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.