Abstract
Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.
Highlights
The incidence of colorectal cancer (CRC) has been increasing both in Japan and globally[1,2]
We included images of hyperplastic polyps (HPs) in the right-sided colon in the training set because interobserver agreement among pathologists for discriminating HPs and SSA/ Ps was reported to be challenging in histology[22]
We developed a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy
Summary
The incidence of colorectal cancer (CRC) has been increasing both in Japan and globally[1,2]. Colonoscopy following the removal of detected neoplastic lesions reduces the incidence and mortality of CRC3,4. Several studies described the characteristics of PCCRC as follows: (1) right-sided colon location, (2) small and early-stage cancer, and (3) flat morphology[8,9,10]. We hypothesized that artificial intelligence (AI) technology may help prevention of missed lesions during colonoscopy and reduce the skills gap among endoscopists, regarding the detection of flat lesions. Deep learning architectures are known to be suitable for quantifying images, exhibiting high capability in detection, classification, and segmentation[15]. We developed an AI system that automatically detects early signs of CRC during colonoscopy on an almost real-time basis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
