Field Of Colonoscopy Research Articles

Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization

AbstractColorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.

M3ResU-Net: a deep residual network for multi-center colorectal polyp segmentation based on multi-scale learning and attention mechanism

Colorectal polyps are considered as an important precursor of colorectal cancer (CRC) in clinical diagnosis. A network automatically and accurately segmenting polyps can recognize, locate and finally help to remove polyps, greatly reducing the misdiagnosis rate. Although many neural networks for polyp segmentation have been proposed, there still exist some difficulties including the diversity of image backgrounds, the jelly effect, and the various shapes and sizes of different polyps. These factors lead to the segmentation accuracy remaining to be improved. In this paper, we propose M3ResU-Net including multi-scale learning and attention mechanisms, aiming to segment multi-center colorectal polyps. First, we implement the contrast limited adaptive histogram equalization (CLAHE) and data augmentation for multi-center data. Then, channel and spatial attention mechanisms are introduced to focus on polyp features and suppress interference features. Finally, in order to balance small target segmentation and the acquisition of global information, multi-scale learning with dilated convolutions is employed. We compared other five polyp segmentation methods on three publicly available datasets. In single-center experiments, M3ResU-Net reaches a Dice similarity coefficient (DSC) exceeding that of the best compared method by over 2%. In various multi-center experiments, M3ResU-Net all achieves a DSC over 0.8. The results demonstrate that M3ResU-Net is capable of assisting clinicians in polyp segmentation in the field of colonoscopy, which provides important and reliable support to improve diagnostic efficiency.

Artificial intelligence-assisted colonoscopy: A review of current state of practice and research.

Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.

ESGE news and upcoming events May 2021

The ESGE Medtronic Research Award supports Artificial Intelligence (AI) research in the field of colonoscopy and we would like to encourage people to apply for our second application round. Winners will receive GI Genius systems, the number determined by the Award Committee on review of the successful award application plus direct costs will be covered up to a maximum total value of € 8,000 to the successful applicants’ institutions.

Artificial intelligence in colonoscopy - Now on the market. What's next?

Adoption of artificial intelligence (AI) in clinical medicine is revolutionizing daily practice. In the field of colonoscopy, major endoscopy manufacturers have already launched their own AI products on the market with regulatory approval in Europe and Asia. This commercialization is strongly supported by positive evidence that has been recently established through rigorously designed prospective trials and randomized controlled trials. According to some of the trials, AI tools possibly increase the adenoma detection rate by roughly 50% and contribute to a 7-20% reduction of colonoscopy-related costs. Given that reliable evidence is emerging, together with active commercialization, this seems to be a good time for us to review and discuss the current status of AI in colonoscopy from a clinical perspective. In this review, we introduce the advantages and possible drawbacks of AI tools and explore their future potential including the possibility of obtaining reimbursement.

Artificial intelligence and its impact on quality improvement in upper and lower gastrointestinal endoscopy.

Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.

Application of Robot in Colonoscopy

Background: Currently, there are about twenty percent of the subjects who feel pain and become uncomfortable while they are in the middle of the process of bowel disease examination by colonoscopy. Within the examination, the attending physician needs the assistance of nurses to complete the operation, which is extremely inconvenient. And there is still the chance of being hurt or infected by corrosive intestinal juice. Therefore, how to improve the efficiency of diagnosis, ensure the safety of operation, and reduce the pain of patients are the current research hotspots in the field of colonoscopy. Objective: The study aimed to provide an overview of the existing colonoscopy robots and introduce their classification and characteristics. Methods: This paper reviews various productions and patents related to the colonoscopy robot. The structural characteristics, differences and applications of the typical colonoscopy robot are also introduced. Results: The auxiliary colonoscopy robot and the active flexible colonoscopy robot were compared and analyzed, and the typical characteristics were concluded. The main problems in colonoscopy robot’s development were analyzed, and the development trend is foreseen. The current and future research works on the productions and patents on colonoscopy robots are discussed. Conclusion: Colonoscopy robot is helpful for reducing the doctor’s efforts while performing labor operation. It gives a more clear vision and offers an excellent interactive interface to make man-machine conversation easy. With the continuous improvement of technology, especially in the field of artificial intelligence and imaging processing technology, colonoscopy robot has been found to be more stable, comfortable, humanized, intelligent and secure.

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement.

BackgroundCecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum.MethodsIn order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest.ResultsA total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer.ConclusionInformation obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.