A structured light laser probe for gastrointestinal polyp size measurement: a preliminary comparative study

Abstract

Background and study aims Polyp size measurement is an important diagnostic step during gastrointestinal endoscopy, and is mainly performed by visual inspection. However, lack of depth perception and objective reference points are acknowledged factors contributing to measurement errors in polyp size. In this paper, we describe the proof-of-concept of a polyp measurement device based on structured light technology for future endoscopes. Patients and methods Measurement accuracy, time, user confidence, and satisfaction were evaluated for polyp size assessment by (a) visual inspection, (b) open biopsy forceps of known size, (c) ruled snare, and (d) structured light probe, for a total of 392 independent polyp measurements in ex vivo porcine stomachs. Results Visual assessment resulted in a median estimation error of 2.2 mm, IQR = 2.6 mm. The proposed probe can reduce the error to 1.5 mm, IQR = 1.67 mm ( P = 0.002, 95 %CI) and its performance was found to be statistically similar to using forceps for reference ( P = 0.81, 95 %CI) or ruled snare ( P = 0.99, 95 %CI), while not occluding the tool channel. Timing performance with the probe was measured to be on average 54.75 seconds per polyp. This was significantly slower than visual assessment (20.7 seconds per polyp, P = 0.005, 95 %CI) but not significantly different from using a snare (68.5 seconds per polyp, P = 0.73, 95 %CI). However, the probe’s timing performance was partly due to lens cleaning problems in our preliminary design. Reported average satisfaction on a 0 – 10 range was highest for the proposed probe (7.92), visual assessment (7.01), and reference forceps (7.82), while significantly lower for snare users with a score of 4.42 ( P = 0.035, 95 %CI). Conclusions The common practice of visual assessment of polyp size was found to be significantly less accurate than tool-based assessment, but easy to carry out. The proposed technology offers an accuracy on par with using a reference tool or ruled snare with the same satisfaction levels of visual assessment and without occluding the tool channel. Further study will improve the design to reduce the operating time by integrating the probe within the scope tip.

Usefulness of a Colonoscopy Cap with an External Grid for the Measurement of Small-Sized Colorectal Polyps: A Prospective Randomized Trial

“…Many methods such as ruler snares, ruler forceps, polyp sizing posters, laser grids, and calibrated hood indicators have been developed to increase the accuracy of on-site polyp size measurement [ 9 , 10 , 11 , 12 , 22 , 23 ]. Using devices such as ruler snares or ruler forceps through the working channel could involve additional procedure time than VE [ 9 , 10 , 11 ].…”

Magnetic Actuation Methods in Bio/Soft Robotics

“…[ 337 ] A noteworthy outcome of the EU project was the development of artificial intelligence algorithms to perform 3D lumen visual reconstruction, vision‐based closed‐loop control strategies and autonomous detection and measurement algorithms of colonic lesions, e.g., polyps. [ 338–342 ]…”

Frontiers of Robotic Colonoscopy: A Comprehensive Review of Robotic Colonoscopes and Technologies

“…The soft robotic colonoscope is featured by a high-definition stereo-camera with a custom-made optics, navigated by an external custom-made permanent magnet through a collaborative industrial anthropomorphic robot (COMAU SpA, Turin, Italy) [ 120 ]. A noteworthy outcome of the EU project was the development of AI algorithms to perform vision-based closed-loop control and autonomous detection and measurement of colonic lesions, e.g., polyps [ 121 , 122 , 123 , 124 ] ( Figure 6 A).…”

“…There is a growing number of studies indicating that CAD systems can perform at least as well if not even better than expert endoscopists [ 138 ], though additional validation and understanding of the clinical impact is still, without doubt, needed. Such endoscopic AI systems rely on large quantities of image or video data where human observers have annotated lesions to some degree either by denoting the presence of a polyp or by delineating its position and shape within the image ( Figure 7 A) [ 124 , 144 , 145 ]. Labelling is a time-consuming task, and this is a current system bottleneck because experts have limited bandwidth to perform annotation, which is necessary to train the AI models.…”