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Technol Health Care. 2019 Mar 7. doi: 10.3233/THC-181484. [Epub ahead of print]

A novel fast solving method for targeted drug-delivery capsules in the gastrointestinal tract.

Author information

1
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
2
Jiangsu Apon Medical Technology Co., LTD, Nantong, Jiangsu 226400, China.
3
Department of Information Technology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China.

Abstract

BACKGROUND:

As an innovative technique without cable connection, targeted drug-delivery capsules improve diagnostic and therapeutic capabilities in the gastrointestinal (GI) tract.

OBJECTIVE:

To fast track targeted drug-delivery capsules in the GI tract, a tracking method based on the multiple alternating magnetic sources with adaptive adjustment of the excitation intensity has been investigated.

METHODS:

The functional prototype of the tracking system has been developed. The tracking model between the magnetic field strength and the capsule's location has been established, which shows a nonlinear equation group with multiple local extremum. Particularly, an improved back-propagation (BP) neural network by particle swarm optimization (PSO) is investigated to solve the tracking problem in real time. The PSO is introduced at an early stage to optimize the weights and thresholds of the BP neural network to improve the generalizability and global search ability. Consequently, the Levenberg-Marquardt (LM) algorithm is used as the learning rule to obtain a higher accuracy and convergence rate.

RESULTS:

The performance on the PSO-BP neural network is experimentally analyzed by comparing it with the standard BP network and the LM-BP network.

CONCLUSIONS:

The tracking experiments show that the PSO-BP neural network can solve the tracking problem successfully. The PSO-BP network can get the solution faster than iterative search algorithms.

KEYWORDS:

Drug-delivery capsules; fast tracking; gastrointestinal tract; neural network; particle swarm optimization

PMID:
30909256
DOI:
10.3233/THC-181484

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