Current status of artificial intelligence colonoscopy on improving adenoma detection rate based on systematic review of multiple metanalysis

Table 1 Summary of metanalysis included in this systemic review

Ref.	Study design	Year of publication	Number of study and participants	Type of the study	Overall heterogeneity
Soleymanjahi et al[25]	Comparison of CADe-versus conventional colonoscopy performance	2024	33 studies, 27404 participants	Mixed	I² = 74%
Makar et al[17]	Impact of CADe systems on key colonoscopy quality indicators	2024	28 RCTs, 23861 participants	RCT	I² = 48%
Lee et al[15]	Evaluation of how study characteristics influence outcomes in AI-assisted polyp detection	2024	24 RCTs, 17413 participants	RCT	I² = 53%
Patel et al[26]	Assessment of benefits and harms associated with CADe in real-world colonoscopy	2024	8 studies, m9782 participants	Non-RCT	I² = 83%
Lou et al[18]	Prospective advantages and disadvantages of AI-assistance systems in colonoscopy	2024	12 studies, 11660 participants	Non-RCT	I² = 87%
Barua et al[27]	Comparison of ADR with and without AI utilization	2023	33 RCTs, 27404 participants	RCT	I² = 38.33%
Mehta et al[28]	Effectiveness of CADs in early colorectal cancer diagnosis compared to conventional colonoscopy	2023	15 studies, 174602 participants	Mixed	Not mentioned
Shiha et al[29]	Effectiveness of CADe in adenoma and polyp detection rates	2023	12 RCTs, 11340 participants	RCT	I2 = 64%
Zhang et al[30]	Accuracy measurement of AI-assisted colonoscopy	2023	8 RCTs, 2984 participants	RCT	Moderate to high heterogeneity
Nazarian et al[31]	Utilizing CADs for polyp detection and characterization	2023	13 RCTs, 15334 participants	RCT	I2 = 86%
Adiwinata et al[32]	Impact of AI colonoscopyon increasing ADR	2023	13 studies, 2958 participants	Mixed	I2 = 57%
Vadhwana et al[33]	Assessment of AI colonoscopy in real-time histological prediction	2023	80 studies, 25304 participants	RCT	Moderate to high heterogeneity
Hassan et al[34]	Summary of RCTs on CADe systems for colorectal neoplasia detection	2021	28 studies, 29079 participants	Mixed (RCTs and preclinical studies)	I2 = 42.1%
Lui et al[35]	AI's role in histology prediction and colorectal polyp detection	2021	10 RCTs, 6629 participants	RCT	I2 = 38.33%
Huang et al[36]	Evaluation of AI's impact on colonoscopy outcome metrics	2021	5 studies, 4311 participants	RCT	I2 = 36%
Li et al[37]	Evaluation of AI's effect on ADR	2021	26 RCT, 17413 participants	Mixed	I2 = 39.2%
Wang et al[38]	AI-assisted polyp detection and classification	2021	6 RCTs, 5058 participants	RCT	I2 = 69%
Ashat et al[39]	Determining the statistical significance of AI polyp detection for clinical adoption	2021	6 RCTs, 4996 participants	RCT	I2 = 28%
Deliwala et al[40]	Comparison of colorectal cancer detection between standard and AI-assisted colonoscopies	2021	5 RCTs, 4354 participants	RCT	I2 = 70%
Hassan et al[41]	Diagnostic accuracy of CADe systems in colorectal neoplasia detection	s2020	5 RCTs, 4311 Participants	RCT	I2 = 42%
Wei et al[42]	Analysis of CADe's effect on ADR and adenoma detection reproducibility	2020	18 studies, 969318 participants	Mixed	I2 = 91%
Mohan et al[43]	Comparison of ADR between CADe assisted colonoscopy and standard colonoscopy	2020	6 RCTs, 4962 participants	RCT	I2 = 56%

ADR: Adenoma detection rate; CADe: Computer assistance detection examination; RCT: Randomized control trail; AI: Artificial intelligence.

Table 2 Result of artificial intelligence colonoscopy on polyp detection and missing rate

Ref.	Adenoma detection with AI	Adenoma detection without AI	Heterogeneity I2	Adenoma missing rate	False positive
Soleymanjahi et al[25]	44.7% (RR: 1.21, 95%CI: 1.15-1.28)	37%	76%	AI: 16.1%, Conv: 35.3% (RR: 0.47, 95%CI: 0.36-0.60, I2 = 35%)
Makar et al[17]	20% (RR: 1.20, 95%CI: 1.14-1.27)	Lower than AI aid	64%	55% reduction (RR: 0.45, 95%CI: 0.37-0.54, I2 = 22.44%)	39% increase in total (RR: 1.39, 95%CI: 1.23-1.57, I2 = 1.81%)
Lee et al[15]	RR: 1.24, 95%CI: 1.17-1.31	Lower than AI aid	53%	RR: 0.44 (95%CI: 0.35-0.56, I2= 41%)
Patel et al[26]	44% (RR: 1.11, 95%CI: 0.97-1.28)	38%	83%
Lou et al[18]	RR 1.24, 95%CI: 115-1.33		78.87%	50.5% decrease (RR: 0.495, 95%CI: 0.390–0.627, I2 = 48.76%)	12.2% increase in total. Range (7.5%–16.9%)
Barua et al[27]	29.6% (RR: 1.52, 95%CI: 1.31-1.77)	19%	48%		11.2% increase in total. Range (7.1%-20.1%)
Mehta et al[28]	37.3% (OR: 1.91, 95%CI: 1.32–2.18)	30%
Shiha et al[29]	33.7% (RR: 1.76, 95%CI: 1.55-2.00)	23%	28%
Zhang et al[30]	33% (OR: 1.52- 1.72)
Nazarian et al[31]	34% (OR: 1.53, 95%CI: 1.32-1.77)	Lower than AI aid	45%
Adiwinata et al[32]	OR: 1.58 (95%CI: 1.37-1.82)	Lower than AI aid
Vadhwana et al[33]	No improvement		74%
Hassan et al[34]	44.0% (RR: 1.24, 95%CI: 1.16-1.33)	36%	70%	16% decrease (RR: 0.45, 95%CI: 0.35-0.58, I2 = 49%)	0.52 increase per colonoscopy, Mean Difference 018 polypectomies (95%CI: 0.11-0.26, I2 = 92%)
Lui et al[35]	24.2% (RR: 1.242, 95%CI: 1.159-1.332)		78%	50.5% decrease (RR: 0.495, 95%CI: 0.390-0.627, I2 = 48.76%)	12.20% increase in total
Huang et al[36]	35.4% (RR: 1.43, 95%CI: 1.33-1.53)	25%	36%		10.5% increase in total. Range (7.1%-17.3%)
Li et al[37]	OR: 1.75, 95%CI: 1.52-2.01		39%
Wang et al[38]	10% (AUC 0.79, 95%CI: 0.79-0.82)		90%
Ashat et al[39]	33.7% (OR 1.76, 95%CI: 1.55-2.00)	30%	28%
Deliwala et al[40]	77% (OR: 1.77, 95%CI: 1.50-2.08)		35%
Hassan et al[41]	36.6% (RR: 1.44, 95%CI: 1.27-1.62)	25%	42%
Wei et al[42]	36.3% (RR: 1.13, 95%CI: 1.01-1.28)	36%	64%
Mohan et al[43]	32.8% (RR: 1.5, 95%CI: 1.33-1.51)	21%	56%		10.3% increase in total (I2 = 93%)

RR: Relative risk; AI: Artificial intelligence; AUC: Area under the curve; OR: Odds ratio; ADR: Adenoma detection rate.

Table 3 Subgroup analysis of factors affecting adenoma detection rate in artificial intelligence colonoscopy

Ref.	Pooled adenoma detection rate	ADR based on subgroup
		Size			Polyp location		Polyp morphology
		diminutive lesions (≤ 5 mm)	Small lesions (6–9 mm)	Large lesions (≥ 10 mm), distal	Distal	Proximal cecum	Polypoid	Non polypoid	SSL
Soleymanjahi et al[25]	RR: 1.21, 95%CI: 1.15–1.28, Heterogeneity: I² = 76%
Makar et al[17]	RR: 1.20, 95%CI: 1.14-1.27, I² = 64%	46% increase in detection (IRR: 1.46, 95%CI: 1.19–1.80, P < 0.001, I² = 86.06%)	No significant improvement detection. IRR: 1.11, 95%CI: 0.94–1.31, P = 0.20, I² = 51.23%	No significant improvement detection. IRR: 1.24, 95%CI: 0.94–1.62, P = 0.12, I² = 31.35%					No significant improvement detection. RR: 1.10, P = 0.27
Lee et al[15]	RR: 1.24, 95%CI: 1.17–1.31, I² = 53%, P < 0.001
Patel et al[26]	RR: 1.11, 95%CI: 0.97–1.28, I² = 83%			No significant difference. RR: 0.84, 95%CI: 0.59–1.20, I² = 65%					No significant improvement. RR: 1.01, 95%CI: 0.84–1.20, I² = 0%
Lou et al[18]	RR: 1.13, 95%CI: 1.01-1.28, I² = 64%
Barua et al[27]	RR: 1.242, 95%CI: 1.159–1.332, I² = 78.87%	Largest improvement. RR: 1.27, 95%CI: 1.13–1.42, I² = 62%	Moderate improvement. RR: 1.24, 95%CI: 1.10–1.39, I² = 76%	No significant improvement. RR: 1.09, 95%CI: 0.98–1.21, I² = 84%	Smaller improvement. RR: 1.13, 95%CI: 1.05–1.22, I² = 51%	Significant improvement. RR: 1.19, 95%CI: 1.13–1.24, I² = 63%
Mehta et al[28]	RR: 1.76, 95%CI: 1.55-2.00	Largest improvement. OR: 2.07, 95%CI: 1.81–2.36, P < 0.001, I² = 27%	No significant improvement. OR: 14.7, 95%CI: 1.19–1.82, P = 0.004, I² = 0%	Moderate improvement. OR: 1.79, 95%CI: 1.27–2.53, P < 0.001, I² = 12%	Smaller improvement. OR: 1.96, 95%CI: 1.70–2.27, P < 0.001, I² = 0%	Moderate improvement. OR: 1.81, 95%CI: 1.57–2.10, P < 0.001, I² = 22%
Shiha et al[29]	OR: 1.52-1.72	Largest improvement. Weighted mean difference = -0.48, 95%CI: -0.81 to -0.15, P = 0.004, I² = 0%					AI detected fewer pedunculated polyps. OR: 0.64, 95%CI: 0.49–0.83, P < 0.001, I² = 0%
Zhang et al[30]	OR: 1.58, 95%CI 1.37-1.82, P = 0.003	Largest improvement. RR: 1.269, 95%CI: 1.133–1.421, I² = 62.34%	Moderate improvement. RR: 1.238, 95%CI: 1.009–1.520, I² = 75.76%	Moderate improvement. RR: 1.287, 95%CI: 0.984–1.684, I² = 83.66%	Smaller improvement. RR = 1.291, 95%CI: 1.092–1.526, I² = 50.91%	Moderate improvement. RR: 1.187, 95%CI: 1.134–1.242, I² = 9.79%	Smaller improvement. RR = 1.230, 95%CI: 1.050–1.441, I² = 63.37%	Better improvement. RR = 1.419, 95%CI: 1.204–1.671, I² = 57.63%
Nazarian et al[31]	No improvement
Adiwinata et al[32]	RR 1.24, 95%CI: 1.16-1.33	Largest improvement (medium differences = 0.167)	No improvement.	No improvement	Small improvement. Improvement (medium differences = 0.105)	Small improvement. (medium differences = 0.091)			Slight Improvement but statistically not significant
Vadhwana et al[33]	OR: 1.53, 95%CI 1.32–1.77, P < 0.001, I² = 45.5, P = 0.088
Hassan et al[34]	RR: 1.43, 95%CI: 1.33-1.53	RR: 1.71, 95%CI: 1.45–2.02, P < 0.001, I² = 42%	RR: 1.45, 95%CI: 1.23–1.71, P < 0.001, I²= 50%	RR: 1.73, 95%CI: 1.38–2.17, P < 0.00, I² = 55%	Moderate improvement. RR: 1.70, 95%CI: 1.40-2.06, P < 0.001, I² = 50%	No significant improvement. RR: 1.28, 95%CI: 0.92–1.78, P = 0.48	No significant improvement. RR: 1.13, 95%CI: 0.86-1.48, P = 0.37, I² = 60%	Significant improvement. RR: 2.00, 95%CI: 1.60 2.50, P < 0.001, I² = 50%	Moderate improvement. RR: 1.75, 95%CI: 1.50–2.04, P < 0.001, I² = 40%
Lui et al[35]	OR: 1.75, 95%CI: 1.52–2.01
Huang et al[36]	OR: 1.75, 95%CI: 1.36–2.25	Sensitivity 95%. I² = 96.86%
Li et al[37]	OR: 1.76, 95%CI: 1.55-2.00	Significantly improved. OR: 2.07, 95%CI: 1.81–2.36, I² = 27%	Improved. OR: 1.47, 95%CI: 1.19–1.82, I² = 0%	Significantly improved. OR: 1.79, 95%CI: 1.27–2.53. Heterogeneity. I² = 12%	Significantly improved. OR: 1.96, 95%CI: 1.70–2.27, I² = 0%	Significantly improved. OR: 1.81, 95%CI: 1.57–2.10, I² = 22%
Wang et al[38]	OR: 1.77, 95%CI: 1.50–2.08, P < 0.001	Significant improvement. OR: 1.33, 95%CI: 1.12–1.59, P < 0.001	No improvement. OR: 0.96, 95%CI: 0.96-1.33, P = 0.79	No improvement. OR: 1.43, 95%CI: .0.87-1.78, P = 0.24	No improvement. OR: 0.19, 95%CI: 1.88-1.43, P = 0.25	No improvement. OR: 1.00, 95%CI: 0.76–1.32, P = 0.99
Ashat et al[39]	RR: 1.44, 95%CI: 1.27-1.62	Significant improvement. RR: 1.69, 95%CI: 1.48–1.84, I² = 63%	Moderate improvement. RR: 1.44, 95%CI: 1.19–1.75, I² = 4%	Moderate improvement. RR: 1.46, 95%CI: 1.04–2.06, I² = 0%	Moderate improvement. RR: 1.68, 95%CI: 1.50–1.88, I² = 0%	Moderate improvement. RR: 1.59, 95%CI: 1.34–1.88, I² = 55%	Moderate improvement. RR: 1.54, 95%CI: 1.40–1.68, I² = 0%	Moderate improvement. RR: 1.78, 95%CI: 1.47–2.15, I² = 71%	No improvement. RR: 1.52, 95%CI: 1.14–2.02, I² = 0%, P = 0.33
Deliwala et al[40]	95%CI: 22.2%–37.0%	Significant improvement. Mean difference: = +0.15, 95%CI: 0.12–0.18, P < 0.001, I² = 0.02%	Minimal improvement. Mean difference: +0.03, 95%CI: 0.01–0.05, P = 0.01, I² = 0.04%	No improvement. Mean difference: +0.01. 95%CI: 0.00–0.02, P = 0.76, I² = 0.19%
Hassan et al[41]	OR: 1.75, 95%CI: 1.52–2.01, I² = 39.2%, P = 0.160	Significant improvement. AUC = 0.98, sensitivity = 93.5%, specificity = 90.8%
Wei et al[42]	RR: 1.42, 95%CI: 1.33–1.51, P < 0.00001, I² = 9%	Significant improvement. RR: 1.39, 95%CI: 1.15–1.69, P = 0.0008		Moderate improvement. RR: 1.56, 95%CI: 1.12–2.19, P = 0.009	Moderate improvement. RR: 1.56, 95%CI: 1.12–2.19, P = 0.009	Significant improvement. RR: 1.36, 95%CI: 1.18–1.58, P < 0.0001			Moderate improvement. RR: 1.75, 95%CI: 1.54–1.98, P = 0.07
Mohan et al[43]	RR: 1.5, 95%CI: 1.33-1.51, I² = 32.8%

SSL: Sessile serrated lesions; RR: Relative risk; OR: Odd ration; AUC: Area under curve; ADR: Adenoma detection rate.

Table 4 Subgroup analysis of factors affecting adenoma detection rate in artificial intelligence colonoscopy

Ref.	ADR based on endoscopist		Indication for colonoscopy
	Baseline ADR < 25%	Baseline ADR ≥ 40%	Screening	Surveillance
Soleymanjahi et al[25]	RR: 1.39, 95%CI: 1.18-1.63. Similar improvements in ADR, but data were less stratified	RR: 1.14, 95%CI: 1.08-1.21. (> 1000 colonoscopies): ADR improved by 19%. (RR: 1.19, 95%CI: 1.11–1.27, P < 0.001, I2 = 24.51%)	Significant improvement. RR: 1.21, 95%CI: 1.15-1.28, I2 = 76%	Less improvement. RR: 1.14, 95%CI: 1.05-1.24, I2 = 65%
Makar et al[17]	Improvement. RR: 1.23, 95%CI: 1.16–1.32, I2 = 0%, P < 0.001	Improvement. RR: 1.24, 95%CI: 1.15–1.34, I2 = 45%, P < 0.001	Improvement. RR: 1.13, 95%CI: 1.07–1.19, I2 = 15%, P < 0.001	Improvement. RR: 1.33, 95%CI: 1.23–1.45, I2 = 42%, P < 0.001
Lee et al[15]	No significant improvement. RR: 1.01, 95%CI: 0.84–1.20		16% increase in ADR compared to standard colonoscopy, but the result was not statistically significant. RR for ADR: 1.16, 95%CI: 0.83–1.62, I2 = 77%	5% increase in ADR but the result was not statistically significant. RR for ADR: 1.05, 95%CI: 0.92–1.19, I2 = 62%
Lou et al[18]	Significant improvement. RR: 1.42, 95%CI: 1.28–1.58, I2 = 65%	Minimal improvement. RR: 1.12, 95%CI: 1.03–1.22, I2 = 52%
Deliwala et al[40]	Significantly improved. AUC: 0.97, 95%CI: 0.96–0.98, P < 0.01	Moderate improved. AUC: 0.90, 95%CI: 0.87–0.93, P < 0.01

RR: Relative risk; OR: Odd ration; AUC: Area under curve; ADR: Adenoma detection rate.

Table 5 Overview of commercially available artificial intelligence systems and the key distinctions among them

Characteristics	GI genius	CAD EYE	EndoScreener	ENDO-AID	SOKUT	Endoangel
Developer	Medtronic	Fujifilm	Beijing-based company weiming vision technology	Olympus	Iterative	Wuhan endoangel medical technology co
AI function	CADe	CADe + CADx	CADe	CADe + CADx	CADe	CADe + CADx
System description	Real-time polyp detection with visual/audio alerts. Integrated with HD colonoscopes	Optimized for Fujifilm scopes. Useful for optical biopsy	Compatible with multiple endoscope brands. High sensitivity for small polyps. Validated in Chinese populations		Focuses on adenoma detection. Trained for sessile adenoma. Validated in United States multicenter trials	Focus in adenoma detection and diagnosis. Can assess bowel preparation quality. Additional benefit in diagnosis gastric neoplasm
Regulatory approval	United States Food and Drug Administration Europe, Select markets in Asia, Australia, and the Middle East	Pharmaceuticals and Medical Devices Agency (Japan). United States Food and Drug Administration	National Medical Products Administration (China)	Europe, Middle East, Africa. United States Food and Drug Administration	United States Food and Drug Administration	National Medical Products Administration (China)
Workflow integration	Flexible	Fujifilm-only	Flexible	Olympus-only	Flexible	Flexible
Clinical adaption	United State. Europe	Japan	China	Japan. Europe	United State	China
ADR improvement	Consistent ADR. Lower non-neoplastic resection. Prolonged withdrawal time. No improvement with SSL	Prioritize large/advanced adenoma. Less sensitive over subtle and small adenoma. No improvement with SSL	Improvement in ADR. Increase non-neoplastic resection	Consistent ADR. Significant SSL benefits	Improvement in ADR. Increase non-neoplastic resection	Real-time detection and withdrawal time monitoring. Alerts for suboptimal technique. Improves withdrawal technique. Reduces miss rates

CADe: Computer assistance detection examination; CADx: Computer assistance detection diagnosis; SSL: Sessile serrated lesion; ADR: Adenoma detection rate.