Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality

Abstract

Background: Colonoscopy is the gold standard for polyp detection, but polyps may be missed. Artificial intelligence (AI) technologies may assist in polyp detection. To date, most studies for polyp detection have validated algorithms in ideal endoscopic conditions. Aim: To evaluate the performance of a deep-learning algorithm for polyp detection in a real-world setting of routine colonoscopy with variable bowel preparation quality. Methods: We performed a prospective, single-center study of 50 consecutive patients referred for colonoscopy. Procedural videos were analyzed by a validated deep-learning AI polyp detection software that labeled suspected polyps. Videos were then re-read by 5 experienced endoscopists to categorize all possible polyps identified by the endoscopist and/or AI, and to measure Boston Bowel Preparation Scale. Results: In total, 55 polyps were detected and removed by the endoscopist. The AI system identified 401 possible polyps. A total of 100 (24.9%) were categorized as “definite polyps;” 53/100 were identified and removed by the endoscopist. A total of 63 (15.6%) were categorized as “possible polyps” and were not removed by the endoscopist. In total, 238/401 were categorized as false positives. Two polyps identified by the endoscopist were missed by AI (false negatives). The sensitivity of AI for polyp detection was 98.8%, the positive predictive value was 40.6%. The polyp detection rate for the endoscopist was 62% versus 82% for the AI system. Mean segmental Boston Bowel Preparation Scale were similar (2.64, 2.59, P=0.47) for true and false positives, respectively. Conclusions: A deep-learning algorithm can function effectively to detect polyps in a prospectively collected series of colonoscopies, even in the setting of variable preparation quality.

Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

“…This technique facilitates the development of ML algorithms based upon relevant patient outcomes. detection [2][3][4][5][6] and classification of polyps based on their predicted histology [7][8][9]. However, additional ML techniques would benefit from a tight linkage of colonoscopy videos with patient-level data.…”

Computer-Aided Detection False Positives in Colonoscopy

“…However, the threshold required for reporting FPs is unsettled. One report suggested that only FPs > 2 s be reported [ 13 ], and another only reported FPs > 1 s [ 15 ], while the majority of FPs (i.e., more than 90%) lasted <0.5 s [ 13 ]. It is unknown whether ignoring the transient FPs (i.e., those lasting for <1 or 2 s) would increase the risk of missing a real polyp.…”

“…The definition of the presence of an FP is dependent on the judgment of the endoscopist. For studies analyzing CADe-overlaid videos, FPs were judged by a single expert reader or through the consensus of 2–3 reviewers [ 12 , 15 , 18 ]. Thus, there is room for errors due to subjectivity in these definitions.…”

“…In these reports, the false-positive rate (FPR) was reported either as a per-frame analysis [ 16 , 18 ] or a per-polyp analysis [ 15 , 17 ]. To determine the per-frame FPR, the videos were transformed into frames of still images.…”

Mesterséges intelligencia alkalmazásának lehetőségei a gasztroenterológiában és az endoszkópiában : Összefoglaló közlemény és saját eredményeink bemutatása

“…A vastagbéltükrözést számos úton tudják támogatni a kü lönböző AIrendszerek. Egyrészt az emberi hibából adódó tévedések csökkentésében, az elnézett polipok számának csökkentésében (20), akár különböző béltisztasági fok ese tén is (21), amely fontos minőségi szempont vastagbéltük rözés során. Másrészt a polip elnézési arány csökkentésében, a polipdetekció javításában és a felismerés pontosításában, a felismert polipok klasszifikációjában (22,23), valamint a szövettani eredmény előzetes becslésében (24,25).…”