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Artificial Intelligence in Colonoscopy: Improving Medical Diagnostic of Colorectal Cancer

Stefanus Bernard, Arli Aditya Parikesit
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Abstract

Introduction- Colorectal cancer (CRC) is a development of abnormal cells either in colon or rectum. CRC considered being the 3rd leading cause of death in 2018 only behind lung and breast cancer. It first arises during pre-cancerous stages called as polyps. The detection and removal of polyp is important to increase the survival rate of patient. Various method of polyp detection are available. However, only colonoscopy remains the gold standard in detection and removal of polyps. Several studies showed how Artificial Intelligence (AI) used in colonoscopy area particularly in detecting polyps, assessing physicians and predicting patient with high risk of CRC. The aim of this study is to describe the involvement of AI in colonoscopy and its impact in reducing the 

Materials and methods– Search for journal articles conducted between May and June 2016 from various resources including PubMed and Google Scholar.  6 research journals were reviewed and all the advantages and limitations were discussed throughout this study. 

Results– Various study showed that AI able to improve medical diagnostic of CRC in several ways, including in the improvement of adenoma detection rate (ADR) in terms of medical diagnostic, finding physicians associated with high Adenoma Detection Rate (ADR) and predicting patients with high risk of CRC. In addition, the use of AI in colonoscopy also associated with limitations including require large amount of datasets and advance computational resources in order to generate accurate output. 

Conclusion– The utilization of AI in colonoscopy shows how it able to improve the diagnosis accuracy and survival rate of patients associated with CRC despite several limitations that were identified during the study. However in the future, instead of allowing it to fully automatically conducting diagnosis, it still needs to be accompanied by physicians conducting the operation as there is no hundred percent perfect algorithms.  


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DOI: http://dx.doi.org/10.30699/fhi.v9i1.209

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