Artificial intelligence enhances the management of esophageal squamous cell carcinoma in the precision oncology era

Table 1 Artificial intelligence in endoscopic examination of esophageal squamous cell carcinoma

Ref.	Data sets	Endoscopic modality	Main study aim	AI algorithms	Best result
Yuan et al[37], 2022	Image data set: 53933; images from 2621 patients; video data set: 142; videos from 19 patients	WLI, NM-NBI, ME -NBI	Detection of superficial ESCC	DCNN	Image data set: Sensitivity: 92.5%-99.7%; specificity: 78.5%-89.0%; AUC: 0.906-0.989; video data set: Sensitivity: 89.5%-100%; specificity: 73.7%-89.5%
Zhao et al[38], 2019	Image data set: 1383; images from 219 patients	ME-NBI	Classification of IPCLs	FCN	Mean accuracy of senior IPCL: 92.0%; mid-level IPCL: 82.0%; junior IPCL: 73.3%
Li et al[39], 2021	Image data set: 5367 images	NM-NBI	Detection of early ESCC	FCN	AUC: 0.9761; sensitivity: 91.0%; specificity: 96.7%; accuracy: 94.3%
Fukuda et al[41], 2020	Image data set: 28333 images	ME-NBI	Detection and characterization of early ESCC	CNN	Detection: Sensitivity: 91.0%; specificity: 51.0%; accuracy: 63.0%; characterization: Sensitivity: 86.0% specificity: 89.0%; accuracy: 88.0%
Liu et al[42], 2022	Image data set: 13083; WLI images from 1239 patients	WLI	Detection and delineation of ESCC margins	DCNN	Detection: Accuracy: 85.7% (internal validation) and 84.5% (external validation); delineation: Accuracy: 93.4% (internal validation) and 95.7% (external validation)
Ikenoyama et al[44], 2021	Image data set: 7301; images from 667 patients	LCE	Predict multiple Lugol-voiding lesions	GoogLeNet deep neural network	Sensitivity: 84.4%; specificity: 70.0%; accuracy: 76.4%
Yuan et al[47], 2023	Image data set: 10047; images from 1112 patients; video data set: 140; videos from 1183 lesions	ME-NBI	Detection and delineation of ESCC margins	DCNN	Detection: Accuracy: 92.4% (internal validation) and 89.9% (external validation); delineation: Accuracy: 88.9% (internal validation) and 87.0% (external validation)
Shimamoto et al[48], 2020	Image data set: 23977; images from 909 patients; video data set: 102 videos	NM-NBI	Assessment of tumor infiltration depth	CNN	Sensitivity: 50.0%; specificity: 99.0%; accuracy: 87.0%
Wang et al[49], 2023	ME-BLI data set: 2887; images from 246 patients; ME-NBI data set: 493; images from 81 patients	ME-BLI, ME-NBI	Identification of IPCL	R-CNN	Recall: 79.25%; precision: 75.54%; F1-score: 0.764; mean average precision: 74.95%
Zhang et al[53], 2023	Image data set: 5119; images from 581 patients; video data set: 33 videos	ME-NBI	Infiltration depth prediction	AI-IDPS	For differentiating SM2-3; lesions: Image data set: Sensitivity: 85.7%; specificity: 86.3%; accuracy: 86.2%; video data set: Sensitivity: 87.5%; specificity: 84.0%; accuracy: 84.9%
Yuan et al[54], 2022	Image data set: 7094; images from 685 patients	ME-NBI	Classification of IPCLs	DCNN	Accuracy (internal validation): 91.3%; accuracy (external validation): 89.8%

CNN: Convolutional neural networks; DCNN: Deep convolutional neural networks; FCN: Fully convolutional network; R-CNN: Region convolutional neural network; IPCLs: Intrapapillary capillary loops; WLI: White light image; LCE: Lugol chromoendoscopy; NM-NBI: Non-magnified endoscopy with narrow-band imaging; ME-NBI: Magnifying endoscopy with narrow-band imaging; ME-BLI: Blue laser imaging; AI: Artificial intelligence.