Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis

Table 1 Main characteristics of 10 studies included in meta-analysis

Ref.	Year	Location	Ethnicity	Study design	Sample size		Type of lesion	Imaging modality	AI model for segmentation	Diagnostic power
										Case				Control
					Case	Control				TP	FP	FN	TN	TP	FP	FN	TN
van der Putten et al[11]	2020	Netherlands	Caucasian	Cohort	80	80	Barrett’s neoplasia	BLI	ResNet	37	7	3	33	29	10	11	30
Yan et al[12]	2020	China	Asian	Cohort	80	80	GIM	NBI	EfficientNet B4	34	6	3	37	32	8	5	35
Struyvenberg et al[13]	2021	Netherlands	Caucasian	Cohort	134	134	Barrett’s neoplasia	VLE	VGG16	36	17	3	78	27	14	12	81
Hussein et al[14]	2022	United Kingdom	Caucasian	Cohort	61	61	Barrett’s neoplasia	WLE	FCNResNet 50	27	4	1	29	22	17	6	16
Zhao et al[15]	2022	China	Asian	Cohort	268	268	CAG	NBI	U-Net	96	10	13	149	66	27	43	132
Zhao et al[16]	2022	China	Asian	Paired cohort study	676	676	CAG	WLE	U-Net	284	10	54	328	212	61	126	277
Abdelrahim et al[17]	2023	United Kingdom	Caucasian	Cohort	75	75	Barrett’s neoplasia	WLE	SegNet	30	4	2	39	20	10	12	33
Zhao et al[18]	2023	China	Asian	Paired cohort study	524	524	CAG	NBI	U-Net	234	25	28	237	177	78	85	184
Meinikheim et al[19]	2024	Germany	Caucasian	Cohort	96	96	Barrett’s neoplasia	Multi-modal	DeepLab V3+	40	12	11	33	36	15	15	30
Tao et al[20]	2024	China	Asian	Cohort	119	119	CAG	WLE	U-Net++	74	3	4	38	55	4	23	37

BLI: Blue light imaging; GIM: Gastric intestinal metaplasia; NBI: Narrow band imaging; VLE: Volumetric laser endomicroscopy; WLE: White light endoscopy; CAG: Chronic atrophic gastritis; TP: True positive; FP: False positive; FN: False negative; TN: True negative; AI: Artificial intelligence.

Table 2 Technical characteristics of artificial intelligence-enhanced real-time computer-aided detection systems utilized in included studies

Ref.	Year	AI model type	Hardware configuration	Processing speed	Dataset size	Risk stratification tool
van der Putten et al[11]	2020	Integrated U-Net + Transfer Learning	Titan Xp 12GB GPU	< 2 sec/frame	500000 frames	None
Yan et al[12]	2020	EfficientNetB4	Not specified	20 fps	11000 frames	None
Struyvenberg et al[13]	2021	VGG16	Not specified	56 fps	318 video cases	None
Hussein et al[14]	2022	ResNet101 + FCN ResNet50	Not specified	48-56 fps	150000 frames	None
Zhao et al[15]	2022	U-Net	Not specified	Not specified	Not specified	None
Zhao et al[16]	2022	U-Net	GeForce RTX 3090	30 fps	Not specified	None
Abdelrahim et al[17]	2022	VGG16 + SegNet Hybrid Model	GeForce RTX 2080 Ti	30 fps	Training: 109071 frames; Testing: 75 video cases	None
Zhao et al[18]	2023	U-Net Extended Model	GeForce RTX 3090	30 fps	Not specified	OLGA
Meinikheim et al[19]	2024	DeepLabV3+ + ResNet50 + Mean-Teacher	Not specified	30 fps	Training: 51273 frames; Testing: 96 video cases	None
Tao et al[20]	2024	UNet++ + ResNet50 Dual Model System	Not specified	Not specified	Training: 119 video cases; Testing: 102 video cases	Kimura-Takemoto

AI: Artificial intelligence.

Table 3 Subgroup analysis of diagnostic effect

Subgroup	No. studies	No. patients	Sensitivity (AI-CAD)			Specificity (AI-CAD)			Sensitivity (endoscopist)			Specificity (endoscopist)			Difference of accuracy			Difference of log (DOR)
			Value	I2 (%)	P value	Value	I2 (%)	P value	Value	I2 (%)	P value	Value	I2 (%)	P value	Value	I2 (%)	P value	Value	I2 (%)	P value
Race
Asian	5	1667	0.890 (95%CI: 0.837-0.928)	53.0	0.070	0.930 (95%CI: 0.891-0.956)	70.8	0.009	0.658 (95%CI: 0.618-0.696)	43.9	0.039	0.810 (95%CI: 0.723-0.874)	75.3	0.001	0.167 (95%CI: 0.112-0.221)	22.9	0.118	-0.355 (95%CI: -1.110 to 0.401)	9.3	0.296
Others	5	446	0.896 (95%CI: 0.821-0.942)	49.9	0.083	0.831 (95%CI: 0.746-0.891)	16.3	0.276	0.703 (95%CI: 0.632-0.766)	0.0	0.746	0.721 (95%CI: 0.608-0.812)	76.1	0.002	0.142 (95%CI: 0.072-0.211)	64.8	0.022	0.151 (95%CI: -0.805 to 1.107)	62.6	0.032
Number of patients
< 100	5	392	0.895 (95%CI: 0.819-0.941)	49.2	0.089	0.842 (95%CI: 0.741-0.909)	31.4	0.242	0.731 (95%CI: 0.660-0.791)	17.6	0.235	0.703 (95%CI: 0.594-0.793)	64.0	0.027	0.147 (95%CI: 0.074-0.220)	60.2	0.041	0.213 (95%CI: -0.756 to 1.182)	60.3	0.035
≥ 100	5	1721	0.891 (95%CI: 0.838-0.928)	54.0	0.066	0.922 (95%CI: 0.870-0.954)	83.1	0.000	0.650 (95%CI: 0.614-0.684)	6.7	0.432	0.816 (95%CI: 0.742-0.872)	78.8	0.000	0.163 (95%CI: 0.106-0.219)	66.1	0.037	-0.387 (95%CI: -1.112 to 0.339)	3.4	0.306
Study design
Paired	2	1200	0.868 (95%CI: 0.783-0.923)	71.1	0.063	0.944 (95%CI: 0.879-0.975)	90.4	0.001	0.650 (95%CI: 0.586-0.710)	33.8	0.219	0.766 (95%CI: 0.600-0.877)	91.1	0.001	0.196 (95%CI: 0.125-0.266)	0.0	0.378	-0.774 (95%CI: -1.475 to -0.073)	0.0	0.536
Others	8	913	0.904 (95%CI: 0.856-0.937)	44.0	0.116	0.868 (95%CI: 0.803-0.913)	57.7	0.018	0.694 (95%CI: 0.640-0.743)	30.1	0.159	0.773 (95%CI: 0.684-0.842)	75.2	0.000	0.141 (95%CI: 0.092-0.189)	56.8	0.026	0.075 (95%CI: -0.542 to 0.693)	40.5	0.101
Type of lesion
BE	5	446	0.896 (95%CI: 0.821-0.942)	49.9	0.083	0.831 (95%CI: 0.746-0.891)	16.3	0.276	0.703 (95%CI: 0.632-0.766)	0.0	0.746	0.721 (95%CI: 0.608-0.812)	76.1	0.002	0.142 (95%CI: 0.072-0.211)	64.8	0.022	0.151 (95%CI: -0.805 to 1.107)	62.6	0.032
Others	5	1667	0.890 (95%CI: 0.837-0.928)	53.0	0.070	0.930 (95%CI: 0.891-0.956)	70.8	0.009	0.658 (95%CI: 0.618-0.696)	43.9	0.039	0.810 (95%CI: 0.723-0.874)	75.3	0.001	0.167 (95%CI: 0.112-0.221)	22.9	0.118	-0.355 (95%CI: -1.110 to 0.401)	9.3	0.296
Imaging modality
WLE	4	931	0.906 (95%CI: 0.835-0.949)	63.0	0.025	0.935 (95%CI: 0.877-0.967)	59.7	0.044	0.662 (95%CI: 0.596-0.722)	26.1	0.255	0.764 (95%CI: 0.634-0.858)	88.1	0.000	0.202 (95%CI: 0.134-0.269)	0.1	0.438	0.560 (95%CI: -0.458 to 1.579)	65.3	0.020
Others	6	1182	0.886 (95%CI: 0.827-0.926)	26.8	0.253	0.861 (95%CI: 0.790-0.911)	73.1	0.002	0.685 (95%CI: 0.630-0.735)	31.3	0.127	0.775 (95%CI: 0.679-0.849)	66.3	0.007	0.128 (95%CI: 0.074-0.183)	69.1	0.005	-0.530 (95%CI: -1.261 to 0.201)	0.9	0.536
AI model for segmentation
U-Net	4	1587	0.887 (95%CI: 0.828-0.927)	59.7	0.048	0.939 (95%CI: 0.904-0.961)	69.1	0.015	0.648 (95%CI: 0.611-0.682)	11.0	0.323	0.810 (95%CI: 0.710-0.881)	83.2	0.000	0.187 (95%CI: 0.155-0.220)	0.0	0.735	-0.376 (95%CI: -1.170 to 0.418)	17.4	0.187
Others	6	526	0.900 (95%CI: 0.835-0.941)	43.6	0.119	0.834 (95%CI: 0.765-0.886)	7.3	0.358	0.724 (95%CI: 0.660-0.779)	0.0	0.327	0.737 (95%CI: 0.632-0.820)	72.4	0.002	0.118 (95%CI: 0.069-0.167)	63.1	0.021	0.093 (95%CI: -0.783 to 0.970)	52.1	0.060

WLE: White light endoscopy; AI-CAD: Artificial intelligence-enhanced real-time computer-aided detection systems; DOR: Diagnostic odds ratio; BE: Barrett's esophagus; AI: Artificial intelligence.

Table 4 Meta-regression examining the impact of imaging modality and artificial intelligence model on artificial intelligence-enhanced real-time computer-aided detection systems specificity and accuracy gain

Outcome	Covariate	β	95%CI	P value	τ2	I2 (%)	R2 (%)
Specificity	Intercept	1.418	1.099-1.737	0	0	0	10000
	WLE (vs others)	0.816	0.307-1.324	0.002
	UNet (vs others)	0.986	0.558-1.415	0
Accuracy difference	Intercept	0.107	0.043-0.17	0.001	0.002	51.4	2498.5
	WLE (vs others)	0.059	-0.022-0.14	0.157
	UNet (vs others)	0.051	-0.029-0.131	0.212

WLE: White light endoscopy.