โก Quick Summary
A recent multicenter trial demonstrated that AI-assisted colonoscopy significantly improved the adenoma detection rate (ADR) among Danish endoscopists, achieving an overall increase of 12.5% and a remarkable 16.3% in the screening population.
๐ Key Details
- ๐ Participants: 795 patients (400 in AI group, 395 in control group)
- โ๏ธ Technology: AI-assisted colonoscopy (GI Genius, Medtronic)
- ๐ Primary outcome: Adenoma detection rate (ADR)
- ๐งโโ๏ธ Endoscopist classification: Experts (> 1000 colonoscopies) and non-experts (โค 1000 colonoscopies)
๐ Key Takeaways
- ๐ AI assistance led to a significant increase in ADR: 59.1% vs. 46.6% (P < 0.001).
- ๐จโโ๏ธ Experts showed a notable increase in ADR with AI: 59.9% vs. 47.3% (P < 0.002).
- ๐ฉบ Screening colonoscopies benefited greatly from AI, with ADR rising to 74.4% vs. 58.1% (P = 0.003).
- ๐ Polyp detection rate (PDR) was also higher in the AI group: 69.8% vs. 56.2% (P < 0.001).
- โ๏ธ Non-neoplastic resection rate (NNRR) remained unchanged, indicating no unnecessary resections.
๐ Background
The adenoma detection rate (ADR) is a crucial performance metric in colonoscopy, reflecting the effectiveness of endoscopists in identifying precancerous lesions. Variability in ADR among endoscopists can lead to disparities in patient outcomes. The integration of artificial intelligence (AI) in colonoscopy aims to standardize and enhance detection rates, thereby improving overall screening efficacy.
๐๏ธ Study
This study was a prospective, quasi-randomized, controlled trial conducted across four centers in Denmark. It involved patients aged 18 and older undergoing various types of colonoscopy. Participants were assigned to either AI-assisted colonoscopy or conventional methods, with endoscopists categorized based on their experience. The primary focus was to evaluate the impact of AI on ADR, particularly in different experience levels of endoscopists.
๐ Results
The findings revealed that the AI-assisted group achieved a significantly higher ADR of 59.1% compared to 46.6% in the control group (P < 0.001). Among expert endoscopists, the ADR increased to 59.9% from 47.3% (P < 0.002). Notably, in screening colonoscopies, the AI group reached an ADR of 74.4%, a substantial improvement over the 58.1% in the control group (P = 0.003). The PDR also showed a significant increase, while the NNRR remained stable, suggesting that the improvements were due to enhanced detection rather than unnecessary procedures.
๐ Impact and Implications
The implications of this study are profound. By demonstrating that AI can significantly enhance ADR, this research supports the integration of AI technologies in routine colonoscopy practices. This advancement could lead to earlier detection of colorectal cancer, ultimately improving patient outcomes and reducing mortality rates. The findings encourage further exploration into AI applications in other areas of healthcare, potentially transforming diagnostic procedures across various specialties.
๐ฎ Conclusion
This study highlights the transformative potential of AI-assisted colonoscopy in improving adenoma detection rates. With a significant overall increase of 12.5% and a remarkable 16.3% in screening populations, AI stands to revolutionize colonoscopy practices. As we continue to explore the integration of AI in healthcare, the future looks promising for enhanced diagnostic accuracy and patient care.
๐ฌ Your comments
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Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial.
Abstract
BACKGROUND AND STUDY AIMS: Adenoma detection rate (ADR) is a key performance measure with variability among endoscopists. Artificial intelligence (AI) in colonoscopy could reduce this variability and has shown to improve ADR. This study assessed the impact of AI on ADR among Danish endoscopists of varying experience levels.
PATIENTS AND METHODS: We conducted a prospective, quasi-randomized, controlled, multicenter trial involving patients aged 18 and older undergoing screening, surveillance, and diagnostic colonoscopy at four centers. Participants were assigned to AI-assisted colonoscopy (GI Genius, Medtronic) or conventional colonoscopy. Endoscopists were classified as experts (> 1000 colonoscopies) or non-experts (โค 1000 colonoscopies). The primary outcome was ADR. We performed a subgroup analysis stratified on endoscopist experience and a subset analysis of the screening population.
RESULTS: A total of 795 patients were analyzed: 400 in the AI group and 395 in the control group. The AI group demonstrated a significantly higher ADR than the control group (59.1% vs. 46.6%, P < 0.001). The increase was significant among experts (59.9% vs. 47.3%, P < 0.002) but not among non-experts. AI assistance significantly improved ADR (74.4% vs. 58.1%, P = 0.003) in screening colonoscopies. Polyp detection rate (PDR) was also higher in the AI group (69.8% vs. 56.2%, P < 0.001). There was no significant difference in the non-neoplastic resection rate (NNRR) (15.1% vs. 17.1%, P = 0.542).
CONCLUSIONS: AI-assisted colonoscopy significantly increased ADR by 12.5% overall, with a notable 16.3% increase in the screening population. The unchanged NNRR indicates that the higher PDR was due to increased ADR, not unnecessary resections.
Author: [‘Lagstrรถm RMB’, ‘Brรคuner KB’, ‘Bielik J’, ‘Rosen AW’, ‘Crone JG’, ‘Gรถgenur I’, ‘Bulut M’]
Journal: Endosc Int Open
Citation: Lagstrรถm RMB, et al. Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial. Improvement in adenoma detection rate by artificial intelligence-assisted colonoscopy: Multicenter quasi-randomized controlled trial. 2025; 13:a25215169. doi: 10.1055/a-2521-5169