Application of artificial intelligence-assisted confocal laser endomicroscopy in gastrointestinal imaging analysis

Table 1 Summary of research progress on artificial intelligence-assisted confocal laser endomicroscopy in gastrointestinal medical imaging

Ref.	Purpose	Model	Input	Datasets	Experimental results
Machicado et al[45]	Develop two CNN-based algorithms for EUS-nCLE image analysis to assist in the accurate diagnosis and risk stratification of IPMNs	SBM, HBM	EUS-nCLE video frames with region of interest segmentation and feature extraction	EUS-nCLE video images consisting of 15027 frames were used from 35 histologically confirmed IPMN patients, including 18 cases of HGD-Ca	Sensitivity: SBM and HBM: 83.3%; accuracy: SBM: 82.9%, HBM: 85.7%; specificity: SBM: 82.4%, HBM: 88.2%
Lee et al[46]	Develop a deep learning-based computer-aided diagnostic system to support EUS-nCLE in classifying pancreatic cystic lesions	VGG19	224 × 224 nCLE video frame images after data augmentation	68 nCLE video clips collected from King Chulalongkorn Memorial Hospital	Classification accuracy of nonmucinous PCLs: pseudocysts (98%), SCN (93.11%), NET (84.31%); classification accuracy of mucinous PCLs: IPMN (84.43%), MCN (86.1%)
André et al[47]	Design pCLE diagnostic software for the automated classification of colonic polyps to support lesion identification during examinations	KNN	Feature vector data	135 colorectal lesion samples from 71 patients, including 93 neoplastic lesions and 42 non-neoplastic lesions	Accuracy: 89.6%; sensitivity: 92.5%; specificity: 83.3%
Gessert et al[48]	CNNs and multiple transfer learning strategies were utilized to perform real-time classification of colorectal cancer in both peritoneal and colonic tissues, aiming to provide auxiliary decision support during surgery	VGG-16, Inception-V3, Densenet121, SE-Resnext50	384 × 384 images	The dataset consisted of 1577 CLE images categorized into four classes: Healthy colon, malignant colon, healthy peritoneum, and malignant peritoneum	Peritoneal metastases: 97.1 AUC; primary colorectal lesions: 73.1 AUC
Pulido et al[49]	Enhancing the classification accuracy of pCLE videos for Barrett's esophagus and related lesions using deep learning models	AttentionPooling, Multi-Module AttentionPooling	224 × 224 images after removing irrelevant parts	pCLE videos from 78 patients, totaling 1057 clips, annotated into 3 categories	The AttentionPooling model performed the best, with an F1 score of 0.89; the Multi-Module AttentionPooling model exhibited the highest sensitivity for precancerous lesions (0.71)
Tong et al[50]	Design the CAESNet model to leverage a large number of unlabeled eCLE images for improving the accuracy of dysplasia grading diagnosis in Barrett's esophagus	CAESNet	256 × 256 images after data augmentation	An eCLE image dataset comprising 429 expert-annotated images (across 9 categories) and 2826 unannotated images	Best performance at 32 layers: Accuracy: 0.824 ± 0.0329; precision: 0.832 ± 0.0302; F1 score: 0.816 ± 0.0342; Cohen's kappa coefficient: 0.781 ± 0.04
Su et al[51]	Apply deep learning techniques to the field of CLE for semantic segmentation of goblet cells in gastric mucosal intestinal metaplasia	GCSCLE	512 × 512 × 3 image data	334 clinical CLE gastric images from 62 subjects, covering different regions of the stomach	IoU: 0.8795; dice coefficient: 0.8664; precision: 0.8554; recall: 0.8834; accuracy: 0.9925
Cho et al[52]	Develop an AI-based real-time assessment system for the automatic detection of gastric cancer in CLE images	EfficientNetV2	Data-augmented grey CLE images	Training set: 5984 tumor images and 5984 normal images; Testing set: 1496 tumor images and 2586 normal images	Accuracy: 0.990; specificity: 0.982; sensitivity: 1.000

CNN: Convolutional Neural Network; EUS-nCLE: Endoscopic ultrasonography needle-based confocal laser endomicroscopy; IPMNs: Intraductal papillary mucinous neoplasms; HGD-Ca: High-grade dysplasia/adenocarcinoma; nCLE: Needle-based confocal laser endomicroscopy; SCN: Serous cystic neoplasm; NET: Cystic neuroendocrine tumor; PCLs: Pancreatic cystic lesions; MCN: Mucinous cystic neoplasm; pCLE: Probe-based confocal laser endomicroscopy; CLE: Confocal laser endomicroscopy; AUC: Area under the curve; eCLE: Endoscope-based confocal laser endomicroscopy; IoU: Intersection over Union.