Efficacy and safety of three-dimensional magnetically assisted capsule endoscopy for upper gastrointestinal and small bowel examination.

BACKGROUND: Magnetically assisted capsule endoscopy (MACE) showed the feasibility for upper gastrointestinal examination. To further enhance the performance of conventional MACE, it is necessary to provide quality-improved and three-dimensional images. The aim of this clinical study was to determine the efficacy and safety of novel three-dimensional MACE (3D MACE) for upper gastrointestinal and small bowel examination at once. METHODS: This was a prospective, single-center, non-randomized, and sequential examination study (KCT0007114) at Dongguk University Ilsan Hospital. Adult patients who visited for upper endoscopy were included. The study protocol was conducted in two stages. First, upper gastrointestinal examination was performed using 3D MACE, and a continuous small bowel examination was performed by conventional method of capsule endoscopy. Two hours later, an upper endoscopy was performed for comparison with 3D MACE examination. The primary outcome was confirmation of major gastric structures (esophagogastric junction, cardia/fundus, body, angle, antrum, and pylorus). Secondary outcomes were confirmation of esophagus and duodenal bulb, accuracy for gastric lesions, completion of small bowel examination, 3D image reconstruction of gastric lesion, and safety. RESULTS: Fifty-five patients were finally enrolled. The examination time of 3D MACE was 14.84 ± 3.02 minutes and upper endoscopy was 5.22 ± 2.39 minutes. The confirmation rate of the six major gastric structures was 98.6% in 3D MACE and 100% in upper endoscopy. Gastric lesions were identified in 43 patients during 3D MACE, and 40 patients during upper endoscopy (Sensitivity 0.97). 3D reconstructed images were acquired for all lesions inspected by 3D MACE. The continuous small bowel examination by 3D MACE was completed in 94.5%. 3D MACE showed better overall satisfaction (3D MACE 9.55 ± 0.79 and upper endoscopy 7.75 ± 2.34, p<0.0001). There were no aspiration or significant adverse event or capsule retention in the 3D MACE examination. CONCLUSIONS: Novel 3D MACE system is more advanced diagnostic modality than the conventional MACE. And it is possible to perform serial upper gastrointestinal and small bowel examination as a non-invasive and one-step test. It would be also served as a bridge to pan-endoscopy.

An ingestible near-infrared fluorescence capsule endoscopy for specific gastrointestinal diagnoses.

Early diagnosis of gastrointestinal (GI) diseases is important to effectively prevent carcinogenesis. Capsule endoscopy (CE) can address the pain caused by wired endoscopy in GI diagnosis. However, existing CE approaches have difficulty effectively diagnosing lesions that do not exhibit obvious morphological changes. In addition, the current CE cannot achieve wireless energy supply and attitude control at the same time. Here, we successfully developed a novel near-infrared fluorescence capsule endoscopy (NIFCE) that can stimulate and capture near-infrared (NIR) fluorescence images to specifically identify subtle mucosal microlesions and submucosal lesions while capturing conventional white light (WL) images to detect lesions with significant morphological changes. Furthermore, we constructed the first synergetic system that simultaneously enables multi-attitude control in NIFCE and supplies long-term power, thus addressing the issue of excessive power consumption caused by the NIFCE emitting near-infrared light (NIRL). We performed in vivo experiments to verify that the NIFCE can specifically "light up" tumors while sparing normal tissues by synergizing with probes actively aggregated in tumors, thus realizing specific detection and penetration. The prototype NIFCE system represents a significant step forward in the field of CE and shows great potential in efficiently achieving early targeted diagnosis of various GI diseases.

AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study.

BACKGROUND: Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. METHODS: Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. FINDINGS: From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). INTERPRETATION: AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. FUNDING: ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.

Capsule endoscopy and panendoscopy: A journey to the future of gastrointestinal endoscopy.

In 2000, the small bowel capsule revolutionized the management of patients with small bowel disorders. Currently, the technological development achieved by the new models of double-headed endoscopic capsules, as miniaturized devices to evaluate the small bowel and colon [pan-intestinal capsule endoscopy (PCE)], makes this non-invasive procedure a disruptive concept for the management of patients with digestive disorders. This technology is expected to identify which patients will require conventional invasive endoscopic procedures (colonoscopy or balloon-assisted enteroscopy), based on the lesions detected by the capsule, i.e., those with an indication for biopsies or endoscopic treatment. The use of PCE in patients with inflammatory bowel diseases, namely Crohn's disease, as well as in patients with iron deficiency anaemia and/or overt gastrointestinal (GI) bleeding, after a non-diagnostic upper endoscopy (esophagogastroduodenoscopy), enables an effective, safe and comfortable way to identify patients with relevant lesions, who should undergo subsequent invasive endoscopic procedures. The recent development of magnetically controlled capsule endoscopy to evaluate the upper GI tract, is a further step towards the possibility of an entirely non-invasive assessment of all the segments of the digestive tract, from mouth-to-anus, meeting the expectations of the early developers of capsule endoscopy.

Detachable string magnetically controlled capsule endoscopy for the noninvasive diagnosis of esophageal diseases: A prospective, blind clinical study.

BACKGROUND: Traditional esophagogastroduodenoscopy (EGD), an invasive examination method, can cause discomfort and pain in patients. In contrast, magnetically controlled capsule endoscopy (MCE), a noninvasive method, is being applied for the detection of stomach and small intestinal diseases, but its application in treating esophageal diseases is not widespread. AIM: To evaluate the safety and efficacy of detachable string MCE (ds-MCE) for the diagnosis of esophageal diseases. METHODS: Fifty patients who had been diagnosed with esophageal diseases were prospectively recruited for this clinical study and underwent ds-MCE and conventional EGD. The primary endpoints included the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of ds-MCE for patients with esophageal diseases. The secondary endpoints consisted of visualizing the esophageal and dentate lines, as well as the subjects' tolerance of the procedure. RESULTS: Using EGD as the gold standard, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of ds-MCE for esophageal disease detection were 85.71%, 86.21%, 81.82%, 89.29%, and 86%, respectively. ds-MCE was more comfortable and convenient than EGD was, with 80% of patients feeling that ds-MCE examination was very comfortable or comfortable and 50% of patients believing that detachable string v examination was very convenient. CONCLUSION: This study revealed that ds-MCE has the same diagnostic effects as traditional EGD for esophageal diseases and is more comfortable and convenient than EGD, providing a novel noninvasive method for treating esophageal diseases.

Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm.

BACKGROUND: In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. METHODS: A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. RESULTS: The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CONCLUSIONS: CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.

Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution.

INTRODUCTION: Deep learning models can assess the quality of images and discriminate among abnormalities in small bowel capsule endoscopy (CE), reducing fatigue and the time needed for diagnosis. They serve as a decision support system, partially automating the diagnosis process by providing probability predictions for abnormalities. METHODS: We demonstrated the use of deep learning models in CE image analysis, specifically by piloting a bowel preparation model (BPM) and an abnormality detection model (ADM) to determine frame-level view quality and the presence of abnormal findings, respectively. We used convolutional neural network-based models pretrained on large-scale open-domain data to extract spatial features of CE images that were then used in a dense feed-forward neural network classifier. We then combined the open-source Kvasir-Capsule dataset (n = 43) and locally collected CE data (n = 29). RESULTS: Model performance was compared using averaged five-fold and two-fold cross-validation for BPMs and ADMs, respectively. The best BPM model based on a pre-trained ResNet50 architecture had an area under the receiver operating characteristic and precision-recall curves of 0.969±0.008 and 0.843±0.041, respectively. The best ADM model, also based on ResNet50, had top-1 and top-2 accuracies of 84.03±0.051 and 94.78±0.028, respectively. The models could process approximately 200-250 images per second and showed good discrimination on time-critical abnormalities such as bleeding. CONCLUSION: Our pilot models showed the potential to improve time to diagnosis in CE workflows. To our knowledge, our approach is unique to the Singapore context. The value of our work can be further evaluated in a pragmatic manner that is sensitive to existing clinician workflow and resource constraints.

Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.

OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study was to examine whether meaningful findings could be obtained when negative SBCE videos were reanalyzed with a deep convolutional neural network (CNN) model. METHODS: Clinical data of patients who received SBCE for suspected small-bowel bleeding at two academic hospitals between February 2018 and July 2020 were retrospectively collected. All SBCE videos read as negative were reanalyzed with the CNN algorithm developed in our previous study. Meaningful findings such as angioectasias and ulcers were finally decided after reviewing CNN-selected images by two gastroenterologists. RESULTS: Among 202 SBCE videos, 103 (51.0%) were read as negative by humans. Meaningful findings were detected in 63 (61.2%) of these 103 videos after reanalyzing them with the CNN model. There were 79 red spots or angioectasias in 40 videos and 66 erosions or ulcers in 35 videos. After reanalysis, the diagnosis was changed for 10 (10.3%) patients who had initially negative SBCE results. During a mean follow-up of 16.5 months, rebleeding occurred in 19 (18.4%) patients. The rebleeding rate was 23.6% (13/55) for patients with meaningful findings and 16.1% (5/31) for patients without meaningful findings (P = 0.411). CONCLUSION: Our CNN algorithm detected meaningful findings in negative SBCE videos that were missed by humans. The use of deep CNN for SBCE image reading is expected to compensate for human error.

Prospective study of diagnostic yields of flexible spectral imaging color enhancement installed in colon capsule endoscopy for colorectal polyps and tumors.

BACKGROUND AND AIMS: We prospectively determined the efficacy of flexible spectral imaging color enhancement (FICE) used with second-generation colon capsule endoscopy (CCE) for colorectal polyps and tumors (CRTs). METHODS: This study included optical colonoscopy within 4 months after CCE. Two colonoscopists independently reviewed CCE using white-light images (CCE-WL) and CCE using FICE images (CCE-FICE), respectively. Based on colonoscopic findings as the criterion standard, the diagnostic accuracy for CRTs was compared between CCE-WL and CCE-FICE. RESULTS: Of 89 enrolled patients (65 men and 24 women; 75 with CRTs including 36 with serrated lesions, 63 with adenomas, and 9 with adenocarcinomas), the per-patient detectability of CCE-FICE for the representative CRTs was significantly higher than that of CCE-WL: overall CRTs (CCE-WL, 79%; CCE-FICE, 88%; P = .0001), 6- to 9-mm CRTs (CCE-WL, 63%; CCE-FICE, 94%; P = .0055), and ≥6-mm CRTs (CCE-WL, 78%; CCE-FICE, 93%; P = .0159). The per-lesion sensitivity of CCE-FICE was significantly higher than that of CCE-WL for CRTs: overall (CCE-WL, 61%; CCE-FICE, 79%; P < .0001), <6 mm (CCE-WL, 53%; CCE-FICE, 69%; P < .0001), 6- to 9-mm CRTs (CCE-WL, 65%; CCE-FICE, 93%; P = .0007), slightly elevated CRTs (CCE-WL, 53%; CCE-FICE, 75%; P < .0001), tubular adenomas (CCE-WL, 61%; CCE-FICE, 79%; P < .0001), and serrated polyps (CCE-WL, 57%; CCE-FICE, 74%; P = .0022). Both modes detected all adenocarcinomas. No significant differences were found between CCE-WL and CCE-FICE of the per-lesion sensitivity for ≥10-mm CRTs (CCE-WL, 81%; CCE-FICE, 94%; P = .1138) or protruding CRTs (CCE-WL, 77%; CCE-FICE, 86%; P = .0614). Kappa coefficients for overall CRTs for CCE-WL and CCE-FICE were .66 and .64, respectively, which indicated substantial agreement. CONCLUSIONS: CCE-FICE improved the detection rates for all CRTs except adenocarcinomas, ≥10-mm polyps, and protruding polyps when compared with CCE-WL. (Clinical trial registration number: UMIN 000021125.).

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different systems. We compared endoscopist readings using an existing and a novel CNN system in a real-world SBCE setting. METHODS: Thirty-six complete SBCE videos, including 43 abnormal lesions (18 mucosal breaks, 8 angioectasia, and 17 protruding lesions), were retrospectively prepared. Three reading processes were compared: (A) endoscopist readings without CNN screening, (B) endoscopist readings after an existing CNN screening, and (C) endoscopist readings after a novel CNN screening. RESULTS: The mean number of small-bowel images was 14 747 per patient. Among these images, existing and novel CNN systems automatically captured 24.3% and 9.4% of the images, respectively. In this process, both systems extracted all 43 abnormal lesions. Next, we focused on the clinical usefulness. The detection rates of abnormalities by trainee endoscopists were not significantly different across the three processes: A, 77%; B, 67%; and C, 79%. The mean reading time of the trainees was the shortest during process C (10.1 min per patient), followed by processes B (23.1 min per patient) and A (33.6 min per patient). The mean psychological stress score while reading videos (scale, 1-5) was the lowest in process C (1.8) but was not significantly different between processes B (2.8) and A (3.2). CONCLUSIONS: Our novel CNN system significantly reduced endoscopist reading time and psychological stress while maintaining the detectability of abnormalities. CNN performance directly affects clinical utility and should be carefully assessed.

Active distance control in multi-capsule endoscopy via closed loop electromagnetic force between capsules.

Capsule endoscopy offers a non-invasive and patient-friendly method for imaging the gastrointestinal tract, boasting superior tissue accessibility compared to traditional endoscopy and colonoscopy. While advances have led to capsules capable of drug delivery, tactile sensing, and biopsy, size constraints often limit a single capsule from having multifunctionality. In response, we introduce multi-capsule endoscopy, where individually ingested capsules, each with unique functionalities, work collaboratively. However, synchronized navigation of these capsules is essential for this approach. In this paper, we present an active distance control strategy using a closed-loop system. This entails equipping one capsule with a sphere permanent magnet and the other with a solenoid. We utilized a Simulink model, incorporating (i) the peristalsis motion on the primary capsule, (ii) a PID controller, (iii) force dynamics between capsules through magnetic dipole approximation, and (iv) position tracking of the secondary capsule. For practical implementation, Hall effect sensors determined the inter-capsule distance, and a PID controller adjusted the solenoid's current to maintain the desired capsule spacing. Our proof-of-concept experiments, conducted on phantoms and ex vivo bovine tissues, pulled the leading capsule mimicking a typical human peristalsis speed of 1 cm/min. Results showcased an inter-capsule distance of 1.94 mm ± 0.097 mm for radii of curvature at 500 mm, 250 mm, and 100 mm, aiming for a 2-mm capsule spacing. For ex vivo bovine tissue, the achieved distance was 0.97 ± 0.28 mm against a target inter-capsule distance of 1 mm. Through the successful demonstration of precise inter-capsule control, this study paves the way for the potential of multi-capsule endoscopy in future research.

Rotational Platform for Real-Time Localization for Active Implantable Medical Devices.

This paper presents a sensor based localization system to localize active implantable medical devices i.e., Wireless Capsule Endoscopy (WCE). The importance of localizing the capsule arises once the images from the capsule detect the abnormalities in the Gastrointestinal tract (GI). A successful system can determine the location that associated with the abnormality for further medical investigation or treatment. The system proposed in this paper comprises a rotational platform that consists of magnetic sensors to detect the position of the embedded magnet in the capsule. The rotational platform provides advantageousness in terms of reducing the number of the sensors and increasing the monitoring accuracy during the real time movement.

Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.

BACKGROUND: Wireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames. METHODS: In this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy. RESULTS: 3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study. CONCLUSION: The results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.

Reclassification of Inflammatory Bowel Disease Type Unclassified by Small Bowel Capsule Endoscopy.

Background and Objectives: Ulcerative colitis (UC) and Crohn's disease (CD) are idiopathic inflammatory bowel diseases (IBDs) without a unique, gold standard diagnostic test. UC and Crohn's colitis are impossible to distinguish in approximately 10% of cases. The term IBD type unclassified (IBD-U) is recommended for cases of chronic colitis showing overlapping endoscopic, radiological, and biopsy histological features between UC and CD, while indetermined colitis is reserved for colectomy specimens. Our aim was to assess the role of small-bowel capsule endoscopy (SBCE) in the diagnostic work-up of IBD-U. Materials and Methods: We retrospectively studied the cases of IBD-U explored by SBCE in a tertiary referral gastroenterology center. Patients were investigated using SBCE after contraindications were excluded. Diagnostic criteria for small bowel CD consisted in more than three ulcerations, irregular ulcers, or stenosis, and the Lewis score was used for the quantification of inflammation. The immediate impact of reclassification and outcome data was recorded over a follow-up period of more than one year. Results: Twenty-eight patients with IBD-U were examined using SBCE. Nine patients had small bowel lesions that met the diagnostic criteria for CD, resulting in a reclassification rate of 32.1%. In five of these cases, the treatment was subsequently changed. In the remaining nineteen examinations, no significant findings were observed. There were no complications associated with SBCE. Median follow-up time was 32.5 months (range 12-60). During follow-up, twelve patients were classified as having UC, and seven remained as having an unclassified type; one case of colectomy, for medically refractory UC, was recorded. Conclusions: SBCE is a useful safe tool in the work-up of IBD-U, allowing reclassification in about one third of cases, with subsequent treatment modifications. SBCE may provide a definite diagnosis, enhance the comprehension of the disease's progression, and optimize the short- and long-term management strategy.

[Usefulness and safety of observing anorectal lesions by colon capsule endoscopy].

We investigated the findings of rectoanal lesions in 190 patients who underwent colon capsule endoscopy (CCE) at our hospital. Internal hemorrhoids were observed in 70 (36.8%) patients and rectal polyps in 19 (10%) patients. When conventional endoscopy (colonoscopy and double balloon endoscopy) was considered the gold standard, the sensitivity and specificity of rectal polyps were 75% and 93.4%, respectively, and those of internal hemorrhoids were 88.9% and 92.7%, respectively. The prevalence of constipation was significantly higher in the false-negative group for internal hemorrhoids, and the colonic transit time was significantly shorter in the false-negative and false-positive groups for rectal polyps. No adverse events occurred in any of the patients. CCE might be a useful and safe examination method for rectoanal lesions.

What Is the Optimal Bowel Preparation for Capsule Colonoscopy and Pan-intestinal Capsule Endoscopy? A Systematic Review and Meta-Analysis.

BACKGROUND: The rate of adequate cleansing (ACR) and complete examinations (CR) are key quality indicators in capsule colonoscopy (CC) and pan-intestinal capsule endoscopy (PCE). AIMS: To evaluate the efficacy of bowel preparation protocols regarding ACR and CR. METHODS: We conducted a systematic review and meta-analysis, search terms regarding colon capsule preparation, publication date from 2006/01, and date of search 2021/12, in six bibliographic databases. Multiple steps of the cleansing protocol were assessed: diet, adjunctive laxatives, purgative solution, use of prokinetic agents, and "booster". The meta-analytical frequency of ACR and CR was estimated, and subgroup analyses performed. Strategies associated with higher ACR and CR were explored using meta-analytical univariable and multivariable regression models. RESULTS: Twenty-six observational studies and five RCTs included (n = 4072 patients). The pooled rate of ACR was 72.5% (95% C.I. 67.8-77.5%; I2 = 92.4%), and the pooled rate of CR was 83.0% (95% C.I. 78.7-87.7%; I2 = 96.5%). The highest ACR were obtained using a low-fibre diet [78.5% (95% C.I. 72.0-85.6%); I2 = 57.0%], adjunctive laxatives [74.7% (95% C.I. 69.8-80.1%); I2 = 85.3%], and split dose < 4L polyethylene glycol (PEG) as purgative [77.5% (95% C.I. 68.4-87.8%); I2 = 47.3%]. The highest CR were observed using routine prokinetics prior to capsule ingestion [84.4% (95% C.I. 79.9-89.2%); I2 = 89.8%], and sodium phosphate (NaP) as "booster" [86.2% (95% C.I. 82.3-90.2%); I2 = 86.8%]. In univariable models, adjunctive laxatives were associated with higher ACR [OR 1.81 (95% C.I. 1.13; 2.90); p = 0.014]. CR was higher with routine prokinetics [OR 1.86 (95% C.I. 1.13; 3.05); p = 0.015] and split-dose PEG purgative [OR 2.03 (95% C.I. 1.01; 4.09), p = 0.048]. CONCLUSIONS: Main quality outcomes (ACR, CR) remain suboptimal for CC and PCE. Despite considerable heterogeneity, our results support low-fibre diet, use of adjunctive sennosides, split dose < 4L PEG, and routine prokinetics, while NaP remains the most consistent option as booster.

Associations between patient factors and successful colon capsule endoscopy - A prospective cohort study.

AIM: To establish patient factors associated with a successful colon capsule endoscopy (CCE) test. METHOD: This prospective cohort study used data collected from patients who underwent CCE as part of the ScotCap evaluation prior to April 2020. A CCE was defined as successful if the capsule visualized the whole colon and rectum (complete test) with sufficient bowel cleansing to permit assessment of the colonic mucosa (adequate bowel preparation). Factors from patients in symptomatic and surveillance groups were analysed for associations with a successful test, complete test, adequate bowel preparation and requirement for further procedure using univariate, multivariate logistic, least absolute shrinkage and selection operator regression. RESULTS: Data from 263 symptomatic and 137 surveillance patients were analysed. There was an association between a symptomatic patient's age and a successful test (OR = 0.97, 95% CI: 0.95-0.99), adequate bowel preparation (OR = 0.97, 95% CI: 0.94-1.00) and further procedure requirement (OR = 1.04, 95% CI: 1.02-1.06). An association was found, for symptomatic patients, between a faecal immunochemical test result in the range 10-399 µg/g and a further procedure (OR = 2.32, 95% CI: 1.23-4.48). In patients undergoing surveillance for previous colorectal cancer (OR = 0.42, 95% CI: 0.18-0.97), had previous bowel resection surgery (OR = 0.43, 95% CI: 0.19-0.98) or took a beta blocker medication (OR = 0.32, 95% CI: 0.11-0.88), an association was found with further procedure requirement. CONCLUSIONS: Among symptomatic patients, younger age was associated with obtaining a successful CCE test. Clinicians could consider patient selection based on these results to improve the rate of successful testing in clinical practice.

Video capsule endoscopy versus computed tomography enterography in assessing suspected small bowel bleeding: a systematic review and diagnostic test accuracy meta-analysis.

Both computed tomography enterography (CTE) and video capsule endoscopy (VCE) are used in identifying small intestinal pathology in patients with suspected small bowel bleeding (SSBB) following normal upper gastrointestinal endoscopy and colonoscopy. Evidence of the comparative accuracy of these two modalities is crucial for clinical and healthcare decision-making. Comprehensive electronic searches were performed for studies on CTE and/or VCE with reference standard(s). Study selection, data extraction and quality assessment were completed by two authors independently. The QUADAS-2 and QUADAS-C tools were used to assess risk of bias, and applicability. Meta-analysis was performed using a bivariate model to obtain summary estimates of sensitivity, specificity, positive and negative likelihood ratios. Twenty-five studies involving 1986 patients with SSBB were included. Four of these were head-to-head comparison of CTE and VCE. Overall, VCE provided significantly higher sensitivity of 0.74 (95% CI: 0.61-0.83) versus 0.47 (95% CI: 0.32-0.62) for CTE, while CTE showed significantly higher specificity of 0.94 (95% CI: 0.64-0.99) versus 0.53 (95% CI: .36-0.69) for VCE. The positive likelihood ratio of CTE was 7.36 (95% CI: 0.97-56.01) versus 1.58 (95% CI: 1.15-2.15) for VCE and the negative likelihood ratio was 0.49 (95% CI: 0.33-0.72) for VCE versus 0.56 (0.40-0.79) for CTE. A secondary analysis of only head-to-head comparative studies gave results that were similar to the main analysis. Certainty of evidence was moderate. Neither VCE nor CTE is a perfect test for identifying etiology of SSBB in small intestine. VCE was more sensitive while CTE was more specific. Clinicians should choose the appropriate modality depending on whether better sensitivity or specificity is required in each clinical scenario.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.