Artificial intelligence in endoscopic ultrasound: Clinical translation of a prediction, navigation, and diagnosis framework

Table 1 Representative artificial intelligence models for the entire endoscopic ultrasound workflow

Application field	Ref.	Algorithm type	Sample size	Validation method	Application scenarios	Accuracy (%)	Sensitivity/specificity (%)	PPV (%)	NPV (%)	F1 score	AUC (95%CI)	Public availability/ reproducibility
Pre-procedural	Sirtl et al[11], 2023	Machine Learning	218 patients	Multicenter retrospective	Biliary sludge risk screening	84	97.9/92	89.5	98.2	0.93	0.96 (0.92-0.98)	Code: Not available; data: Not shared
	Chen et al[14], 2024	Predictive model	> 1000 patients	Multicenter prospective	Endoscopic resection outcome prediction	NA	NA	NA	NA	NA	NA	Model: Not released; data: Institutional
Intra-procedural	Wu et al[18], 2023	5 DL models ensemble	290 patients	RCT	Anatomical landmark tracking	NA	NA	NA	NA	NA	NA	Code: Not available; system: Proprietary (EUS-IREAD)
	Li et al[19], 2025	Automated reporting system	114 patients	Prospective trial	Standard site documentation	90.3	NA	NA	NA	NA	NA	Code: Closed source; data: Not public
	Rizzatti et al[20], 2025	DCNN	550 cases	Technical validation	Real-time anatomical navigation	NA	NA	NA	NA	NA	NA	Not publicly available
Image diagnosis	Hirai et al[23], 2022	EfficientNetV2-L	631 cases	Multicenter retrospective	5-class SEL differentiation	89.3	98.8/67.6	85.4	94.2	0.91	0.94 (0.90-0.97)	Code: Not shared; data: Multi-institutional (restricted)
	Joo et al[24], 2024	Random forest	NA	Single-center	GIST intervention prediction	89.6	93.8/81.8	88.9	89.5	0.91	0.896 (0.86-0.93)	Code: Not available; data: Not disclosed
	Liu et al[32], 2022	RetinaNet + VGGNet	NA	Single-center	Depth/origin classification	82.5	80.2/90.6	87.1	85	0.83	0.88 (0.84-0.92)	Not publicly released
	Zhang et al[33], 2025	YOLOv8s-seg + MobileNetv2	NA	Single-center	Layer identification	76.50	NA	NA	NA	NA	NA	Code: Not available; model: Proprietary
	Cui et al[39], 2024	Joint-AI	12 physicians	RCT	Solid lesion diagnosis	90.0	91.0/88.0	89.5	89.8	0.9	0.93 (0.89-0.96)	Code: Not shared; data: Anonymized, available on request
	Orzan et al[42], 2024	CNN + DeepLabv3+	112 cases (1248 images)	Single-center	dCCA detection and segmentation	97.8	100.0/94.4	96.8	100	0.98	0.99 (0.97-1.00)	Not publicly available
	Men et al[46], 2025	ResNet50	554 cases (8738 images)	Dual-center	Identification of 3 colon tumors	80.9	72.9/84.4	78.5	79.8	0.75	0.85 (0.81-0.89)	Code: Not released; data: Multi-center (restricted)
Cytology diagnosis	Fujii et al[52], 2024	Transformer	45 cases (4059 images)	Augmentation test	Malignant cell detection	88.2	91.4/85.0	86.7	90.1	0.89	0.954 (0.93-0.98)	Code: GitHub; data: Partially open
	Fang et al[53], 2025	SSCNN	NA	Semi-supervised framework	Cytology analysis with limited labels	95.1	93.8/97.3	96.5	94.8	0.95	0.97 (0.94-0.99)	Code: Not available; model: Internal use only

This table summarizes key artificial intelligence applications within a “Prediction-Navigation-Diagnosis” framework for endoscopic ultrasound, covering preoperative planning, intraoperative navigation, and postoperative image/cytology diagnosis. Performance metrics (e.g., positive predictive value, negative predictive value, F1 score, area under the curve) are included where reported to facilitate comparative evaluation of clinical validity. The “public availability/reproducibility” column indicates access to code, data, or models, highlighting current transparency challenges. Most cited studies are from 2022-2025, reflecting the rapidly evolving nature of this field. DL: Deep learning; RCT: Randomized controlled trial; DCNN: Deep convolutional neural networks; SEL: Subepithelial lesions; GIST: Gastrointestinal stromal tumors; CNN: Convolutional neural networks; dCCA: Distal cholangiocarcinoma; SSCNN: Semi-supervised convolutional neural network; PPV: Positive predictive value; NPV: Negative predictive value; F1: F1 score (harmonic mean of precision and recall); AUC: Area under the curve; CI: Confidence interval; EUS-IREAD: Endoscopic ultrasound-intelligent and real-time endoscopy analytical device; NA: Not available.

Table 2 Comparison of regulatory approaches to artificial intelligence-assisted endoscopic ultrasound in foreign countries

Feature	United States (FDA)	European Union (MDR/IVDR + AI Act)
Regulatory core	Function-based (what the software does) tiered regulation	Device risk + AI system risk dual matrix regulation.
Classification logic	CADt → CADe → CADx → CADa, with increasing risk	Class I, IIa, IIb, III (device risk), plus the AI Act’s “high-risk” category for most medical AI
Primary pathways	510(k) (substantial equivalence), De Novo (novel low-moderate risk), PMA (high risk)	Self-certification (class I low risk), notified body conformity assessment (class IIa and above)
Adaptability	Predetermined change control plans allow for iterative updates to cleared algorithms within defined bounds without new submission	Update processes under regulations are stricter; significant software changes typically require re-assessment/notification to the notified body
Clinical evidence	Emphasizes prospective, multi-center clinical trials to demonstrate safety and effectiveness	Emphasizes clinical evaluation with comprehensive technical documentation and performance validation, aligned with GDPR
Transparency	Requires disclosure of algorithm performance	The AI act emphasizes transparency, requiring high-risk AI systems to provide clear usage information and ensure outputs are interpretable and overseen by humans
Process flowcharts	AI medical device concept → determine intended use and function: CADt, CADe, CADx, or CADa → assign FDA risk class → generate clinical and technical evidence → submit to FDA for review → FDA approval clearance → post-market surveillance	Medical AI product → determine device risk class per MDR/IVDR → determine AI system risk level per AI act (typically “high-risk”) → comply with both sets of requirements → undergo conformity Assessment by a notified body (for class IIa and above) → obtain CE marking

This table summarizes the fundamental differences between the United States Food and Drug Administration and European Union [Medical Device Regulation/In Vitro Diagnostic Regulation + artificial intelligence (AI) act] regulatory approaches for AI in endoscopic ultrasound. Understanding these distinctions in regulatory philosophy, classification logic, and approval pathways is crucial for the global clinical translation and integration of AI-enhanced endoscopic ultrasound devices. FDA: Food and Drug Administration; MDR: Medical Device Regulation; IVDR: In Vitro Diagnostic Regulation; AI: Artificial intelligence; 510(k): Premarket notification; PMA: Premarket approval; De Novo: Pathway for novel, low-to-moderate risk devices; GDPR: General data protection regulation; Notified Body: Independent organization designated by an European Union country to assess conformity of medical devices; CADt: Computer-aided detection for triage; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CADa: CompUTER-aIDED aUTONOmous diagnosis; CE: European conformity.

Table 3 Phased clinical integration roadmap

Phase	Primary goals	Key evidence required	Regulatory milestones
Phase 1 (1-2 years)	Low-risk auxiliary tasks (e.g., automated measurement, structured reporting)	Feasibility studies, human-machine interaction safety data	Registration or simplified clearance (e.g., class II medical device)
Phase 2 (3-5 years)	Moderate-risk diagnostic support (e.g., lesion detection and classification)	Multicenter diagnostic accuracy studies, clinical utility assessment	Moderate-risk device approval (requires performance monitoring plan)
Phase 3 (> 5 years)	High-risk autonomous functions (e.g., puncture path planning, real-time complication alert)	Large-scale RCTs: Real-world effectiveness data	High-risk device approval (requires a full post-market surveillance system)

This table outlines a risk-escalating, phased pathway for integrating artificial intelligence into endoscopic ultrasound practice. Each phase defines primary objectives, key study designs for validation, and corresponding regulatory milestones, supporting a systematic and evidence-based approach to clinical implementation. RCT: Randomized controlled trial.