Artificial intelligence system for the detection of Barrett’s esophagus

Table 1 Baseline characteristics of development and test sets

NBI image number	Training set, n = 771		Label balance, n = 1187		Valid set, n = 193		Test set, n = 160
	n	%	n	%	n	%	n	%
No Barrett’s esophagus	563	73.02	563	47.43	141	73.06	90	56.25
Barrett’s esophagus	208	26.98	624	52.57	52	26.94	70	43.75

NBI: Narrow-band imaging.

Table 2 Comparison of performance using different pretrained models

Pre-trained model	Training			Validation			Test
	ACC	SEN	SPE	ACC	SEN	SPE	ACC	SEN	SPE
EfficientNetV2B1	0.9196	0.8702	0.9378	0.7876	0.6346	0.8440	0.7625	0.6857	0.8222
EfficientNetV2B2	0.9702	0.9663	0.9716	0.8497	0.7500	0.8865	0.8500	0.8286	0.8667
EfficientNetV2B3	0.9170	0.8798	0.9307	0.7824	0.6154	0.8440	0.8125	0.7429	0.8667
ResNet50	0.8962	0.7885	0.9361	0.8290	0.5577	0.9291	0.7063	0.5429	0.8333
DenseNet201	0.8755	0.8173	0.8970	0.7772	0.5577	0.8582	0.7312	0.6143	0.8222
VGG16	0.2698	1.0000	0.0000	0.2694	1.0000	0.0000	0.4375	1.0000	0.0000

ACC: Accuracy; SEN: Sensitivity; SPE: Specificity.

Table 3 Confusion matrices

Confusion matrices		Predicted class
		Training, n = 771		Validation, n = 193		Test, n = 160
		BE	No BE	BE	No BE	BE	No BE
Actual class	BE	206	2	50	2	66	4
	No BE	2	561	3	138	5	85

BE: Barrett’s esophagus.

Table 4 A comparative summary of the state-of-the-art approaches for the binary classification of Barrett’s esophagus

Ref.	Validation (train:valid:test)	Valid	Test
Ebigbo et al[33], 2020	Holdout; Image (129:N/A:62); Test patients:14	ACC: N/A	ACC: 89.9%
		AUC: N/A	AUC: N/A
		SEN: N/A	SEN: 83.7%
		SPE: N/A	SPE: 100.0%
		PPV: N/A	PPV: N/A
		NPV: N/A	NPV: N/A
Hussein et al[26], 2022	Holdout; Video (64:11:44); Patients: 118	ACC: N/A	ACC: N/A
		AUC: N/A	AUC: 93%
		SEN: N/A	SEN: 91%
		SPE: N/A	SPE: 79%
		PPV: N/A	PPV: N/A
		NPV: N/A	NPV: N/A
Abdelrahim et al[25], 2023	Holdout; Image (816:471:N/A); Video (161:N/A:75); Case (161:34:75)	ACC: 94.7%	ACC: 92.0%
		AUC: 0.898	AUC: 0.964
		SEN: 95.3%	SEN: 93.8%
		SPE: 94.5%	SPE: 90.7%
		PPV: 83.6%	PPV: 88.2%
		NPV: 98.6%	NPV: 95.1%
Our approach, 2023	Holdout image; Image (771:193:160); Case (579:145:86)	ACC: 97.41%	ACC: 94.37%
		AUC: 97.01%	AUC: 94.37%
		SEN: 96.15%	SEN: 94.29%
		SPE: 97.87%	SPE: 94.44%
		PPV: 94.00%	PPV: 92.96%
		NPV: 98.57%	NPV: 95.51%

ACC: Accuracy; AUC: Area under the curve; N/A: Not applicable; NPV: Negative predictive value; PPV: Positive predictive value; SEN: Sensitivity; SPE: Specificity.