Can AI Conquer the Late-Shift Dip in Colonoscopy Quality?
New research confirms that colonoscopies conducted later in an endoscopist’s shift are associated with a decline in adenoma detection and demonstrates that artificial intelligence (AI) can help eliminate the problem.
AI systems “may be a potential tool for minimizing time-related degradation of colonoscopy quality and further maintaining high quality and homogeneity of colonoscopies in high-workload centers,” Honggang Yu, MD, with the Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China, told Medscape Medical News.
The study was published online today in JAMA Network Open.
Fatigue a Factor?
Adenoma detection rate (ADR) is a critical quality measure of screening colonoscopy. Time of day is a well-known factor related to suboptimal ADR — with morning colonoscopies associated with improved ADR and afternoon colonoscopies with reduced ADR, Yu and colleagues write.
“However, an objective approach to solve this problem is still lacking,” Yu said. AI systems have been shown to improve the ADR, but the performance of AI during different times of the day remains unknown.
To investigate, the researchers randomly allocated 1780 consecutive patients to conventional colonoscopy or AI-assisted colonoscopy and compared the ADR for early and late colonoscopy sessions per half day.
Colonoscopy procedures were divided into two groups according to the end time of procedure. The early group included procedures started in the early session per half day (8:00 AM–10:59 AM or 1:00 PM–2:59 PM). The late group included procedures started in the later session per half day (11:00 AM–12:59 PM or 3:00 PM–4:59 PM).
A total of 1041 procedures were performed in the early sessions (357 conventional and 684 AI assisted). A total of 739 procedures were performed in the late sessions (263 conventional and 476 AI assisted).
In the unassisted colonoscopy group, later sessions per half day were associated with a decline in ADR (early vs late, 13.73% vs 5.7%; P = .005; odds ratio [OR], 2.42; 95% CI, 1.31 – 4.47).
However, with AI assistance, no such association was found in the ADR (early vs late, 22.95% vs 22.06%; P = .78; OR, 0.96; 95% CI, 0.71 – 1.29). AI provided the highest assistance capability in the last hour per half day.
The decline in ADR in late sessions (vs early sessions) was evident in different colonoscopy settings. The investigators say accrual of endoscopist fatigue may be an independent factor of time-related degradation of colonoscopy quality.
More Exploration Required
“We’re excited about the great potential of using the power of AI to assist endoscopists in quality control or disease diagnosis in colonoscopy practice, but it’s too early to see AI as the standard,” Yu told Medscape Medical News.
“Despite recent achievements in the design and validation of AI systems, much more exploration is required in the clinical application of AI,” Yu said.
Yu further explained that in addition to regulatory approval, the results of AI output must be trusted by the endoscopist, which remains a challenge for current AI systems that lack interpretability.
“Therefore, at the current stage of AI development, AI models can only serve as an extra reminder to assist endoscopists in colonoscopy,” Yu said.
This study was supported by the Innovation Team Project of Health Commission of Hubei Province. The authors have indicated no relevant financial relationships.
JAMA Netw Open. Published online January 31, 2023. Full text
For more news, follow Medscape on Facebook, Twitter, Instagram, YouTube, and LinkedIn
Source: Read Full Article
