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Applied Sciences
Volume 16
Issue 2
10.3390/app16021134
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Systematic Review

Neural Network Architectures in Video Capsule Endoscopy: A Systematic Review and Meta-Analysis on Accuracy and Reading Time Performances

by
Daniele Salvi
1,†,
Chiara Zani
2,†,
Cristiano Spada
3,4,*,
Stefania Piccirelli
1,
Lorenzo Zileri Dal Verme
5,
Giulia Tripodi
4,
Loredana Gualtieri
4,
Paola Cesaro
1,‡ and
Clarissa Ferrari
2,‡
1
Department of Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy
2
Research and Clinical Trials Unit, Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy
3
Center for Endoscopic Research Therapeutics and Training (CERTT), Università Cattolica del Sacro Cuore, 00168 Rome, Italy
4
Digestive Endoscopy Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
5
Department of Emergency, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, Largo Gemelli 8, 00168 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
Joint last authors.
Appl. Sci. 2026, 16(2), 1134; https://doi.org/10.3390/app16021134 (registering DOI)
Submission received: 23 December 2025 / Revised: 15 January 2026 / Accepted: 19 January 2026 / Published: 22 January 2026
(This article belongs to the Section Computing and Artificial Intelligence)
Versions Notes

Abstract

Artificial intelligence (AI) has revolutionized medical image analysis. Several neural network (NN) architectures were developed and applied across the last decade, becoming essential for automated diagnosis and clinical applications. AI based on NNs has become increasingly integrated into gastroenterology, offering new opportunities for automated lesion detection and workflow optimization. Small-bowel capsule endoscopy (SBCE) has benefited substantially from these advances, addressing long-standing challenges such as time-consuming video review and variability among readers. This systematic review and meta-analysis evaluated neural network-based models for lesion detection in SBCE, assessing pooled diagnostic accuracy and the impact of AI on reading time. A total of 44 primary studies were included: 36 validation studies for accuracy and 9 clinical studies for reading time. All NN architectures demonstrated high diagnostic performance, with a pooled accuracy of 95.3% (95% CI: 94.1–96.5%). More recent architectures, including transformer-based and capsule networks, outperformed classical convolutional neural networks (CNNs). AI assistance significantly reduced SBCE reading time, with a pooled mean reduction of 84% compared to standard review. These findings highlight the strong potential of AI to enhance SBCE efficiency and diagnostic reliability.
Keywords: artificial intelligence; CNN; deep learning; capsule endoscopy; small-bowel lesions artificial intelligence; CNN; deep learning; capsule endoscopy; small-bowel lesions

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MDPI and ACS Style

Salvi, D.; Zani, C.; Spada, C.; Piccirelli, S.; Zileri Dal Verme, L.; Tripodi, G.; Gualtieri, L.; Cesaro, P.; Ferrari, C. Neural Network Architectures in Video Capsule Endoscopy: A Systematic Review and Meta-Analysis on Accuracy and Reading Time Performances. Appl. Sci. 2026, 16, 1134. https://doi.org/10.3390/app16021134

AMA Style

Salvi D, Zani C, Spada C, Piccirelli S, Zileri Dal Verme L, Tripodi G, Gualtieri L, Cesaro P, Ferrari C. Neural Network Architectures in Video Capsule Endoscopy: A Systematic Review and Meta-Analysis on Accuracy and Reading Time Performances. Applied Sciences. 2026; 16(2):1134. https://doi.org/10.3390/app16021134

Chicago/Turabian Style

Salvi, Daniele, Chiara Zani, Cristiano Spada, Stefania Piccirelli, Lorenzo Zileri Dal Verme, Giulia Tripodi, Loredana Gualtieri, Paola Cesaro, and Clarissa Ferrari. 2026. "Neural Network Architectures in Video Capsule Endoscopy: A Systematic Review and Meta-Analysis on Accuracy and Reading Time Performances" Applied Sciences 16, no. 2: 1134. https://doi.org/10.3390/app16021134

APA Style

Salvi, D., Zani, C., Spada, C., Piccirelli, S., Zileri Dal Verme, L., Tripodi, G., Gualtieri, L., Cesaro, P., & Ferrari, C. (2026). Neural Network Architectures in Video Capsule Endoscopy: A Systematic Review and Meta-Analysis on Accuracy and Reading Time Performances. Applied Sciences, 16(2), 1134. https://doi.org/10.3390/app16021134

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