Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives

Table 1 Previous studies on upper endoscopy of gastric cancer using artificial intelligence

Ref.	Targets	Sample sizes	Inputs	Tasks	Analysis method	Diagnostic performance
Yoon et al[28]	GC (ESD/surgery)	800 cases	GC/non-GC images in close-up and distant views	Detection and invasion depth prediction	CNN	AUC: detection, 0.981; depth, 0.851
Zhu et al[29]	GC	993 images	GC images	Diagnosis of invasion depth	CNN	Sensitivity: 76.4%, PPV: 89.6%
Li et al[30]	GC and healthy	386 GC and 1702 NC images	NBI images	Diagnosis of GC	CNN	Sensitivity: 91.1%, PPV: 90.6%
Hirasawa et al[31]	GC	13584 training and 2296 test images	GC images	Diagnosis of GC	CNN	Sensitivity: 92.2%, PPV: 30.6%
Ishioka et al[32]	EGC	62 cases	Real-time images	Detection	CNN	Detection rate: 94.1%
Luo et al[33]	GC	1036496 images	GC images	Detection	CNN	PPV: 0.814, NPV:0.978
Horiuchi et al[34]	GC and gastritis	1492 GC and 1078 gastritis images	NBI images	Detection	CNN	Sensitivity: 95.4%, PPV: 82.3%

GC: Gastric cancer; CNN: Convolutional neural network; AUC: Area under the curve; PPV: Positive predictive value; NC: Non-cancer; NBI: Narrow-band image; EGC: Early gastric cancer.