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.

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.

Table 3 Summary of reviews and meta-analysis on esophageal squamous cell carcinoma using artificial intelligence

Ref.	Year	Primary outcomes	Secondary outcomes	Patients studied	Type of data	Study design
Zhang et al[63]	2020	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Syed et al[82]	2020	Summary of literature using DL to identify esophageal tumors	DL/CNN overview, current limitations, and future directions	Not applicable (not focused on individual study data; 21 relevant studies reviewed)	Endoscopic images with histology-confirmed clinical data	Mentored review
Huang et al[83]	2020	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Lazăr et al[84]	2020	ML model comparison for endoscopic esophageal lesion assessment	AI in care quality, workflow efficiency, and physician support	Not applicable (review of multiple studies)	Review of AI in endoscopic assessment of esophageal disease	Review
Namikawa et al[85]	2020	Review of studies using CNNs in gastrointestinal endoscopy	AI in GI polyp/cancer detection and capsule endoscopy	Not applicable	Not applicable	Review article
Zhang et al[1]	2021	Diagnostic accuracy of AI-assisted models	AI vs endoscopists: Pooled accuracy and subgroup performance	Not applicable	Endoscopic media and histology-confirmed clinical data	Meta-analysis
Liu et al[44]	2021	Not applicable	Not applicable	Not applicable	Not applicable	Mini-review
Ma et al[13]	2022	CNN-AI for early EC detection from endoscopy	Heterogeneity and bias analysis (I2, meta-regression, Deeks’ plot)	Meta-analysis of 7 studies; number of patients and images varied across studies	Meta-analysis of WLE/NBI-based studies with histological confirmation	Meta-analysis
Nagao et al[64]	2022	AI in upper GI: Current status, clinical integration, and future outlook	AI in H. pylori infection diagnosis, gastric anatomy, and upper GI cancer detection	Not applicable (review of multiple studies)	CNN-based AI for diagnosis of GI and pharyngeal cancers	Review
Tokat et al[86]	2022	Overview of the applicability of AI technology in upper gastrointestinal endoscopy	Clinical AI challenges and applications in GI oncology	Not applicable	WLE/NBI/ME-NBI images with clinical and histological data	Review article
Guidozzi et al[14]	2023	AI in endoscopic diagnosis of esophageal cancer	AI vs endoscopists, tumor types, and bias risk (QUADAS-2)	1590 patients for ESCC diagnosis (14 studies); 478 patients for EAC diagnosis (9 studies)	English studies using endoscopic images/videos	Systematic review and meta-analysis
Pan et al[87]	2023	AI in early ESCC endoscopic diagnosis: Current status	Discussion of limitations and future prospects of AI in ESCC diagnosis	Not applicable (review of multiple studies)	Review of DL models for ESCC diagnosis	Review
Tao et al[32]	2024	AI vs experts: Accuracy in early EC and depth diagnosis	AI vs endoscopists: Performance, invasion depth diagnosis, and bias assessment	Not applicable	Meta-analysis of 19 studies with heterogeneous sample sizes	Meta-analysis
Kikuchi et al[88]	2024	Recent studies on endoscopic AI for GI tumors	Discussion on the future outlook of AI in gastroenterology	Not applicable	Published studies on DL-based endoscopic AI	Review
Yan et al[33]	2025	Not applicable	Not applicable	Not applicable	Not applicable	Review article
Shahzil et al[89]	2025	Lesion color contrast, visibility, and GI detection rate	Lesion visibility and analysis of serrated lesions and adenomas	16634 individuals (data integrated from 17 studies)	Clinical and endoscopic image data	Systematic review and meta-analysis

AI: Artificial intelligence; CNN: Convolutional neural network; DL: Deep learning; EAC: Esophageal adenocarcinoma; EC: Esophageal cancer; ESCC: Esophageal squamous cell carcinoma; GI: Gastrointestinal; H. pylori: Helicobacter pylori; ME: Magnifying endoscopy; ML: Machine learning; NBI: Narrow-band imaging; WLE: White light endoscopy.