoring of intracranial acoustic field during transcranial focused ultrasound therapy

BACKGROUND AND OBJECTIVE

Transcranial focused ultrasound (tFUS) is an emerging non-invasive therapeutic technology that offers new brain stimulation modality. Precise localization of the acoustic focus to the desired brain target throughout the procedure is needed to ensure the safety and effectiveness of the treatment, but acoustic distortion caused by the skull poses a challenge. Although computational methods can provide the estimated location and shape of the focus, the computation has not reached sufficient speed for real-time inference, which is demanded in real-world clinical situations. Leveraging the advantages of deep learning, we propose multi-modal networks capable of generating intracranial pressure map in real-time.

METHODS

The dataset consisted of free-field pressure maps, intracranial pressure maps, medical images, and transducer placements was obtained from 11 human subjects. The free-field and intracranial pressure maps were computed using the k-space method. We developed network models based on convolutional neural networks and the Swin Transformer, featuring a multi-modal encoder and a decoder.

RESULTS

Evaluations on foreseen data achieved high focal volume conformity of approximately 93% for both computed tomography (CT) and magnetic resonance (MR) data. For unforeseen data, the networks achieved the focal volume conformity of 88% for CT and 82% for MR. The inference time of the proposed networks was under 0.02 s, indicating the feasibility for real-time simulation.

CONCLUSIONS

The results indicate that our networks can effectively and precisely perform real-time simulation of the intracranial pressure map during tFUS applications. Our work will enhance the safety and accuracy of treatments, representing significant progress for low-intensity focused ultrasound (LIFU) therapies.

Artificial Intelligence in Gastrointestinal Endoscopy

Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanning a wide range of clinical indications. The central goals for AI in GI endoscopy are to improve endoscopic procedural performance and quality assessment, optimize patient outcomes, and reduce administrative burden. Despite early progress, such as Food and Drug Administration approval of the first computer-aided polyp detection system in 2021, there are numerous important challenges to be faced on the path toward broader adoption of AI algorithms in clinical endoscopic practice.

A Review of Application of Deep Learning in Endoscopic Image Processing

Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.

Applications of artificial intelligence in emergency and critical care diagnostics: a systematic review and meta-analysis

Introduction

Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial.

Methodology

The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews.

Results

In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette's neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%.

Conclusion

The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.

Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review

This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review

Artificial intelligence (AI) is making notable advancements in the medical field, particularly in bone fracture detection. This systematic review compiles and assesses existing research on AI applications aimed at identifying bone fractures through medical imaging, encompassing studies from 2010 to 2023. It evaluates the performance of various AI models, such as convolutional neural networks (CNNs), in diagnosing bone fractures, highlighting their superior accuracy, sensitivity, and specificity compared to traditional diagnostic methods. Furthermore, the review explores the integration of advanced imaging techniques like 3D CT and MRI with AI algorithms, which has led to enhanced diagnostic accuracy and improved patient outcomes. The potential of Generative AI and Large Language Models (LLMs), such as OpenAI's GPT, to enhance diagnostic processes through synthetic data generation, comprehensive report creation, and clinical scenario simulation is also discussed. The review underscores the transformative impact of AI on diagnostic workflows and patient care, while also identifying research gaps and suggesting future research directions to enhance data quality, model robustness, and ethical considerations.

Linked Color Imaging with Artificial Intelligence Improves the Detection of Early Gastric Cancer

INTRODUCTION

Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detection (CADe) system to detect GC with white light imaging (WLI) and linked color imaging (LCI) modes and aimed to compare the performance of CADe with that of endoscopists.

METHODS

The system was developed based on the deep learning framework from 9,021 images in 385 patients between 2017 and 2020. A total of 116 LCI and WLI videos from 110 patients between 2017 and 2023 were used to evaluate per-case sensitivity and per-frame specificity.

RESULTS

The per-case sensitivity and per-frame specificity of CADe with a confidence level of 0.5 in detecting GC were 78.6% and 93.4% for WLI and 94.0% and 93.3% for LCI, respectively (p < 0.001). The per-case sensitivities of nonexpert endoscopists for WLI and LCI were 45.8% and 80.4%, whereas those of expert endoscopists were 66.7% and 90.6%, respectively. Regarding detectability between CADe and endoscopists, the per-case sensitivities for WLI and LCI were 78.6% and 94.0% in CADe, respectively, which were significantly higher than those for LCI in experts (90.6%, p = 0.004) and those for WLI and LCI in nonexperts (45.8% and 80.4%, respectively, p < 0.001); however, no significant difference for WLI was observed between CADe and experts (p = 0.134).

CONCLUSIONS

Our CADe system showed significantly better sensitivity in detecting GC when used in LCI compared with WLI mode. Moreover, the sensitivity of CADe using LCI is significantly higher than those of expert endoscopists using LCI to detect GC.

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By analyzing the differences between the internal and external features of the lesion area, we propose a lesion-decoupling-based segmentation (LDS) network for assisting early cancer diagnosis. We introduce a plug-and-play module called self-sampling similar feature disentangling module (FDM) to obtain accurate lesion boundaries. Then, we propose a feature separation loss (FSL) function to separate pathological features from normal ones. Moreover, since physicians make diagnoses with multimodal data, we propose a multimodal cooperative segmentation network with two different modal images as input: white-light images (WLIs) and narrowband images (NBIs). Our FDM and FSL show a good performance for both single-modal and multimodal segmentations. Extensive experiments on five backbones prove that our FDM and FSL can be easily applied to different backbones for a significant lesion segmentation accuracy improvement, and the maximum increase of mean Intersection over Union (mIoU) is 4.58. For colonoscopy, we can achieve up to mIoU of 91.49 on our Dataset A and 84.41 on the three public datasets. For esophagoscopy, mIoU of 64.32 is best achieved on the WLI dataset and 66.31 on the NBI dataset.

Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review

The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.

Application of artificial intelligence in gastrointestinal endoscopy

Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

Novel milestones for early esophageal carcinoma: From bench to bed

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Background

In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective

To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods

Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.

Results

RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.

Conclusion

We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

The role of artificial intelligence in the endoscopic diagnosis of esophageal cancer: a systematic review and meta-analysis

Early detection of esophageal cancer is limited by accurate endoscopic diagnosis of subtle macroscopic lesions. Endoscopic interpretation is subject to expertise, diagnostic skill, and thus human error. Artificial intelligence (AI) in endoscopy is increasingly bridging this gap. This systematic review and meta-analysis consolidate the evidence on the use of AI in the endoscopic diagnosis of esophageal cancer. The systematic review was carried out using Pubmed, MEDLINE and Ovid EMBASE databases and articles on the role of AI in the endoscopic diagnosis of esophageal cancer management were included. A meta-analysis was also performed. Fourteen studies (1590 patients) assessed the use of AI in endoscopic diagnosis of esophageal squamous cell carcinoma-the pooled sensitivity and specificity were 91.2% (84.3-95.2%) and 80% (64.3-89.9%). Nine studies (478 patients) assessed AI capabilities of diagnosing esophageal adenocarcinoma with the pooled sensitivity and specificity of 93.1% (86.8-96.4) and 86.9% (81.7-90.7). The remaining studies formed the qualitative summary. AI technology, as an adjunct to endoscopy, can assist in accurate, early detection of esophageal malignancy. It has shown superior results to endoscopists alone in identifying early cancer and assessing depth of tumor invasion, with the added benefit of not requiring a specialized skill set. Despite promising results, the application in real-time endoscopy is limited, and further multicenter trials are required to accurately assess its use in routine practice.

Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis

BACKGROUND

Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.

AIM

To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).

METHODS

PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.

RESULTS

A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).

CONCLUSION

CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

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

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

Machine learning applications for early detection of esophageal cancer: a systematic review

INTRODUCTION

Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.

METHODS

We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.

RESULTS

The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.

CONCLUSION

Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Integration of IDPC Clustering Analysis and Interpretable Machine Learning for Survival Risk Prediction of Patients with ESCC

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

The current state of artificial intelligence in endoscopic diagnosis of early esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor of the digestive tract. The most effective method of reducing the disease burden in areas with a high incidence of esophageal cancer is to prevent the disease from developing into invasive cancer through screening. Endoscopic screening is key for the early diagnosis and treatment of ESCC. However, due to the uneven professional level of endoscopists, there are still many missed cases because of failure to recognize lesions. In recent years, along with remarkable progress in medical imaging and video evaluation technology based on deep machine learning, the development of artificial intelligence (AI) is expected to provide new auxiliary methods of endoscopic diagnosis and the treatment of early ESCC. The convolution neural network (CNN) in the deep learning model extracts the key features of the input image data using continuous convolution layers and then classifies images through full-layer connections. The CNN is widely used in medical image classification, and greatly improves the accuracy of endoscopic image classification. This review focuses on the AI-assisted diagnosis of early ESCC and prediction of early ESCC invasion depth under multiple imaging modalities. The excellent image recognition ability of AI is suitable for the detection and diagnosis of ESCC and can reduce missed diagnoses and help endoscopists better complete endoscopic examinations. However, the selective bias used in the training dataset of the AI system affects its general utility.

Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system

BACKGROUND

The use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform.

METHODS

In this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis.

RESULTS

In the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006).

CONCLUSIONS

The results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video)

BACKGROUND AND AIMS

Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI.

METHODS

Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated.

RESULTS

The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents.

CONCLUSIONS

The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

LEARNING CURVE IN ESOPHAGEAL ENDOSCOPIC SUBMUCOSAL DISSECTION BY WESTERN ENDOSCOSPISTS TRAINED IN JAPAN: EXPERIENCE IN LATIN AMERICA

•This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Retrospective observational study, enrolling patients that underwent EESD from 2009 to 2021, divided in 2 groups. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). The learning curve in esophageal ESD could be overcomed effectively and safely by an adequately trained Western endoscopist. Background - Esophageal endoscopic submucosal dissection (EESD) is a complex and time-consuming procedure at which training are mainly available in Japan. There is a paucity of data concerning the learning curve to master EESD by Western endoscopists. Objective - This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Methods - This is a retrospective observational study. Enrolling patients that underwent EESD from 2009 to 2021. The analysis was divided into two periods; T1: case 1 to 49 and T2: case 50 to 98. The following features were analyzed for each group: patients and tumors characteristics, en-bloc, complete and curative resection rates, procedure duration and adverse events rate. Results - Ninety-eight EESD procedures were performed. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). En bloc resection rate was 93.8% and 97.9% for T1 and T2, respectively (P=0.307). Complete resection rate was 79.5% and 85.7% for T1 and T2, respectively (P=0.424). Curative resection rate was 65.3% and 71.4% for T1 and T2, respectively (P=0.258). Four patients had complications; three during T1 period and one during T2 period. Overall mortality rate: 0%. Conclusion - The esophageal endoscopic submucosal dissection could be performed effectively and safely by an adequately trained Western endoscopist.

Machine learning for detection and classification of oral potentially malignant disorders: A conceptual review

Oral potentially malignant disorders represent precursor lesions that may undergo malignant transformation to oral cancer. There are many known risk factors associated with the development of oral potentially malignant disorders, and contribute to the risk of malignant transformation. Although many advances have been reported to understand the biological behavior of oral potentially malignant disorders, their clinical features that indicate the characteristics of malignant transformation are not well established. Early diagnosis of malignancy is the most important factor to improve patients' prognosis. The integration of machine learning into routine diagnosis has recently emerged as an adjunct to aid clinical examination. Increased performances of artificial intelligence AI-assisted medical devices are claimed to exceed the human capability in the clinical detection of early cancer. Therefore, the aim of this narrative review is to introduce artificial intelligence terminology, concepts, and models currently used in oncology to familiarize oral medicine scientists with the language skills, best research practices, and knowledge for developing machine learning models applied to the clinical detection of oral potentially malignant disorders.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review

Importance

Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity.

Highlights

We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma.

Conclusion

AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.

Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video)

BACKGROUND AND AIMS

Accurately diagnosing malignant biliary strictures (MBSs) as benign or malignant remains challenging. It has been suggested that direct visualization and interpretation of cholangioscopy images provide greater accuracy for stricture classification than current sampling techniques (ie, brush cytology and forceps biopsy sampling) using ERCP. We aimed to develop a convolutional neural network (CNN) model capable of accurate stricture classification and real-time evaluation based solely on cholangioscopy image analysis.

METHODS

Consecutive patients with cholangioscopy examinations from 2012 to 2021 were reviewed. A CNN was developed and tested using cholangioscopy images with direct expert annotations. The CNN was then applied to a multicenter, reserved test set of cholangioscopy videos. CNN performance was then directly compared with that of ERCP sampling techniques. Occlusion block heatmap analyses were used to evaluate and rank cholangioscopy features associated with MBSs.

RESULTS

One hundred fifty-four patients with available cholangioscopy examinations were included in the study. The final image database comprised 2,388,439 still images. The CNN demonstrated good performance when tasked with mimicking expert annotations of high-quality malignant images (area under the receiver-operating characteristic curve, .941). Overall accuracy of CNN-based video analysis (.906) was significantly greater than that of brush cytology (.625, P = .04) or forceps biopsy sampling (.609, P = .03). Occlusion block heatmap analysis demonstrated that the most frequent image feature for an MBS was the presence of frond-like mucosa/papillary projections.

CONCLUSIONS

This study demonstrates that a CNN developed using cholangioscopy data alone has greater accuracy for biliary stricture classification than traditional ERCP-based sampling techniques.

Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System

OBJECTIVES

Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI).

MATERIALS AND METHODS

A total of 2081 endoscopic images were used for the development of a DL model, and the classification accuracy of the models and endoscopists with different levels of experience was compared.

RESULTS

Some mainstream DL models were utilized, of which DenseNet-121 outperformed. The area under the curve (AUC) of the DenseNet-121 was 0.968, and its classification accuracy (86.7%) was significantly higher than that of junior (71.5%) and experienced (77.4%) endoscopists. An XAI evaluation was also performed to explore the perception consistency between the DL model and endoscopists, which showed meaningful results for real-world applications.

CONCLUSIONS

The DL model showed a potential in improving the accuracy of endoscopists in LA-grading of GERD, and it has noticeable clinical application prospects and is worthy of further promotion.