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

Table 2 Summary of technical or algorithmic studies on esophageal squamous cell carcinoma using artificial intelligence

Ref.	Year	Primary outcomes	Secondary outcomes	Patients studied	Type of data	Study design
Everson et al[24]	2019	Abnormal IPCL classification metrics and prediction time	CNN feature visualization, eCAM analysis, and future AI insights	17 individuals (10 with ESCN, 7 with normal esophageal squamous epithelium)	HD ME-NBI videos/images (.png) with matched histopathology	Proof-of-concept study
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
Horie et al[77]	2019	CNN-based AI for esophageal cancer detection	Detection of small/superficial lesions and predictive values	Training: 8428 images from 384 patients; Test: 47 patients with 49 cancer lesions	WLI/NBI endoscopic images with histopathology	Retrospective study
Ohmori et al[17]	2020	Image analysis system for ESCC detection and classification	Comparison of diagnostic performance between AI system and expert endoscopists	135 patients for validation; training data included 804 histologically confirmed superficial esophageal SCC lesions	WLI/NBI images (with/without magnification) and histological data	Retrospective training; prospective external validation
Guo et al[78]	2020	Real-time AI for early ESCC and precancerous lesion detection	Frame-based/Lesion-based sensitivity/specificity and normal video evaluation	Training data: 191 cases of precancerous/early ESCC; 358 cases of non-cancerous lesions; validation data: 100 endoscopic videos from 41 patients with ESCC and 30 normal controls	NBI images/videos with histological confirmation	Development study with retrospective training and delayed validation dataset
Tang et al[79]	2021	Diagnostic performance of the DCNN model	Evaluation of DCNN support and agreement with endoscopists	1078 patients (for training and cross-validation); 243 patients (for independent internal and external validation)	WLI endoscopic images and retrospective clinical data	Multicenter diagnostic retrospective study
Uema et al[80]	2021	CNN system for microvessel classification in superficial ESCC	CNN vs endoscopists in microvessel classification, agreement, and diagnostic support	336 patients with 393 SESCC lesions; 1777 training images; 617 validation images	Trimmed ME-NBI images of SESCC and clinical data	Retrospective single-center study
Liu et al[44]	2022	Development of AI model to detect and delineate early ESCC under WLI endoscopy	Detection and delineation performance	1239 patients (13083 images for training/testing); 262 patients (1479 internal test images); 96 patients (648 external test images)	WLI images with confirmed early ESCC clinical data	Retrospective study
Yuan et al[21]	2022	AI-based IPCL subtype prediction for early ESCC	AI validation, diagnostic support, and comparison with endoscopists	685 patients (training/validation); 176 patients (ER validation dataset)	ME-NBI images and confirmed precancerous/superficial ESCC data	Retrospective multicenter study
Zhao et al[81]	2022	Effectiveness of AI-DEN system for early EC diagnosis	Diagnostic accuracy and speed comparison	300 patients suspected of having esophageal cancer; Training group: 200 patients (148 with early cancer, 52 with benign disease); Test group: 100 patients (92 with early cancer, 8 with benign disease)	NBI-DEN images with pathology results	Retrospective study
Tani et al[31]	2023	Diagnostic accuracy of AI system vs endoscopists	Diagnostic accuracy and safety outcomes	388 patients (registered); 380 patients (underwent endoscopy); 237 lesions (evaluated)	Endoscopic images (WLI, NBI/BLI) and real-time clinical data	Single-center prospective single-arm non-inferiority trial
Zhang et al[45]	2023	Interpretable AI-IDPS for ESCC invasion depth prediction	AI-IDPS vs DL models and endoscopists; trust in AI predictions	581 patients for training; validation using 196 images and 33 continuously collected videos	ME-NBI images/videos with pathology reports and endoscopist feedback	Multicenter retrospective study with crossover validation by endoscopists
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; BLI: Blue light imaging; CAD: Computer-aided diagnosis; CNN: Convolutional neural network; DCNN: Deep convolutional neural network; DEN: Digestive endoscopy; DL: Deep learning; EC: Esophageal cancer; eCAM: Explicit class activation maps; ER: Endoscopic resection; ESCC: Esophageal squamous cell carcinoma; ESCN: Early squamous cell neoplasia; ESN: Esophageal squamous neoplasm; HD: High-definition; HSI: Hyperspectral imaging; iCLE: Integrated confocal laser endomicroscopy; IDPS: Invasion depth prediction system; IPCL: Intra-papillary capillary loop; ME: Magnifying endoscopy; NBI: Narrow-band imaging; 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.