Gastrointestinal endoscopy nurse assistance during colonoscopy and polyp detection

Liu, Aihong; Wang, Huashe; Lin, Yujun; Fu, Liping; Liu, Yanan; Shu-hong, Yan; Chen, Honglei

doi:10.1097/md.0000000000021278

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…A retrospective study by Lei and colleagues also aimed to investigate the performance of computer-aided diagnosis in colonoscopy and found no difference in ADR in the morning or in the afternoon [11] in patients who were investigated with AI. However, the authors did not include colonoscopies without the use of AI into their analysis as comparators.…”

Section: Discussionmentioning

confidence: 99%

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Influence of Artificial Intelligence on the Adenoma Detection Rate throughout the Day

Richter¹,

Bruns²,

Obst³

et al. 2022

Dig Dis

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Background: Artificial intelligence systems recently demonstrated an increase in polyp- and adenoma detection rate. Over the daytime the adenoma detection rate decreases as tiredness leads to a lack of attention. It is not clear if a polyp detection system with artificial intelligence leads to a constant adenoma detection over the day. Methods: We performed a database analysis of screening and surveillance colonoscopies with and without the use of AI. In both groups, patients were investigated with the same endoscopy equipment and by the same endoscopists. Only patients with good bowel preparation (BBPS > 6) were included. We correlated the daytime, the investigational time, day of the week and the adenoma and polyp detection. Results: A total of 303 colonoscopies were analyzed. In the AI+ group 163 endoscopies and in the AI- group 140 procedures were included. In both groups the total adenoma detection rate was equal (AI+ 0.39 vs AI- 0.43). The adenoma detection rate throughout the day had a significant decreasing trend in the group without the use of AI (p=0.015) whereas this trend was not present in the investigations that have been performed with AI (p=0.65). The duration of investigation did not show a significant difference between the groups (8.9 minutes in both groups). No relevant effect was noticed in adenoma detection between single days of the working week with or without the use of AI. Conclusion: AI helps to overcome the decay in adenoma detection over the daytime. This may be attributed to a constant awareness caused by the use of the AI system.

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Section: Discussionmentioning

confidence: 99%

“…The use of AI systems may have a similar effect as having additional investigators in the room, as shown by several studies. A meta-analysis clearly demonstrated that the participation of nurses in the detection of polyps of adenoma increased the ADR and PDR significantly [11].…”

Section: Discussionmentioning

confidence: 99%

Influence of Artificial Intelligence on the Adenoma Detection Rate throughout the Day

Richter¹,

Bruns²,

Obst³

et al. 2022

Dig Dis

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“…[109][110][111][112] A metaanalysis of these trials including 2268 patients demonstrated that assignment of nurses to observe the colonic inspection was associated with significantly increased adenoma detection (RR, 1.19; 95% CI, 1.07-1.32) and nonsignificantly increased advanced lesion detection (RR, 1.33; 95% CI, .95-1.87). 113 These studies suggest that colonoscopists should not only endeavor to retain experienced endoscopy nurses, but they should also encourage endoscopy room staff to participate in the inspection of the colonic mucosa whenever not otherwise occupied with their role in monitoring of the patient and assisting with the procedure.…”

Section: Interventions (Facility Level Provider Level)mentioning

confidence: 99%

Interventions to improve adenoma detection rates for colonoscopy

Shaukat¹,

Tuskey²,

Rao³

et al. 2022

Gastrointestinal Endoscopy

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“…There have been several experimental trials aimed at reducing the false-negative results in adenoma detection on colonoscopy. One study reported that an appropriate with-194 DOI: 10.1159/000528085 drawal technique is useful for decreasing the adenoma miss rate (AMR), whereas another suggested that the rate of false-negative results can be reduced by having a nurse or another doctor look at the endoscope screen during the examination [3,4]. However, although the support of nurses and doctors is considered to be highly effective, their participation in the diagnostic process is limited by the lack of adequate legal regulations.…”

Section: Introductionmentioning

confidence: 99%

Clinical Evaluation of Computer-Aided Colorectal Neoplasia Detection Using a Novel Endoscopic Artificial Intelligence: A Single-Center Randomized Controlled Trial

Nakashima¹,

Kitazawa²,

Fukuyama

et al. 2023

Digestion

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Introduction: Computer-aided diagnostic systems are emerging in the field of gastrointestinal endoscopy. In this study, we assessed the clinical performance of the computer-aided detection (CADe) of colonic adenomas using a new endoscopic artificial intelligence system. Methods: This was a single-center prospective randomized study including 415 participants allocated into the CADe group (n = 207) and control group (n = 208). All endoscopic examinations were performed by experienced endoscopists. The performance of the CADe was assessed based on the adenoma detection rate (ADR). Additionally, we compared the adenoma miss rate for the rectosigmoid colon (AMRrs) between the groups. Results: The basic demographic and procedural characteristics of the CADe and control groups were as follows: mean age, 54.9 and 55.9 years; male sex, 73.9% and 69.7% of participants; and mean withdrawal time, 411.8 and 399.0 s, respectively. The ADR was 59.4% in the CADe group and 47.6% in the control group (p = 0.018). The AMRrs was 11.9% in the CADe group and 26.0% in the control group (p = 0.037). Conclusion: The colonoscopy with the CADe system yielded an 11.8% higher ADR than that performed by experienced endoscopists alone. Moreover, there was no need to extend the examination time or request the assistance of additional medical staff to achieve this improved effectiveness. We believe that the novel CADe system can lead to considerable advances in colorectal cancer diagnosis.

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Gastrointestinal endoscopy nurse assistance during colonoscopy and polyp detection

Cited by 9 publications

References 22 publications

Influence of Artificial Intelligence on the Adenoma Detection Rate throughout the Day

Influence of Artificial Intelligence on the Adenoma Detection Rate throughout the Day

Interventions to improve adenoma detection rates for colonoscopy

Clinical Evaluation of Computer-Aided Colorectal Neoplasia Detection Using a Novel Endoscopic Artificial Intelligence: A Single-Center Randomized Controlled Trial

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