Use of rapid reading software to reduce capsule endoscopy reading times while maintaining accuracy

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.

Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy

Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.

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 within the small bowel: are we lagging behind?

PURPOSE OF REVIEW

The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date.

RECENT FINDINGS

The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician.

SUMMARY

Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.

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.

A Current and Newly Proposed Artificial Intelligence Algorithm for Reading Small Bowel Capsule Endoscopy

Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

Revising the European Society of Gastrointestinal Endoscopy (ESGE) research priorities: a research progress update

BACKGROUND

One of the aims of the European Society of Gastrointestinal Endoscopy (ESGE) is to encourage high quality endoscopic research at a European level. In 2016, the ESGE research committee published a set of research priorities. As endoscopic research is flourishing, we aimed to review the literature and determine whether endoscopic research over the last 4 years had managed to address any of our previously published priorities.

METHODS

As the previously published priorities were grouped under seven different domains, a working party with at least two European experts was created for each domain to review all the priorities under that domain. A structured review form was developed to standardize the review process. The group conducted an extensive literature search relevant to each of the priorities and then graded the priorities into three categories: (1) no longer a priority (well-designed trial, incorporated in national/international guidelines or adopted in routine clinical practice); (2) remains a priority (i. e. the above criterion was not met); (3) redefine the existing priority (i. e. the priority was too vague with the research question not clearly defined).

RESULTS

The previous ESGE research priorities document published in 2016 had 26 research priorities under seven domains. Our review of these priorities has resulted in seven priorities being removed from the list, one priority being partially removed, another seven being redefined to make them more precise, with eleven priorities remaining unchanged. This is a reflection of a rapid surge in endoscopic research, resulting in 27 % of research questions having already been answered and another 27 % requiring redefinition.

CONCLUSIONS

Our extensive review process has led to the removal of seven research priorities from the previous (2016) list, leaving 19 research priorities that have been redefined to make them more precise and relevant for researchers and funding bodies to target.

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.

Summary of The Third Capsule Endoscopy Global Summit

In order to emphasize the epidemic prevention during capsule endoscopy examinations, exhibit the latest achievements of capsule endoscopy, and strengthen international exchanges and cooperation in capsule endoscopy products, quality control, R&D, clinical applications, and talents, The Third Capsule Endoscopy Global Summit was held in Chongqing, China. The summit invited foreign experts to live online and remotely broadcast special academic speeches. The invited domestic experts brought the latest academic reports on the spot. A total of 17 medical experts presented a number of latest technologies and academic achievements in the field of capsule endoscopy from five levels. Professor Xue-Liang Jiang, President of the World Chinese Digestive Society and Editor-in-Chief of the World Chinese Journal of Digestology, was invited to give a report on the clinical application of capsule endoscopy.

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

Background: A new computer algorithm called Express-View has recently been introduced by Mirocam, but data concerning its application and efficacy are scarce.Objective: To evaluate the lesion detection rate, per-patient sensitivity and the diagnostic accuracy using Express-View.Methods: All patients who performed CE between January 2018 and June 2019, whose indication was obscure gastrointestinal bleeding (OGIB) and with findings on CE, were included. Lesions identified in conventional reading were selected and considered as reference.Results: Eighty-nine patients were included, 50.6% male, with a mean age of 68.4 years-old (±12.3). The Express-View mode detected 85.5% of lesions previously detected by conventional reading (524 out of 613). There were 89 missed lesions, mainly erosions or ulcers (44.9%) and angioectasias (38.2%). The lesion detection rate was found to be lower in the jejunum and ileum compared to extra-small bowel locations and duodenum (p = .04). Although Express-View had a per-patient sensitivity for all lesions of 56.2% and a per-patient sensitivity for all clinically significant lesions of 83.1%, it achieved a diagnostic accuracy of 91%.Conclusions: The per-patient sensitivity for all lesions was shown to be below expectations, although the lesion detection rate, the per-patient sensitivity for all clinically significant lesions, and the diagnostic accuracy were shown to be higher.