The New Zealand medical journal · 2022
Artificial intelligence improves adenoma detection rate during colonoscopy.
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Schauer Cameron, Chieng Michael, Wang Michael, Neave Michelle, Watson Sarah, Van Rijnsoever Marius, Walmsley Russell, Jafer Ali
Аннотация
BACKGROUND: Artificial intelligence-assisted colonoscopy (AIAC) has gained attention as a tool to assist with polyp detection during colonoscopy. Uncertainty remains as to the clinical benefit, given limited publications using different modules.
METHOD: A single-centre retrospective study was performed at Waitematā Endoscopy, a private endoscopy centre in Auckland, New Zealand. An Olympus Endo-AID module was utilised for the first time by 13 experienced endoscopists. Outcomes from AIAC between 10 March 2021 to 23 April 2021 were compared to a subsequent non-AI conventional colonoscopy (CC) control group from 27/4/21 to 20/6/21.
RESULTS: A total of 213 AIACs were compared with 213 CCs. Baseline patient age, gender, indication for procedure, bowel preparation scores and specialty of proceduralist (gastroenterologist or surgeon) were well matched (p>0.05). The withdrawal time was significantly longer in the AIAC group compared to CC controls (15 vs 13 minutes; p<0.001). The adenoma detection rate (ADR) was significantly higher in the AIAC group compared to CC group (47.9% vs 38.5%; odds ratio 1.59; 95% CI [1.05-2.41]; p=0.03). The overall polyp detection rate (PDR) was similar between groups (70% vs 70%; p=0.79). Analysis by polyp size, location and other histology was not significant between groups.
CONCLUSION: AI-assisted colonoscopy significantly improved ADR compared with conventional colonoscopy. Further research is required to understand its utility and impact on long-term clinical outcomes.
METHOD: A single-centre retrospective study was performed at Waitematā Endoscopy, a private endoscopy centre in Auckland, New Zealand. An Olympus Endo-AID module was utilised for the first time by 13 experienced endoscopists. Outcomes from AIAC between 10 March 2021 to 23 April 2021 were compared to a subsequent non-AI conventional colonoscopy (CC) control group from 27/4/21 to 20/6/21.
RESULTS: A total of 213 AIACs were compared with 213 CCs. Baseline patient age, gender, indication for procedure, bowel preparation scores and specialty of proceduralist (gastroenterologist or surgeon) were well matched (p>0.05). The withdrawal time was significantly longer in the AIAC group compared to CC controls (15 vs 13 minutes; p<0.001). The adenoma detection rate (ADR) was significantly higher in the AIAC group compared to CC group (47.9% vs 38.5%; odds ratio 1.59; 95% CI [1.05-2.41]; p=0.03). The overall polyp detection rate (PDR) was similar between groups (70% vs 70%; p=0.79). Analysis by polyp size, location and other histology was not significant between groups.
CONCLUSION: AI-assisted colonoscopy significantly improved ADR compared with conventional colonoscopy. Further research is required to understand its utility and impact on long-term clinical outcomes.