Development of a computed cleansing score to assess quality of bowel preparation in colon capsule endoscopy

Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.

Artificial Intelligence in Colon Capsule Endoscopy—A Systematic Review

Background and aims: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability. Automated reviewing of colon capsule endoscopy images using artificial intelligence could be timesaving while providing an objective and reproducible outcome. This systematic review aims to provide an overview of the available literature on artificial intelligence for reviewing colonic mucosa by colon capsule endoscopy and to assess the necessary action points for its use in clinical practice. Methods: A systematic literature search of literature published up to January 2022 was conducted using Embase, Web of Science, OVID MEDLINE and Cochrane CENTRAL. Studies reporting on the use of artificial intelligence to review second-generation colon capsule endoscopy colonic images were included. Results: 1017 studies were evaluated for eligibility, of which nine were included. Two studies reported on computed bowel cleansing assessment, five studies reported on computed polyp or colorectal neoplasia detection and two studies reported on other implications. Overall, the sensitivity of the proposed artificial intelligence models were 86.5–95.5% for bowel cleansing and 47.4–98.1% for the detection of polyps and colorectal neoplasia. Two studies performed per-lesion analysis, in addition to per-frame analysis, which improved the sensitivity of polyp or colorectal neoplasia detection to 81.3–98.1%. By applying a convolutional neural network, the highest sensitivity of 98.1% for polyp detection was found. Conclusion: The use of artificial intelligence for reviewing second-generation colon capsule endoscopy images is promising. The highest sensitivity of 98.1% for polyp detection was achieved by deep learning with a convolutional neural network. Convolutional neural network algorithms should be optimized and tested with more data, possibly requiring the set-up of a large international colon capsule endoscopy database. Finally, the accuracy of the optimized convolutional neural network models need to be confirmed in a prospective setting.

New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times

BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26−340) and 13 min (range 3−85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.

Lavage, Simethicone, and Prokinetics-What to Swallow with a Video Capsule

The development of new capsules now allows endoscopic diagnosis in all segments of the gastrointestinal tract and comes with new needs for differentiated preparation regimens. Although the literature is steadily increasing, the results of the conducted trials on preparation are sometimes conflicting. The ingestion of simethicone before gastric and small bowel capsule endoscopy for prevention of air bubbles is established. The value of a lavage before small bowel capsule endoscopy (SBCE) is recommended, although not supported by all studies. Ingestion in the morning before the procedure seems useful for the improvement of mucosa visualization. Lavage after swallowing of the capsule seems to improve image quality, and in some studies also diagnostic yield. Prokinetics has been used with first generation capsules to shorten gastric transit time and increase the rate of complete small bowel visualization. With the massively prolonged battery capacity of the new generation small bowel capsules, prokinetics are only necessary in significantly delayed gastric emptying as documented by a real-time viewer. Lavage is crucial for an effective colon capsule or pan-intestinal capsule endoscopy. Mainly high or low volume polyethylene glycol (PEG) is used. Apart from achieving optimal cleanliness, propulsion of the capsule by ingested boosts is required to obtain a complete passage through the colon within the battery lifetime. Boosts with low volume sodium picosulfate (NaP) or diatrizoate (gastrografin) seem most effective, but potentially have more side effects than PEG. Future research is needed for more patient friendly but effective preparations, especially for colon capsule and pan-intestinal capsule endoscopy.

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.

Colon capsule endoscopy is a viable alternative to colonoscopy for the investigation of intermediate- and low-risk patients with gastrointestinal symptoms: results of a pilot study

Background and study aims  Colon capsule endoscopy (CCE) is a recommended viable alternative to colonoscopy for colonic visualisation in a variety of clinical settings with proven efficacy in polyp detection, surveillance, screening and Inflammatory Bowel Disease (IBD) assessment. CCE efficacy in an unselected average risk symptomatic cohort has yet to be established. The aim of this study was to determine the feasibility of CCE imaging assessment in average risk symptomatic patients as an alternative to colonoscopy with and without additional biomarker assessment. Patients and methods  This was a prospective, single-center comparison study of colonoscopy, CCE and biomarker assessment. Results  Of 77 invited subjects, 66 underwent both a CCE and colonoscopy. A fecal immunochemical test (FIT) and fecal calprotectin (FC) were available in 56 and 59 subjects. In all 64 % (n = 42) had any positive finding with 16 (24 %) found to have significant disease (high-risk adenomas, IBD) on colonoscopy. The CCE completion rate was 76 %, five (8 %) had an inadequate preparation, the CCE polyp detection rate was high at 35 %. The sensitivity, specificity, positive and negative predictive values of CCE for significant disease were 81 %, 98 %, 93 % and 94 % respectively. In addition, three (5 %) significant small bowel diagnoses were made on CCE. FC and FIT were frequently elevated in patients with both colitis (5/7, 71 %) and high-risk adenomas (4/7 57 %). While both had a low positive predictive value for clinically significant disease, FIT 32 % and FC 26 %. Conclusions  CCE is a safe and effective alternative to colonoscopy in symptomatic average risk patients with or without the addition of biomarker screening.

Scoring Systems for Clinical Colon Capsule Endoscopy-All You Need to Know

In the constantly developing era of minimal diagnostic invasiveness, the role of colon capsule endoscopy in colonic examination is being increasingly recognised, especially in the context of curtailed endoscopy services due to the COVID-19 pandemic. It is a safe diagnostic tool with low adverse event rates. As with other endoscopic modalities, various colon capsule endoscopy scores allow the standardisation of reporting and reproducibility. As bowel cleanliness affects CCE’s diagnostic yield, a few operator-dependent scores (Leighton–Rex and CC-CLEAR scores) and a computer-dependent score (CAC score) have been developed to grade bowel cleanliness objectively. CCE can be used to monitor IBD mucosal disease activity through the UCEIS and the panenteric CECDAIic score for UC and CD, respectively. CCE may also have a role in CRC screening, given similar sensitivity and specificity rates to conventional colonoscopy to detect colonic polyps ≥ 10 mm and CRC. Given CCE’s diagnostic yield and reproducible clinical scores with high inter-observer agreements, CCE is fast becoming a suitable alternative to conventional colonoscopy in specific patient populations.

CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy

Background and study aims  Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods  Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results  Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis.