Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions

Abstract

Background The efficiency of artificial intelligence as computer‐aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non‐false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work. Objectives Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, suppresses distracting CADe detections, and reliably detects endoscopic interventions. Methods A total of 580 different examination videos from 9 different centers using 4 different processor types were screened for instruments and represented the training dataset (519,856 images in total, 144,217 contained a visible instrument). The test dataset included 10 full‐colonoscopy videos that were analyzed for the recognition of visible instruments and detections by a commercially available CADe system (GI Genius, Medtronic). Results The test dataset contained 153,623 images, 8.84% of those presented visible instruments (12 interventions, 19 instruments used). The convolutional neuronal network reached an overall accuracy in the detection of visible instruments of 98.59%. Sensitivity and specificity were 98.55% and 98.92%, respectively. A mean of 462.8 frames containing distracting CADe detections per colonoscopy were avoided using the convolutional neuronal network. This accounted for 95.6% of all distracting CADe detections. Conclusions Detection of endoscopic instruments in colonoscopy using artificial intelligence technology is reliable and achieves high sensitivity and specificity. Accordingly, the new convolutional neuronal network could be used to reduce distracting CADe detections during endoscopic procedures. Thus, our study demonstrates the great potential of artificial intelligence technology beyond mucosal assessment.

Advances in artificial intelligence and computer science for computer-aided diagnosis of colorectal polyps: current status

“…In addition, availability of computer systems able to assess independent polyp characteristics could provide possibilities for automated polyp description for endoscopy reports [75]. When combined with algorithms for purposes such as recognition of resection methods [110], this might significantly ease administrative burdens for endoscopists. In the last place, optimizing accuracy…”

“…In addition, availability of computer systems able to assess independent polyp characteristics could provide possibilities for automated polyp description for endoscopy reports 75 . When combined with algorithms for purposes such as recognition of resection methods 110 , this might significantly ease administrative burdens for endoscopists. In the last place, optimizing accuracy of endoscopic assessment of different polyp characteristics could aid in development of more trustworthy clinical decision-making algorithms or prediction models involving specific polyp characteristics 111 112 113 .…”

Direct comparison of multiple computer-aided polyp detection systems

“…Customization of systems will be increasingly implemented 21 . In this regard, Endo-AID uses two different detection types, A and B.…”

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer

“…The introduction of a semi-automated video annotation tool by Krenzer et al 43 , aimed at streamlining the machine learning annotation process for medical professionals, was foundational for the training of accurate AI systems. This was complemented by the development of a benchmark dataset, ENDOTEST, by Fitting et al 44 , designed to rigorously evaluate computer-aided polyp detection systems, and the efforts of Brand et al 45,46 to develop, evaluate, and analyze the effectiveness of deep learning models and commercially available AI systems in real-world clinical settings were highlighted, showcasing the practical applications and challenges of integrating AI into current medical practices.…”