Detection Of Gastrointestinal Diseases Research Articles

Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023).

Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications. This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps. The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020. The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations.

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic.

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn’s disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture’s results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

A Fuzzy Minkowski Distance-based fusion of convolutional neural networks for gastrointestinal disease detection

Accurate classification of gastrointestinal (GI) conditions from medical images is a critical task for facilitating timely diagnosis and effective treatment. However, the reliance on manual diagnosis introduces the possibility of human errors. In response, researchers have been tirelessly working to develop robust computerized methods that can significantly enhance diagnostic accuracy. In this study, we present a novel approach designed to elevate the precision of classification tasks through the utilization of a Fuzzy Minkowski Distance-based Ensemble Model. Our ensemble model is accurately constructed by integrating three well-established pre-trained convolutional neural network (CNN) models: MobileNet, ResNet101V2, and Xception. To enhance the robustness of these base models, we incorporate ResNeXt block, effectively amplifying their ability in feature extraction and representation. Furthermore, we extract probabilities from these models and aggregate them using a fuzzy Minkowski Distance approach. This technique serves to minimize error values between observed and ground-truth data, leading to a further enhancement in detection accuracy. For our experiments, we utilize a publicly available dataset containing 6000 endoscopy images. These images are not only enhanced through the application of image contrast enhancement techniques but are also subjected to augmentation processes, effectively enhancing the dataset's diversity and contributing to improved performance in the classification task. After conducting comprehensive assessments, we highlight the strength of our ensemble model compared to single base models and existing methods. The ensemble consistently achieves an impressive accuracy of 99.62%, showcasing its potential as a powerful tool for accurate diagnosis in the field of gastroenterology.

An optimized ensemble model bfased on cuckoo search with Levy Flight for automated gastrointestinal disease detection

An optimized ensemble model bfased on cuckoo search with Levy Flight for automated gastrointestinal disease detection

Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.

Gastrointestinal (GI) diseases have a high prevalence throughout the United States. Screening and diagnostic modalities are often expensive and invasive, and therefore, people do not utilize them effectively. Lack of proper screening and diagnostic assessment may lead to delays in diagnosis, more advanced disease at the time of diagnosis, and higher morbidity and mortality rates. Research on the intestinal microbiome has demonstrated that dysbiosis, or unfavorable alteration of organismal composition, precedes the onset of clinical symptoms for various GI diseases. GI disease diagnostic research has led to a shift towards non-invasive methods for GI screening, including chemical-detection tests that measure changes in volatile organic compounds (VOCs), which are the byproducts of bacterial metabolism that result in the distinct smell of stool. Many of these tools are expensive, immobile benchtop instruments that require highly trained individuals to interpret the results. These attributes make them difficult to implement in clinical settings. Alternatively, electronic noses (E-noses) are relatively cheaper, handheld devices that utilize multi-sensor arrays and pattern recognition technology to analyze VOCs. The purpose of this review is to (1) highlight how dysbiosis impacts intestinal diseases and how VOC metabolites can be utilized to detect alterations in the microbiome, (2) summarize the available VOC analytical platforms that can be used to detect aberrancies in intestinal health, (3) define the current technological advancements and limitations of E-nose technology, and finally, (4) review the literature surrounding several intestinal diseases in which headspace VOCs can be used to detect or predict disease.

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease's characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN-FFNN and CNN-XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet-MobileNet-DenseNet121-XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%.

Crosstalk between microbial biofilms in the gastrointestinal tract and chronic mucosa diseases.

The gastrointestinal (GI) tract is the largest reservoir of microbiota in the human body; however, it is still challenging to estimate the distribution and life patterns of microbes. Biofilm, as the predominant form in the microbial ecosystem, serves ideally to connect intestinal flora, molecules, and host mucosa cells. It gives bacteria the capacity to inhabit ecological niches, communicate with host cells, and withstand environmental stresses. This study intends to evaluate the connection between GI tract biofilms and chronic mucosa diseases such as chronic gastritis, inflammatory bowel disease, and colorectal cancer. In each disease, we summarize the representative biofilm makers including Helicobacter pylori, adherent-invasive Escherichia coli, Bacteroides fragilis, and Fusobacterium nucleatum. We address biofilm's role in causing inflammation and the pro-carcinogenic stage in addition to discussing the typical resistance, persistence, and recurrence mechanisms seen in vitro. Biofilms may serve as a new biomarker for endoscopic and pathologic detection of gastrointestinal disease and suppression, which may be a useful addition to the present therapy strategy.

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

High-fluence relay-based disposable photoacoustic-ultrasonic endoscopy for in vivo anatomical imaging of gastrointestinal tract

Photoacoustic endomicroscopy combined with ultrasound (PAEM-US) has been a long-standing expectation for gastrointestinal tumor examination. Here, we introduce a prototype disposable PAEM-US catheter and corresponding power interface unit, featuring catheter switchability, self-internal three-dimensional scanning, and system repeatability for gastrointestinal endoscopy. By utilizing high-fluence relays, cascade insertion loss of the optical waveguide is minimized to 0.6 dB with a high performance of power resistance, and a focus-customizable acousto-optic coaxial probe is designed for high-sensitivity optical-resolution photoacoustic imaging. Imaging capability was demonstrated with in vivo anatomical imaging at 30 frames per second. Imaging results showed co-registered microscopic visualization of the microvascular and stratification of the rat colorectum with lateral resolution of 18 μm and axial resolution of 63 μm, holding great potential in the clinical detection of gastrointestinal diseases.

An oral ratiometric NIR-II fluorescent probe for reliable monitoring of gastrointestinal diseases in vivo

Early monitoring of gastrointestinal diseases via orally delivered NIR-II ratiometric fluorescent probes represents a promising noninvasive diagnostic modality, but is challenging due to the limitation of harsh digestive environment. Here, we report a single-component NIR-II ratiometric molecular nanoprobe (LC-1250 NP) to monitor gastrointestinal disease with high specificity to its biomarker H2O2 via oral administration. LC-1250 NP displays stable fluorescence in the channel of 1250 long-pass (F1250LP) before and after the gastrointestinal disease detection as the reference, while it presents significantly enhanced fluorescence signal in the response channel of 1150 nm short-pass (F1150SP) in diseased gastrointestinal environment due to the intramolecular cyclization of LC-1250 molecules activated by H2O2. The fluorescence ratio (F1150SP/F1250LP) increases linearly with the concentration of H2O2 with a low detection limit of 20 nM. Therefore, when delivered orally, LC-1250 NP can accurately map the diseased areas and surmount the false-positive interference from biological heterogeneity by NIR-II ratiometric fluorescence imaging, providing sensitive and reliable evaluation for the progress of gastroenteritis.

WCE‐DCGAN: A data augmentation method based on wireless capsule endoscopy images for gastrointestinal disease detection

WCE‐DCGAN: A data augmentation method based on wireless capsule endoscopy images for gastrointestinal disease detection

Deep convolutional neural networks with ensemble learning and transfer learning for automated detection of gastrointestinal diseases

Gastrointestinal (GI) diseases are serious health threats to human health, and the related detection and treatment of gastrointestinal diseases place a huge burden on medical institutions. Imaging-based methods are one of the most important approaches for automated detection of gastrointestinal diseases. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to detection of gastrointestinal diseases has not been sufficiently explored. In this study, we propose a novel and practical method to detect gastrointestinal disease from wireless capsule endoscopy (WCE) images by convolutional neural networks. The proposed method utilizes three backbone networks modified and fine-tuned by transfer learning as the feature extractors, and an integrated classifier using ensemble learning is trained to detection of gastrointestinal diseases. The proposed method outperforms existing computational methods on the benchmark dataset. The case study results show that the proposed method captures discriminative information of wireless capsule endoscopy images. This work shows the potential of using deep learning-based computer vision models for effective GI disease screening.

Image recognition for gastrointestinal disease detection and diagnosis in QoS and QoE evaluation of 5G network communication

Image recognition for gastrointestinal disease detection and diagnosis in QoS and QoE evaluation of 5G network communication

Frictional resistance model for a capsule and an intestine with different central axes based on the intestinal nonlinear constitutive relationship

A magnetically controlled capsule endoscopy has a broad application for the detection of gastrointestinal diseases. The frictional resistance between a capsule and an intestine will affect the position control of the capsule. A quasi-linear viscoelastic (QLV) model is developed to describe the biomechanics of small intestines of a rabbit under the finite deformation. When the capsule is moving inside the intestine whose centerline is deviated from that of the capsule, a frictional resistance prediction model is created based on the established intestinal constitutive relationship, and the model is further validated by the frictional resistance experiment for the capsules with different diameters in small intestines of a rabbit. The work can help to design and optimize the magnetically controlled capsule endoscope.

Sensitivity improved Cerenkov luminescence endoscopy using optimal system parameters.

The challenges of clinical translation of optical imaging, including the limited availability of clinically used imaging probes and the restricted penetration depth of light propagation in tissues can be avoided using Cerenkov luminescence endoscopy (CLE). However, the clinical applications of CLE are limited due to the low signal level of Cerenkov luminescence and the large transmission loss caused by the endoscope, which results in a relatively low detection sensitivity of current CLE. The aim of this study was to enhance the detection sensitivity of the CLE system and thus improve the system for clinical application in the detection of gastrointestinal diseases. Four optical fiber endoscopes were customized with different system parameters, including monofilament (MF) diameter of imaging fiber bundles, fiber material, probe coating, etc. The endoscopes were connected to the detector via a specifically designed straight connection device to form the CLE system. The β-2-[18F]-Fluoro-2-deoxy-D-glucose (18F-FDG) solution and the radionuclide of Gallium-68 (68Ga) were used to evaluate the performance of the CLE system. The images of the 18F-FDG solution acquired by the CLE were used to optimize imaging parameters of the system. By using the endoscope with optimized parameters, including the MF diameter of imaging fiber bundles, fiber materials, etc., the resolution and sensitivity of the assembled CLE system were measured by imaging the radionuclide of 68Ga. The results of 18F-FDG experiments showed that larger MF diameter led to higher collection efficiency. The fiber material and probe coating with high transmission ratios in the range of 400-900 nm also increased signal collection and transmission efficiency. The results of 68Ga evaluations showed that a minimum radioactive activity of radionuclides as low as 0.03 µCi was detected in vitro within 5 minutes, while that of 0.68 µCi can be detected within 1 minute. In vivo experiments also demonstrated that the developed CLE system achieved a high sensitivity at a submicrocurie level; that is, 0.44 µCi within 5 minutes, and 0.83 µCi within 1 minute. The weaker in vivo sensitivity was due to the attenuation of the signal by the mouse tissue skin and the autofluorescence interference produced by biological tissues. By optimizing the structural parameters of fiber endoscope and imaging parameters for data acquisition, we developed a CLE system with a sensitivity at submicrocurie level. These results support the possibility that this technology can clinically detect early tumors within 1 minute.

The Clinical Value of Multislice Spiral Computed Tomography in the Diagnosis of Upper Digestive Tract Diseases.

Imaging methods for gastrointestinal diseases were based on X-ray imaging until the 1970s, but the development of fiberoptic endoscopy in the 1980s has replaced X-ray imaging. Endoscopy can directly observe the location, size, scope, and color of lesions and obtain pathological results through biopsy, while ligation and other treatments can be performed on polyps and other lesions. Studies have shown that multilayer spiral computed tomography (CT) examination after standardized gastrointestinal preparation and full use of the advantages of various 3D postprocessing reconstruction techniques are of great clinical value in the detection of gastrointestinal diseases, determination of the nature of lesions, localization of lesions, and staging of gastrointestinal malignancies and can make up for the shortcomings of fiberoptic endoscopy, and various 3D postprocessing reconstruction modes have their own advantages and disadvantages. Among them, conventional CT cross-sectional images are the basic images for the diagnosis of various gastric testicular lesions. Axial images, especially thin-layer axial images, can detect the absolute majority of lesions, but there are limitations in observing the anatomical position of lesions, invasion of surrounding tissues, lymph node metastasis, vascularity, and determination of the stage of malignant tumors.

Intelligent Ingestibles: Future of Internet of Body

In this article, we first provide the basics of ingestibles. Then, we provide a detailed survey on ingestibles as per their applicability in leveraging gastrointestinal disease detection, management, and treatment. Next, we show how ingestibles could be related with the concept of Internet of body. Lastly, we discuss various key challenges and future directions to mitigate these issues.

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

Content-based image medical retrieval (CBMIR) is a technique for retrieving medical images on the basis of automatically derived image features such as colour, texture and shape. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. The retrieval performance of a CBMIR system depends mainly on the representation of image features, which researchers have studied extensively for decades. Although a number of methods and approaches have been suggested, it remains one of the most challenging problems in current (CBMIR) studies, largely due to the well-known “semantic gap” issue that exists between machine-captured low-level image features and human-perceived high-level semantic concepts. There have been many techniques proposed to bridge this gap. This study proposes a novel relevance feedback retrieval method (RFRM) for CBMIR. The feedback implemented here is based on voting values performed by each class in the image repository. Here, eighteen using colour moments and GLCM texture features were extracted to represent each image and eight common similarity coefficients were used as similarity measures. After briefly researching using a single random image query, the top images retrieved from each class are used as voters to select the most effective similarity coefficient that will be used for the final searching process. Our proposed method is implemented on the Kvasir dataset, which has 4,000 images divided into eight classes and was recently widely used for gastrointestinal disease detection. Intensive statistical analysis of the results shows that our proposed RFRM method has the best performance for enhancing both recall and precision when it uses any group of similarity coefficients.

Advanced endoscopic methods in gastrointestinal diseases: a systematic review.

Endoscopic imaging is the main method for detecting gastrointestinal diseases, which adversely affect human health. White light endoscopy (WLE) was the first method used for endoscopic examination and is still the preliminary step in the detection of gastrointestinal diseases during clinical examination. However, it cannot accurately diagnose gastrointestinal diseases owing to its poor correlation with histopathological diagnosis. In recent years, many advanced endoscopic methods have emerged to improve the detection accuracy by endoscopy. Chromoendoscopy (CE) enhances the contrast between normal and diseased tissues using biocompatible dye agents. Narrow band imaging (NBI) can improve the contrast between capillaries and submucosal vessels by changing the light source acting on the tissue using special filters to realize the visualization of the vascular structure. Flexible spectral imaging color enhancement (FICE) technique uses the reflectance spectrum estimation technique to obtain individual spectral images and reconstructs an enhanced image of the mucosal surface using three selected spectral images. The i-Scan technology takes advantage of the different reflective properties of normal and diseased tissues to obtain images, and enhances image contrast through post-processing algorithms. These abovementioned methods can be used to detect gastrointestinal diseases by observing the macroscopic structure of the digestive tract mucosa, but the ability of early cancer detection is limited with low resolution. However, based on the principle of confocal imaging, probe-based confocal laser endomicroscopy (pCLE) can enable cellular visualization with high-performance probes, which can present cellular morphology that is highly consistent with that shown by biopsy to provide the possibility of early detection of cancer. Other endoscopic imaging techniques including endoscopic optical coherence tomography (EOCT) and photoacoustic endoscopy (PAE), are also promising for diagnosing gastrointestinal diseases. This review focuses on these technologies and aims to provide an overview of different technologies and their clinical applicability.