Evaluation of the sensitivity of the Express View function in the Mirocam® capsule endoscopy software

A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence

Background

Magnetically controlled capsule endoscopy (MCCE) is a non-invasive, painless, comfortable, and safe equipment to diagnose gastrointestinal diseases (GID), partially overcoming the shortcomings of conventional endoscopy and wireless capsule endoscopy (WCE). With advancements in technology, the main technical parameters of MCCE have continuously been improved, and MCCE has become more intelligent.

Objectives

The aim of this systematic review was to summarize the research progress of MCCE and artificial intelligence (AI) in the diagnosis and treatment of GID.

Data Sources and Methods

We conducted a systematic search of PubMed and EMBASE for published studies on GID detection of MCCE, physical factors related to MCCE imaging quality, the application of AI in aiding MCCE, and its additional functions. We synergistically reviewed the included studies, extracted relevant data, and made comparisons.

Results

MCCE was confirmed to have the same performance as conventional gastroscopy and WCE in detecting common GID, while it lacks research in detecting early gastric cancer (EGC). The body position and cleanliness of the gastrointestinal tract are the main factors affecting imaging quality. The applications of AI in screening intestinal diseases have been comprehensive, while in the detection of common gastric diseases such as ulcers, it has been developed. MCCE can perform some additional functions, such as observations of drug behavior in the stomach and drug damage to the gastric mucosa. Furthermore, it can be improved to perform a biopsy.

Conclusion

This comprehensive review showed that the MCCE technology has made great progress, but studies on GID detection and treatment by MCCE are in the primary stage. Further studies are required to confirm the performance of MCCE.

Which model of small bowel capsule endoscopy has a better diagnostic yield? A systematic review and meta-analysis

Background and study aims: Small-bowel capsule endoscopy (SBCE) is a safe and efficient method for diagnosis of small-bowel diseases. Since its development, different models have appeared. The aim of this study was to analyze which of the different models of SBCE has the best diagnostic yield. Patients and methods: Extensive medical literature research was reviewed, using MESH terms, searching studies comparing different SBCE types. We analyzed the diagnostic yield of all the comparisons and when there were 2 or more studies that compared the same model of SBCEs, a meta-analysis was performed. Results: Ten eligible studies including 1065 SBCEs procedures were identified. The main indication was occult gastrointestinal bleeding in 9/10 studies. Two of them included anemia, chronic diarrhea and/or chronic abdominal pain. The indication in one article was celiac disease. In 9 studies, different types of SBCEs (MiroCam, Endocapsule, OMOM and CapsoCam) were compared with PillCam (SB, SB2 and SB3). Three studies compared MiroCam vs PillCam and CapsoCam vs PillCam, while two studies contrast Endocapsule vs PillCam. None of the SBCEs show superiority over PillCam [OR 0.78 (95%CI;0.60-1.01)]. One study compared SBCEs other than Pillcam (MiroCam vs Endocapsule). Nine studies did not find statistical differences between SBCEs, one showed better diagnostic yield of Mirocam compared with PillCam SB3 (p=0.02). The difference between these SBCE was not replayed in the metaanalysis [OR 0.77 (95%CI;0.49-1.21)]. Conclusions: Despite the appearance of new SBCE models, there are no differences in diagnostic yield; therefore, SBCE endoscopist’s performance should be based on experience and availability.

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.

Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.

Artificial intelligence in small intestinal diseases: Application and prospects

The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

Training, Reading, and Reporting for Small Bowel Video Capsule Endoscopy

Video capsule endoscopy (VCE) is an established modality for examining the small bowel. Formal training in interpretation and reporting of VCE examinations, along with assessment of performance metrics, is advocated for all gastroenterology fellowship programs. This review provides an overview of VCE minimum training requirements and competency assessment, cognitive and technical aspects of interpretation, and standardized reporting of findings. In order to optimize and advance the clinical utility of VCE, efforts must continue to promote and encourage consensus and standardization of training, definition and assessment of competence, enhancements of VCE reading tools, and use of appropriate nomenclature in VCE reports.