Table 3.

Performance data on artificial intelligence algorithms used for computer-aided diagnosis for early esophageal cancer

References	Deep learning model?	Model	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	Accuracy (%)
Zhang et al. [28]*	No	Supervised learning via CNN. 218,000 patches generated from 218 endoscopic images	73.41	83.54	72.09	84.44	79.38
Mendel et al. [29]	Yes	Deep CNN with a residual net (ResNet) architecture	94	88	–	–	–
Ebigbo et al. [30]	Yes	Deep CNN with a residual net (ResNet) architecture	97 (WLE) 94 (NBI)	88 (WLE) 80 (NBI)	–	–	–
Ebigbo et al. [30]	Yes	Deep CNN with a residual net (ResNet) architecture	92	100	–	–	–
Everson et al. [31]	Yes	Deep CNN	89.3	98	–	–	93.7
Guo et al. [32]*	NA	NA	97.8	76.2	–	–	–
Horie et al. [33]*	–	Deep CNN	–	–	54	–	98
Cai et al. [34]	Yes	CNN	97.8	85.4	86.4	97.6	91.4
de Groof et al. [26]	No	Supervised learning technique	95	85	87.2	–	91.7
Ghatwary et al. [35]	Yes	Regional-based convolutional neural network (R-CNN), Fast R-CNN, Faster R-CNN and Single Shot MultiBox Detector (SSD)	96	92	–	–	–
Horie et al. [36]	Yes	Deep CNN (Single Shot MultiBox Detector)	98	79	39	95	98
Liu et al. [37]	Yes	CNN	77.58	–	78.06	–	–
Shiroma et al. [38]*	Yes	CNN	70	–	42.1	–	80
Struyvenberg et al. [39]*	Yes	CNN	88	78	–	–	84
Tang et al. [40]*	Yes	CNN	97	94	–	–	97
de Groof et al. [25]	Yes	ResNet U-net	93	83	–	–	88
Ebigbo et al. [41]	Yes	Encoder–decoder artificial neural network	83.7	100	–	–	89.9
Guo et al. [42]	Yes	Detection neural network	98.04	95.03	–	–	–
Ohmori et al. [43]	Yes	CNN	100 (NME-NBI) 90 (WLE)	63(NME-NBI) 76(WLE)	63(NME-NBI) 70(WLE)	100(NME-NBI 93(WLE)	77(NME-NBI) 81 (WLE)
de Groof et al. [24]	Yes	Custom-made, fully residual, hybrid ResNet/U-Net model	91	89	–	–	90
Hashimoto et al.[44]	Yes	CNN: inception-ResNet-v2 algorithm	96.4	94.2	89.2	–	95.4
Iwagami et al. [45]	Yes	CNN: single shot multiBox detector	94	42	58	90	66

^*Conference abstracts. WLE; White light endoscopy, NBI; narrow-band imaging, NME; non-magnified endoscopy