Opportunities and challenges of artificial intelligence-assisted endoscopy and high-quality data for esophageal squamous cell carcinoma

Table 1 Summary of clinical studies on the diagnosis of esophageal squamous cell carcinoma using artificial intelligence

Ref.	Year	Primary outcomes	Secondary outcomes	Patients studied	Type of data	Study design
Zhao et al[65]	2019	Feasibility of CAD-based IPCL classification	Observer agreement and pixel-level/Lesion-level accuracy	219 patients (30 with inflammation, 24 with low-grade intraepithelial neoplasia, 165 with early esophageal cancer); 185 lesions selected for analysis	NBI-ME images with histological findings	Retrospective study
Fukuda et al[18]	2020	NBI-based CAD system for ESCC diagnosis	Observer agreement, AI speed, and detection time	Training: 1544 pathological SCC lesions and 458 non-cancer/normal tissues; 354 video clips for testing	NBI/BLI endoscopic images and videos with pathology assessment	Evaluation study using retrospectively collected data
Shimamoto et al[66]	2020	AI system for invasion depth estimation in superficial ESCC	AI vs expert endoscopists on same validation videos	909 patients for training dataset; 102 videos of superficial ESCC cases used for validation	WLI/NBI/BLI endoscopic media with invasion depth pathology	Retrospective training; independent video-based validation
Shiroma et al[67]	2021	AI detection capability of T1 ESCC in EGD videos	Real-time AI-assisted detection: Performance and comparison with endoscopists	8428 images (training set); 144 EGD videos (validation set); 40 patients (validation set)	Retrospective EGD data: WLI/NBI videos and images	Retrospective study
Ikenoyama et al[68]	2021	LVL prediction from non-stained images and cancer risk assessment	Comparison of AI performance with experienced endoscopists	595 patients (6634 training images); 72 patients (667 independent validation images)	WLI/NBI endoscopic images and clinical data (ESCC, HNSCC, Lugol staining)	Retrospective study
Li et al[69]	2021	Early ESCC CAD system and comparison with WLI-based models	Reduced missed diagnoses and unnecessary biopsies	Training: 235 cases (abnormal NM-NBI images), 412 cases (normal images); Validation: 284 cases (202 abnormal, 82 normal)	NM-NBI/WLI images, patient/Lesion data, and histology	4-phase observational retrospective study with endoscopist assessment
Waki et al[70]	2021	AI performance in ESCC detection under simulated oversight	AI vs endoscopists: Sensitivity gain, specificity loss with AI support	Training: 1376 superficial ESCC cases (17336 images); 196 non-cancerous cases (2916 images); Testing: 52 superficial ESCC cases (1459 images); 47 non-cancerous lesions (1168 images)	NBI/BLI/WLI images and videos of superficial ESCC, benign lesions, and normal esophagus	DL-based AI system: Retrospective development with partial prospective validation
Meng et al[71]	2022	CAD performance metrics (AUROC, accuracy, sensitivity, specificity)	CAD vs endoscopists: Diagnostic performance and predictive values	837 patients (training); 323 patients (test)	Non-magnified WLI/NBI images and histology-confirmed SESCC/HGIN data	Retrospective diagnostic accuracy
Tajiri et al[72]	2022	AI-based ESCC vs non-ESCC classification under simulated conditions	AI vs endoscopists: Subgroup accuracy by pathology and lesion size	Training: 1433 superficial ESCC cases (25048 images); 410 non-cancerous esophageal lesions (8557 images); Testing: 123 superficial ESCC cases (3370 images); 107 non-cancerous lesions (2075 images)	ME/non-ME endoscopic images and videos (WLI, NBI, BLI)	DL-AI system development in simulated setting (retrospective/prospective mixed design)
Feng et al[16]	2023	AI diagnostic accuracy for superficial ESCC detection	AI diagnostic support and cancer feature recognition	1283 patients (training); 319 (internal validation); 905 (external validation); total 2507 patients, 9663 images	WLI images (Olympus/Fujifilm) and SESCC-confirmed clinical data	Retrospective diagnostic study (partly prospectively registered)
Yuan et al[22]	2023	Real-time detection and delineation of early ESCC using a novel AI system	Accurate classification of image modalities	Not available	WLI/NBI videos and Lugol-stained ME images with histology-confirmed clinical data	Pilot study (video demonstration)
Wang et al[73]	2023	IPCL-based early ESCC localization and classification	Performance metrics and model comparison	246 patients (2887 ME-BLI images); 81 patients (493 ME-NBI images)	Magnified ME-BLI/NBI images and histology-confirmed ESCC data	Pilot retrospective image collection study
Wang et al[74]	2024	YOLO-HSI integration for early ESCC detection	HSI vs RGB models: Classification of normal, dysplasia, and ESCC	16 patients (7 with esophageal SCC, 9 with squamous dysplasia, 10 healthy controls); training dataset: 1836 images	WLI/NBI images converted to HSI with pathology evaluation	Not clearly described; development and evaluation study
Nakao et al[75]	2024	AI support for non-experts in ESCC detection	ESCC detection rate, observation time, and adverse events	320 patients included in analysis (AI group: 152; control group: 168)	WLI/NBI/Lugol endoscopic media with histopathology and clinical data	Prospective, single-center, exploratory randomized controlled trial
Aoyama et al[20]	2025	AI model for superficial ESCC detection from endoscopic videos	Subgroup analysis and comparison with endoscopist performance	Training data: 280 cases (140 with lesions, 140 without); Test data: 115 cases (57 with lesions, 58 without)	NBI endoscopic videos with histopathological diagnosis	Prospective data, retrospective analysis design
Ma et al[76]	2025	Diagnostic performance for ESN classification	iCLE vs experts and non-experts: Diagnostic performance and support evaluation	2803 patients (for iCLE training/validation); 226 patients (image test); additional patients for video recognition testing (prospective, total N not shown)	pCLE video/image data with histopathological gold standard	Prospective diagnostic study

AI: Artificial intelligence; AUROC: Area under the receiver operating characteristic; BLI: Blue light imaging; CAD: Computer-aided diagnosis; DL: Deep learning; EGD: Esophagogastroduodenoscopy; ESCC: Esophageal squamous cell carcinoma; ESN: Esophageal squamous neoplasm; HGIN: High grade intraepithelial neoplasia; HNSCC: Head and neck squamous cell carcinoma; HSI: Hyperspectral imaging; iCLE: Integrated confocal laser endomicroscopy; IPCL: Intra-papillary capillary loop; ME: Magnifying endoscopy; NBI: Narrow-band imaging; NM: Non-magnifying; pCLE: Probe-based confocal laser endomicroscopy; RGB: Red, green, blue; SCC: Squamous cell carcinoma; SESCC: Superficial esophageal squamous cell carcinoma; WLI: White light endoscopy; YOLO: You only look once.