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Detection of abnormality in wireless capsule endoscopy images using fractal features

Jain S, Seal A, Ojha A, Krejcar O, Bureš J, Tachecí I, et al.

Comput Biol Med, 2020 12;127:104094.
PMID: 33152668 DOI: 10.1016/j.compbiomed.2020.104094

One of the most recent non-invasive technologies to examine the gastrointestinal tract is wireless capsule endoscopy (WCE). As there are thousands of endoscopic images in an 8-15 h long video, an evaluator has to pay constant attention for a relatively long time (60-120 min). Therefore the possibility of the presence of pathological findings in a few images (displayed for evaluation for a few seconds only) brings a significant risk of missing the pathology with all negative consequences for the patient. Hence, manually reviewing a video to identify abnormal images is not only a tedious and time consuming task that overwhelms human attention but also is error prone. In this paper, a method is proposed for the automatic detection of abnormal WCE images. The differential box counting method is used for the extraction of fractal dimension (FD) of WCE images and the random forest based ensemble classifier is used for the identification of abnormal frames. The FD is a well-known technique for extraction of features related to texture, smoothness, and roughness. In this paper, FDs are extracted from pixel-blocks of WCE images and are fed to the classifier for identification of images with abnormalities. To determine a suitable pixel block size for FD feature extraction, various sizes of blocks are considered and are fed into six frequently used classifiers separately, and the block size of 7×7 giving the best performance is empirically determined. Further, the selection of the random forest ensemble classifier is also done using the same empirical study. Performance of the proposed method is evaluated on two datasets containing WCE frames. Results demonstrate that the proposed method outperforms some of the state-of-the-art methods with AUC of 85% and 99% on Dataset-I and Dataset-II respectively.

Matched MeSH terms: Capsule Endoscopy*
A deep CNN model for anomaly detection and localization in wireless capsule endoscopy images

Jain S, Seal A, Ojha A, Yazidi A, Bures J, Tacheci I, et al.

Comput Biol Med, 2021 10;137:104789.
PMID: 34455302 DOI: 10.1016/j.compbiomed.2021.104789

Wireless capsule endoscopy (WCE) is one of the most efficient methods for the examination of gastrointestinal tracts. Computer-aided intelligent diagnostic tools alleviate the challenges faced during manual inspection of long WCE videos. Several approaches have been proposed in the literature for the automatic detection and localization of anomalies in WCE images. Some of them focus on specific anomalies such as bleeding, polyp, lesion, etc. However, relatively fewer generic methods have been proposed to detect all those common anomalies simultaneously. In this paper, a deep convolutional neural network (CNN) based model 'WCENet' is proposed for anomaly detection and localization in WCE images. The model works in two phases. In the first phase, a simple and efficient attention-based CNN classifies an image into one of the four categories: polyp, vascular, inflammatory, or normal. If the image is classified in one of the abnormal categories, it is processed in the second phase for the anomaly localization. Fusion of Grad-CAM++ and a custom SegNet is used for anomalous region segmentation in the abnormal image. WCENet classifier attains accuracy and area under receiver operating characteristic of 98% and 99%. The WCENet segmentation model obtains a frequency weighted intersection over union of 81%, and an average dice score of 56% on the KID dataset. WCENet outperforms nine different state-of-the-art conventional machine learning and deep learning models on the KID dataset. The proposed model demonstrates potential for clinical applications.

Matched MeSH terms: Capsule Endoscopy*
Fulltext Application of wireless power transmission systems in wireless capsule endoscopy: an overview

Basar MR, Ahmad MY, Cho J, Ibrahim F

Sensors (Basel), 2014 Jun 19;14(6):10929-51.
PMID: 24949645 DOI: 10.3390/s140610929

Wireless capsule endoscopy (WCE) is a promising technology for direct diagnosis of the entire small bowel to detect lethal diseases, including cancer and obscure gastrointestinal bleeding (OGIB). To improve the quality of diagnosis, some vital specifications of WCE such as image resolution, frame rate and working time need to be improved. Additionally, future multi-functioning robotic capsule endoscopy (RCE) units may utilize advanced features such as active system control over capsule motion, drug delivery systems, semi-surgical tools and biopsy. However, the inclusion of the above advanced features demands additional power that make conventional power source methods impractical. In this regards, wireless power transmission (WPT) system has received attention among researchers to overcome this problem. Systematic reviews on techniques of using WPT for WCE are limited, especially when involving the recent technological advancements. This paper aims to fill that gap by providing a systematic review with emphasis on the aspects related to the amount of transmitted power, the power transmission efficiency, the system stability and patient safety. It is noted that, thus far the development of WPT system for this WCE application is still in initial stage and there is room for improvements, especially involving system efficiency, stability, and the patient safety aspects.

Matched MeSH terms: Capsule Endoscopy/instrumentation*
Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors

Vicnesh J, Wei JKE, Ciaccio EJ, Oh SL, Bhagat G, Lewis SK, et al.

J Med Syst, 2019 Apr 26;43(6):157.
PMID: 31028562 DOI: 10.1007/s10916-019-1285-6

Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.

Matched MeSH terms: Capsule Endoscopy/methods*; Capsule Endoscopy/standards
Fulltext Capsule endoscopy in inflammatory bowel disease: when and how

Hilmi I, Kobayashi T

Intest Res, 2020 Jul;18(3):265-274.
PMID: 32623876 DOI: 10.5217/ir.2019.09165

Capsule endoscopy (CE) is emerging as an important investigation in inflammatory bowel disease (IBD); common types include the standard small bowel CE and colon CE. More recently, the pan-enteric CE was developed to assess the large and small bowel in patients with Crohn's disease (CD). Emerging indications include noninvasive assessment for mucosal healing (both in the small bowel and the colon) and detection of postoperative recurrence in patients with CD. Given the increasing adoption, several CE scoring systems have been specifically developed for IBD. The greatest concern with performing CE, particularly in CD, is capsule retention, but this can be overcome by performing cross-sectional imaging such as magnetic resonance enterography and using patency capsules before performing the procedure. The development of software for automated detection of mucosal abnormalities typically seen in IBD may further increase its adoption.

Matched MeSH terms: Capsule Endoscopy
Fulltext Bleeding detection in wireless capsule endoscopy videos - Color versus texture features

Pogorelov K, Suman S, Azmadi Hussin F, Saeed Malik A, Ostroukhova O, Riegler M, et al.

J Appl Clin Med Phys, 2019 Aug;20(8):141-154.
PMID: 31251460 DOI: 10.1002/acm2.12662

Wireless capsule endoscopy (WCE) is an effective technology that can be used to make a gastrointestinal (GI) tract diagnosis of various lesions and abnormalities. Due to a long time required to pass through the GI tract, the resulting WCE data stream contains a large number of frames which leads to a tedious job for clinical experts to perform a visual check of each and every frame of a complete patient's video footage. In this paper, an automated technique for bleeding detection based on color and texture features is proposed. The approach combines the color information which is an essential feature for initial detection of frame with bleeding. Additionally, it uses the texture which plays an important role to extract more information from the lesion captured in the frames and allows the system to distinguish finely between borderline cases. The detection algorithm utilizes machine-learning-based classification methods, and it can efficiently distinguish between bleeding and nonbleeding frames and perform pixel-level segmentation of bleeding areas in WCE frames. The performed experimental studies demonstrate the performance of the proposed bleeding detection method in terms of detection accuracy, where we are at least as good as the state-of-the-art approaches. In this research, we have conducted a broad comparison of a number of different state-of-the-art features and classification methods that allows building an efficient and flexible WCE video processing system.

Matched MeSH terms: Capsule Endoscopy/methods*
Fulltext How to assess regional and whole gut transit time with wireless motility capsule

Lee YY, Erdogan A, Rao SS

J Neurogastroenterol Motil, 2014 Apr 30;20(2):265-70.
PMID: 24840380 DOI: 10.5056/jnm.2014.20.2.265

Assessment of transit through the gastrointestinal tract provides useful information regarding gut physiology and patho-physiology. Although several methods are available, each has distinct advantages and limitations. Recently, an ingestible wire-less motility capsule (WMC), similar to capsule video endoscopy, has become available that offers a less-invasive, standardized, radiation-free and office-based test. The capsule has 3 sensors for measurement of pH, pressure and temperature, and collec-tively the information provided by these sensors is used to measure gastric emptying time, small bowel transit time, colonic transit time and whole gut transit time. Current approved indications for the test include the evaluation of gastric emptying in gastroparesis, colonic transit in constipation and evaluation of generalised dysmotility. Rare capsule retention and malfunc-tion are known limitations and some patients may experience difficulty with swallowing the capsule. The use of WMC has been validated for the assessment of gastrointestinal transit. The normal range for transit time includes the following: gastric empty-ing (2-5 hours), small bowel transit (2-6 hours), colonic transit (10-59 hours) and whole gut transit (10-73 hours). Besides avoiding the use of multiple endoscopic, radiologic and functional gastrointestinal tests, WMC can provide new diagnoses, leads to a change in management decision and help to direct further focused work-ups in patients with suspected disordered motility. In conclusion, WMC represents a significant advance in the assessment of segmental and whole gut transit and mo-tility, and could prove to be an indispensable diagnostic tool for gastrointestinal physicians worldwide.

Matched MeSH terms: Capsule Endoscopy
Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases

Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK

Curr Med Chem, 2021 Apr 04.
PMID: 33820515 DOI: 10.2174/0929867328666210405114938

There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.

Matched MeSH terms: Capsule Endoscopy
Fulltext Jejunal GIST: Hunting down an unusual cause of gastrointestinal bleed using double balloon enteroscopy. A case report

Dualim DM, Loo GH, Rajan R, Nik Mahmood NRK

Int J Surg Case Rep, 2019;60:303-306.
PMID: 31277041 DOI: 10.1016/j.ijscr.2019.06.053

INTRODUCTION: Gastrointestinal stromal tumours (GISTs) are the most common mesenchymal neoplasms of the alimentary tract but accounts for only 0.1-3% of all gastrointestinal neoplasms. The most common presentation of GISTs is acute or chronic gastrointestinal bleeding, in which the patient presents with symptomatic anaemia.
PRESENTATION OF CASE: With that in mind, we describe a 66-year-old man who presented with recurrent episodes of obscure gastrointestinal bleeding for two years. Video capsule endoscopy (VCE) showed several small telangiectasias in the proximal small bowel. Oral route double-balloon enteroscopy (DBE) revealed abnormal mucosa 165 cm from incisor with central ulceration and vascular component. He subsequently underwent surgical excision. The histopathological report confirmed the diagnosis of GIST arising from the jejunum. During his clinic follow up, he remains symptom-free with no evidence of recurrence.
DISCUSSION: The diagnosis of bleeding small intestine GISTs can be challenging as these are inaccessible by conventional endoscopy. Imaging modalities such as double-balloon enteroscopy, capsule endoscopy, CT angiography, intravenous contrast-enhanced multidetector row CT (MDCT) and magnetic resonance enterography (MRE) have been used to assist in the diagnosis of bleeding small intestine GISTs. The mainstay of management for small intestine GIST is complete surgical excision.
CONCLUSION: Bleeding jejunal GIST is very rare and only a handful of case reports have been published. The mainstay of management for small intestine GIST is complete surgical excision. It is essential to obtain a complete excision of localised disease and avoiding tumour spillage in order to reduce the risk of local recurrence and metastatic spread of GISTs.

Matched MeSH terms: Capsule Endoscopy