In-body ranging for ultra-wide band wireless capsule endoscopy using a neural network architecture

This paper focuses to design a compact (110mm³) Ultra-Wide Band (UWB) (3.1GHz to 10.6GHz) antenna, which covers almost the whole 10dB impedance matching bandwidth of the UWB range. Two of the main specialties of this article over other related articles are its antenna’s wider bandwidth (approx. 7.3GHz) and antenna’s simulation environment. No other papers consider such a realistic model to simulate their antenna, before. Due to its wider bandwidth, this antenna can be employed in the Wireless Capsule Endoscopy (WCE) system, which mainly requires a high-speed real-time data transfer-capable antenna. The antenna was examined inside simplified human Gastrointestinal (GI) tract phantoms (Colon, Esophagus, Small Intestine and Stomach) as well as the human Voxel GI tract model by maintaining proper tissue properties for the sake of accurate parametric results. Biocompatible material polyimide was used to construct the capsule wall to fulfill the system’s biocompatibility. In the result analysis part, the proposed antenna’s SAR (Specific Absorption Rate) or electromagnetic energy amount, consumed by near-side body tissue was considered and found in the acceptable region, according to Federal Communication Commission (FCC)’s regulation. Also, other crucial antenna parameters such as VSWR, reflection coefficient, radiation characteristics, efficiencies, directivity and surface current density were adoptable compare to other related articles. The Finite Integration Technique (FIT) of CST Microwave Studio Suite 2020 was used to investigate the antenna parameters.

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IN-BODY RANGING FOR ULTRA-WIDE BAND WIRELESS CAPSULE ENDOSCOPY USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

Selcuk University Journal of Engineering Science and Technology ◽

10.15317/scitech.2018.127 ◽

2018 ◽

Vol 6 (2) ◽

pp. 207-217

Author(s):

Muzaffer KANAAN ◽

Rüştü AKAY ◽

Memduh SUVEREN

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Capsule Endoscopy ◽

Particle Swarm ◽

Wide Band ◽

Wireless Capsule Endoscopy ◽

Ultra Wide Band ◽

Swarm Optimization ◽

Wireless Capsule

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Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis

Surgical Endoscopy ◽

10.1007/s00464-021-08689-3 ◽

2021 ◽

Author(s):

Kaiwen Qin ◽

Jianmin Li ◽

Yuxin Fang ◽

Yuyuan Xu ◽

Jiahao Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Sensitivity And Specificity ◽

Capsule Endoscopy ◽

Meta Analysis ◽

Wireless Capsule Endoscopy ◽

Convolution Neural Network ◽

Research Progress ◽

Gi Bleeding ◽

Wireless Capsule

Abstract Background Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE. Methods A search in PubMed, SinoMed, and Web of Science was conducted to collect all original publications about CNN implementation in WCE. Assessment of the risk of bias was performed by Quality Assessment of Diagnostic Accuracy Studies-2 risk list. Pooled sensitivity and specificity were calculated by an exact binominal rendition of the bivariate mixed-effects regression model. I2 was used for the evaluation of heterogeneity. Results 16 articles with 23 independent studies were included. CNN application to WCE was divided into detection on erosion/ulcer, gastrointestinal bleeding (GI bleeding), and polyps/cancer. The pooled sensitivity of CNN for erosion/ulcer is 0.96 [95% CI 0.91, 0.98], for GI bleeding is 0.97 (95% CI 0.93–0.99), and for polyps/cancer is 0.97 (95% CI 0.82–0.99). The corresponding specificity of CNN for erosion/ulcer is 0.97 (95% CI 0.93–0.99), for GI bleeding is 1.00 (95% CI 0.99–1.00), and for polyps/cancer is 0.98 (95% CI 0.92–0.99). Conclusion Based on our meta-analysis, CNN-dependent diagnosis of erosion/ulcer, GI bleeding, and polyps/cancer approached a high-level performance because of its high sensitivity and specificity. Therefore, future perspective, CNN has the potential to become an important assistant for the diagnosis of WCE.

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