Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis

Background: Wireless capsule endoscopy allows the identification of small intestinal protruded lesions, such as polyps, tumors, or venous structures. However, reading wireless capsule endoscopy images or movies is time-consuming, and minute lesions are easy to miss. Computer-aided diagnosis (CAD) has been applied to improve the efficacy of the reading process of wireless capsule endoscopy images or movies. However, there are no studies that systematically determine the performance of CAD models in diagnosing gastrointestinal protruded lesions. Objective: The aim of this study was to evaluate the diagnostic performance of CAD models for gastrointestinal protruded lesions using wireless capsule endoscopic images. Methods: Core databases were searched for studies based on CAD models for the diagnosis of gastrointestinal protruded lesions using wireless capsule endoscopy, and data on diagnostic performance were presented. A systematic review and diagnostic test accuracy meta-analysis were performed. Results: Twelve studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of protruded lesions were 0.95 (95% confidence interval, 0.93–0.97), 0.89 (0.84–0.92), 0.91 (0.86–0.94), and 74 (43–126), respectively. Subgroup analyses showed robust results. Meta-regression found no source of heterogeneity. Publication bias was not detected. Conclusion: CAD models showed high performance for the optical diagnosis of gastrointestinal protruded lesions based on wireless capsule endoscopy.

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Purpose: Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems. We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon’s diagnostic ability and sampling precision and investigate how operation complications could be minimized. Methods: We performed a literature search in PubMed, IEEE, Xplore, Elsevier, and Google Scholar, which yielded 28 relevant articles. Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer. Results: Hysteroscopic image texture analysis of the endometrium successfully distinguished benign from malignant conditions up to 91% of the time. In dermatologic studies, the accuracy of distinguishing nevi melanoma from benign disease fluctuated from 73% to 81%. Skin biopsies of basal cell carcinoma and melanoma exhibited an accuracy of 92.4%, sensitivity of 99.1%, and specificity of 93.3% and distinguished nonmelanoma and normal lesions from benign precancerous lesions with 91.9% and 82.8% accuracy, respectively. Gastrointestinal and endometrial examinations are still at the experimental phase. Conclusions: CATIA is a promising application for distinguishing normal from abnormal tissues during endoscopic procedures and minimally invasive surgeries. However, the efficacy of computer-assisted diagnostics in distinguishing benign from malignant states is still not well documented. Prospective and randomized studies are needed before CATIA is implemented in clinical practice.

Role of Artificial Intelligence in Video Capsule Endoscopy

Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.

Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader?

Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performances and unsatisfactory accuracy, deep learning systems raised up the expectancy of effective AI (Artificial Intelligence) application in SBCE reading. Automatic detection and characterization of lesions, such as angioectasias, erosions and ulcers, would significantly shorten reading time other than improve reader attention during SBCE review in routine activity. It is debated whether AI can be used as first or second reader. This issue should be further investigated measuring accuracy and cost-effectiveness of AI systems. Currently, AI has been mostly evaluated as first reader. However, second reading may play an important role in SBCE training as well as for better characterizing lesions for which the first reader was uncertain.

Artificial intelligence and capsule endoscopy: unravelling the future

The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.

Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection

This work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time-frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for training and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients. Each class of decomposed images is later given as input to a couple of considered convolutional neural network (CNN) models for training and testing in two different classification levels to recognize its predicted value. Afterward, the classification performance is measured through the following measuring indices: accuracy, precision, recall, specificity, and F1 score. The experimental result shows 97.25% and 93.75% of accuracy in the first level and second level of classification, respectively. Lastly, a comparative performance analysis is carried out with several other previously published works on a similar dataset where the proposed approach performs better than its contemporary methods.

Videomics: bringing deep learning to diagnostic endoscopy

PURPOSE OF REVIEW

Machine learning (ML) algorithms have augmented human judgment in various fields of clinical medicine. However, little progress has been made in applying these tools to video-endoscopy. We reviewed the field of video-analysis (herein termed 'Videomics' for the first time) as applied to diagnostic endoscopy, assessing its preliminary findings, potential, as well as limitations, and consider future developments.

RECENT FINDINGS

ML has been applied to diagnostic endoscopy with different aims: blind-spot detection, automatic quality control, lesion detection, classification, and characterization. The early experience in gastrointestinal endoscopy has recently been expanded to the upper aerodigestive tract, demonstrating promising results in both clinical fields. From top to bottom, multispectral imaging (such as Narrow Band Imaging) appeared to provide significant information drawn from endoscopic images.

SUMMARY

Videomics is an emerging discipline that has the potential to significantly improve human detection and characterization of clinically significant lesions during endoscopy across medical and surgical disciplines. Research teams should focus on the standardization of data collection, identification of common targets, and optimal reporting. With such a collaborative stepwise approach, Videomics is likely to soon augment clinical endoscopy, significantly impacting cancer patient outcomes.

Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy

BACKGROUND AND AIMS

Magnetically controlled capsule endoscopy (MCE) has become an efficient diagnostic modality for gastric diseases. We developed a novel automatic gastric lesion detection system to assist in diagnosis and reduce inter-physician variations. This study aimed to evaluate the diagnostic capability of the computer-aided detection system for MCE images.

METHODS

We developed a novel automatic gastric lesion detection system based on a convolutional neural network (CNN) and faster region-based convolutional neural network (RCNN). A total of 1,023,955 MCE images from 797 patients were used to train and test the system. These images were divided into 7 categories (erosion, polyp, ulcer, submucosal tumor, xanthoma, normal mucosa, and invalid images). The primary endpoint was the sensitivity of the system.

RESULTS

The system detected gastric focal lesions with 96.2% sensitivity (95% confidence interval [CI], 95.7%-96.5%), 76.2% specificity (95% CI, 75.97%-76.3%), 16.0% positive predictive value (95% CI, 15.7%-16.3%), 99.7% negative predictive value (95% CI, 99.74%-99.79%), and 77.1% accuracy (95% CI, 76.9%-77.3%) (sensitivity was 99.3% for erosions; 96.5% for polyps; 89.3% for ulcers; 87.2% for submucosal tumors; 90.6% for xanthomas; 67.8% for normal; and 96.1% for invalid images). Analysis of the receiver operating characteristic curve showed that the area under the curve for all positive images was 0.84. Image processing time was 44 milliseconds per image for the system and 0.38 ± 0.29 seconds per image for clinicians (P < .001). The kappa value of 2 times repeated reads was 1.

CONCLUSIONS

The CNN faster-RCNN-based diagnostic program system showed good performance in diagnosing gastric focal lesions in MCE images.

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

PURPOSE

Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two-step based procedure: region of interest selection and classification.

METHODS

The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation-maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity.

RESULTS

This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%.

CONCLUSIONS

This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

Gastrointestinal diagnosis using non-white light imaging capsule endoscopy

Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.

Role of Mucosal Protrusion Angle in Discriminating between True and False Masses of the Small Bowel on Video Capsule Endoscopy

The diagnosis of small-bowel tumors is challenging due to their low incidence, nonspecific presentation, and limitations of traditional endoscopic techniques. In our study, we examined the utility of the mucosal protrusion angle in differentiating between true submucosal masses and bulges of the small bowel on video capsule endoscopy. We retrospectively reviewed video capsule endoscopies of 34 patients who had suspected small-bowel lesions between 2002 and 2017. Mucosal protrusion angles were defined as the angle between the small-bowel protruding lesion and surrounding mucosa and were measured using a protractor placed on a computer screen. We found that 25 patients were found to have true submucosal masses based on pathology and 9 patients had innocent bulges due to extrinsic compression. True submucosal masses had an average measured protrusion angle of 45.7 degrees ± 20.8 whereas innocent bulges had an average protrusion angle of 108.6 degrees ± 16.3 (p < 0.0001; unpaired t-test). Acute angle of protrusion accurately discriminated between true submucosal masses and extrinsic compression bulges on Fisher's exact test (p = 0.0001). Our findings suggest that mucosal protrusion angle is a simple and useful tool for differentiating between true masses and innocent bulges of the small bowel.

Advances in automated tongue diagnosis techniques

Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work reviews the recent advances in tongue diagnosis, which is a significant constituent of traditional oriental medicinal technology, and explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system.

Deep learning for polyp recognition in wireless capsule endoscopy images

PURPOSE

Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge arises from the difficulty of robust characterization of images. This study aims to provide discriminative description of WCE images and assist physicians to recognize polyp images automatically.

METHODS

We propose a novel deep feature learning method, named stacked sparse autoencoder with image manifold constraint (SSAEIM), to recognize polyps in the WCE images. Our SSAEIM differs from the traditional sparse autoencoder (SAE) by introducing an image manifold constraint, which is constructed by a nearest neighbor graph and represents intrinsic structures of images. The image manifold constraint enforces that images within the same category share similar learned features and images in different categories should be kept far away. Thus, the learned features preserve large intervariances and small intravariances among images.

RESULTS

The average overall recognition accuracy (ORA) of our method for WCE images is 98.00%. The accuracies for polyps, bubbles, turbid images, and clear images are 98.00%, 99.50%, 99.00%, and 95.50%, respectively. Moreover, the comparison results show that our SSAEIM outperforms existing polyp recognition methods with relative higher ORA.

CONCLUSION

The comprehensive results have demonstrated that the proposed SSAEIM can provide descriptive characterization for WCE images and recognize polyps in a WCE video accurately. This method could be further utilized in the clinical trials to help physicians from the tedious image reading work.

ADAPTED BIT-PLANE PROBABILITY AND WAVELET-BASED ULCER DETECTION IN WIRELESS CAPSULE ENDOSCOPY IMAGES

Wireless capsule endoscopy (WCE) has been proven to be a robust technology which is able to ease diagnosing the GI tract diseases. It can be seen that a better computational algorithm is needed to analyze WCE images. Ulcer is one of the several diseases which are diagnosed using these images. Non-uniform lighting can complicate the detection process because it can change the color of tissue and make it seem darker or lighter than usual. This change of color makes the detection harder as the main feature of detecting ulcer as the color of the tissue. In this research work, adapted bit-planes are used to detect useful areas in images and then two sets of features, bit-plane probability and wavelet-based features, were extracted from the detected areas and used to classify them. Experimental results demonstrate a promising ground for further analysis of the channel-based bit-plane data and wavelet-based features.

Detection of small bowel tumor based on multi-scale curvelet analysis and fractal technology in capsule endoscopy

Wireless capsule endoscopy (WCE) has been a revolutionary technique to noninvasively inspect gastrointestinal (GI) tract diseases, especially small bowel tumor. However, it is a tedious task for physicians to examine captured images. To develop a computer-aid diagnosis tool for relieving the huge burden of physicians, the intestinal video data from 89 clinical patients with the indications of potential tumors was analyzed. Out of the 89 patients, 15(16.8%) were diagnosed with small bowel tumor. A novel set of textural features that integrate multi-scale curvelet and fractal technology were proposed to distinguish normal images from tumor images. The second order textural descriptors as well as higher order moments between different color channels were computed from images synthesized by the inverse curvelet transform of the selected scales. Then, a classification approach based on support vector machine (SVM) and genetic algorithm (GA) was further employed to select the optimal feature set and classify the real small bowel images. Extensive comparison experiments validate that the proposed automatic diagnosis scheme achieves a promising tumor classification performance of 97.8% sensitivity and 96.7% specificity in the selected images from our clinical data.

Automatic detection of small bowel tumors in endoscopic capsule images by ROI selection based on discarded lightness information

This paper addresses the problem of automatic detection of tumoral frames in endoscopic capsule videos by using features directly extracted from the color space. We show that tumor can be appropriately discriminated from normal tissue by using only color information histogram measures from the Lab color space and that light saturated regions are usually classified as tumoral regions when color based discriminative procedures are used. These regions are correctly classified if lightening is discarded becoming the tissue classifier based only on the color differences a and b of the Lab color space. While current state of the art systems for small bowel tumor detection usually rely on the processing of the whole frame regarding features extraction this paper proposes the use of fully automatic segmentation in order to select regions likely to contain tumoral tissue. Classification is performed by using Support Vector Machine (SVM) and Multilayer Perceptron (MLP) by using features from color channels a and b of the Lab color space. The proposed algorithm outperforms in more than 5% a series of other algorithms based on features obtained from the higher frequency components selected from Wavelets and Curvelets transforms while saving important computational resources. In a matter of fact the proposed algorithm is more than 25 times faster than algorithms requiring wavelet/curvelet and co-occurrence computations.

Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary Expectation Maximization

Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an important role to detect neurodegenerative diseases such as Alzheimer's disease. This is performed by measuring the substantial changes in the CSF flow dynamics, volume and/or pressure gradient. Magnetic resonance imaging (MRI) technique has become a prominent tool to quantitatively measure these changes and image segmentation method has been widely used to distinguish the CSF flows from the brain tissues. However, this is often hampered by the presence of partial volume effect in the images. In this paper, a new hybrid evolutionary spatial fuzzy clustering method is introduced to overcome the partial volume effect in the MRI images. The proposed method incorporates Expectation Maximization (EM) method, which is improved by the evolutionary operations of the Genetic Algorithm (GA) to differentiate the CSF from the brain tissues. The proposed improvement is incorporated into a spatial-based fuzzy clustering (SFCM) method to improve segmentation of the boundary curve of the CSF and the brain tissues. The proposed method was validated using MRI images of Alzheimer's disease patient. The results presented that the proposed method is capable to filter the CSF regions from the brain tissues more effectively compared to the standard EM, FCM, and SFCM methods.

Automated frame selection process for high-resolution microendoscopy

We developed an automated frame selection algorithm for high-resolution microendoscopy video sequences. The algorithm rapidly selects a representative frame with minimal motion artifact from a short video sequence, enabling fully automated image analysis at the point-of-care. The algorithm was evaluated by quantitative comparison of diagnostically relevant image features and diagnostic classification results obtained using automated frame selection versus manual frame selection. A data set consisting of video sequences collected in vivo from 100 oral sites and 167 esophageal sites was used in the analysis. The area under the receiver operating characteristic curve was 0.78 (automated selection) versus 0.82 (manual selection) for oral sites, and 0.93 (automated selection) versus 0.92 (manual selection) for esophageal sites. The implementation of fully automated high-resolution microendoscopy at the point-of-care has the potential to reduce the number of biopsies needed for accurate diagnosis of precancer and cancer in low-resource settings where there may be limited infrastructure and personnel for standard histologic analysis.

Capsule endoscopy: Present status and future expectation

Video capsule endoscopy (CE) since its introduction 13 years back, has revolutionized our approach to small intestinal diseases. Obscure gastrointestinal bleed (OGIB) continues to be the most important indication for CE with a high sensitivity, specificity as well as positive and negative predictive values. It is best performed during ongoing bleed or immediately thereafter. Overt OGIB has a higher diagnostic yield than occult OGIB. However, even in iron deficiency anemia, CE is emerging as important investigation after initial negative work up. In suspected Crohn’s disease (CD), CE has been shown superior to traditional imaging and endoscopic technique and should be considered after a negative ileocolonoscopy. Although CE has also been used for evaluating established CD, a high capsule retention rate precludes its use ahead of cross-sectional imaging. Celiac disease, particularly where gastro-duodenoscopy cannot be performed or is normal, can also be investigated by CE. Small bowel tumor, hereditary polyposis syndrome, and non-steroidal anti-inflammatory drugs induced intestinal damage are other indications for CE. Capsule retention is the only significant adverse outcome of CE and occurs mostly in presence of intestinal obstruction. This can be prevented by use of Patency capsule prior to CE examination. Presence of cardiac pacemaker and intracardiac devices continue to be relative contraindications for CE, though data do not suggest interference of CE with these devices. Major limitations of CE today include failure to control its movement from outside, inability of CE to acquire tissue for diagnosis, and lack of therapeutic help. With ongoing interesting and exciting developments taking place in these areas, these issues would be solved in all probability in near future. CE has the potential to become one of the most important tools in diagnostic and possibly in the therapeutic field of gastrointestinal disorder.

Evaluation and management of small-bowel tumors in the era of deep enteroscopy

In summary, small-intestine tumors are a rare phenomenon, but they are being discovered more frequently with newer diagnostic techniques. Prior studies of the small bowel were limited, making the diagnosis difficult. With the advent of CE and deep enteroscopy, gastroenterologists are finding these tumors at an earlier stage, thereby offering better management options for these patients. Although the incidence of small-bowel tumors has increased, the survival rates have remained the same. This may be a lag-time bias but could be a future area of research in this emerging field.

Texture and color based image segmentation and pathology detection in capsule endoscopy videos

This paper presents an in-depth study of several approaches to exploratory analysis of wireless capsule endoscopy images (WCE). It is demonstrated that versatile texture and color based descriptors of image regions corresponding to various anomalies of the gastrointestinal tract allows their accurate detection of pathologies in a sequence of WCE frames. Moreover, through classification of single pixels described by texture features of their neighborhood, the images can be segmented into homogeneous areas well matched to the image content. For both, detection and segmentation tasks the same procedure is applied which consists of features calculation, relevant feature subset selection and classification stages. This general three-stage framework is realized using various recognition strategies. In particular, the performance of the developed Vector Supported Convex Hull classification algorithm is compared against Support Vector Machines run in configuration with two different feature selection methods.

Video capsule endoscopy of the small bowel

PURPOSE OF REVIEW

Small bowel capsule endoscopy (SBCE) was introduced 13 years ago by Given Imaging (Yokneam, Israel). It has become one of the most important investigational tools of the small bowel. The capsule is swallowed with water after a 12 h fast, propelled via peristalsis through the gastrointestinal tract and excreted naturally. A nonvideo patency capsule was developed to confirm functional patency of the gastrointestinal tract.

RECENT FINDINGS

Four additional companies have introduced competitive small bowel video capsules, some of which are Food and Drug Admistration (FDA) approved. Due to the easiness of the procedure, SBCE has become a first-line tool to detect small bowel abnormalities. The main indications for SBCE include obscure gastrointestinal bleeding, suspected Crohn's disease, small bowel tumors and practically any abnormal small bowel imaging. New indications are emerging like small bowel motility and monitoring of drug therapy and mucosal healing. The present review will describe the available capsules in the market, the procedure itself, present indications and future expectations. It will focus on the PillCam SB of Given Imaging as it is the one on which most of the literature is written.

SUMMARY

Over the past 10 years, SBCE has become a routine, first-line investigational tool of many small bowel pathologies.