Multimodal artificial intelligence in capsule endoscopy: Integrating video and sensor data for advanced gastrointestinal diagnostics

Table 1 Clinical applications of artificial intelligence in capsule endoscopy¹

Lesion type	Ref.	Model	Capsule type	Number of training images	Performance metrics
GI hemorrhage	Jia et al[38]	Deep CNN for bleeding detection	Small bowel	10000 images (bleeding and non-bleeding)	Improved precision, recall
	Spada et al[35]	A multicenter study utilizing AI-assisted reading of lesions	Small bowel	158235 images	Reading time reduced: 33.7 min → 3.8 minutes (P < 0.0001); improved accuracy
Erosions and ulcers	Ribeiro et al[40]	CNN model for colonic ulcers	Colon capsule	37319 (3570 with lesions)	Sensitivity: 96.9%, specificity: 99.9%
Vascular lesions	Mascarenhas et al[42]	Multicenter study utilizing CNN	Small bowel and colon capsule	34665 (11091 with lesions)	Diagnostic accuracy: 95%, sensitivity: 86.4%, specificity: 98.3%
Polyps and tumors	Kjølhede et al[44]	Systematic review and meta-analysis for the detection of polyps < 6 mm to ≥ 10 mm	CCE-2	Combined across studies	Sensitivity: 85%-87%, specificity: 85%-95%
Polyps and tumors	Moen et al[45]	Systematic review of AI models for polyp or colorectal neoplasia detection	CCE-2	Varied across studies (thousands to 30000)	Per-frame sensitivity: 47.4%-98.1%, specificity: 87.0%-96.3%; per-lesion sensitivity: 81.3%–98.1%

¹Summary of key studies evaluating the diagnostic performance of artificial intelligence (AI)-assisted capsule endoscopy across various gastrointestinal lesion types. Convolutional neural network-based models have demonstrated improved precision, sensitivity, and specificity in detecting gastrointestinal bleeding, erosions, ulcers, vascular lesions, and colorectal polyps compared with conventional reading. AI integration has also significantly reduced reading time and enhanced diagnostic consistency among readers of different experience levels.

CCE: Colon capsule endoscopy; CNN: Convolutional neural networks; AI: Artificial intelligence.