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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.
(1) Background: Interventional endoscopic procedures are growing more popular, requiring innovative instruments and novel techniques. Three-dimensional printing has demonstrated great potential for the rapid development of prototypes that can be used for the early assessment of various concepts. In this work, we present the development of a flexible endoscopic instrument and explore its potential benefits. (2) Methods: The properties of the instrument, such as its maneuverability, flexibility, and bending force, were evaluated in a series of bench tests. Additionally, the effectiveness of the instrument was evaluated in an ex vivo porcine model by medical experts, who graded its properties and performance. Furthermore, the time necessary to complete various interventional endoscopic tasks was recorded. (3) Results: The instrument achieved bending angles of ±216° while achieving a bending force of 7.85 (±0.53) Newtons. The time needed to reach the operating region was 120 s median, while it took 70 s median to insert an object in a cavity. Furthermore, it took 220 s median to insert the instrument and remove an object from the cavity. (4) Conclusions: This study presents the development of a flexible endoscopic instrument using three-dimensional printing technology and its evaluation. The instrument demonstrated high bending angles and forces, and superior properties compared to the current state of the art. Furthermore, it was able to complete various interventional endoscopic tasks in minimal time, thus potentially leading to the improved safety and effectiveness of interventional endoscopic procedures in the future.
Pilot study of a new freely available computer-aided polyp detection system in clinical practice
(2022)
Purpose
Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system.
Methods
We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092).
Results
During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80–200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7–2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70–100).
Conclusion
EndoMind’s ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.