In this article, we present a novel stationary electromagnetic actuation system (EMS) and a redundant magnetic field control approach that can enhance the magnetic actuation ability for a capsule endoscope. The EMS configured with multiple electromagnets has an intrinsic redundant problem when computing input current to each coil from inverse formulation. Here, we propose a redundant electromagnetic field control scheme of utilizing this redundancy to increase magnetic force and reduce the peak input current that deteriorates the performance. It is based on the optimization of magnetic field encountering input current and null-space equations of a pseudoinverse matrix. We validated the proposed methods in simulation and experiments through the prototyped EMS designed by considering actual human size and a magnetic capsule endoscope. The results show significant improvements in the magnetic field and lower input current compared to the conventional method as well as extended operating time through reduced heating effects. Especially, the in vitro in a gastric phantom and in vivo experiments in a live pig could prove the practical applicability of the developed method for a magnetic capsule endoscope toward upper stomach diagnosis in the clinical environment.

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.

Omega-3 fatty acid (n-3 FA) intake is known to have a preventive effect on depressive symptoms in a general population. This study assessed the effects of n-3 FA intake on depressive symptoms and brain function in middle-aged women. Depressive symptoms were screened using the Beck Depression Inventory-II (BDI-II) and Center for Epidemiologic Studies-Depression scale (CES-D) assessment questionnaires, and n-3 FA intakes were assessed using semiquantitative food frequency questionnaire. We found that n-3 FA intakes were negatively associated with depressive symptoms in middle-aged women. Psychiatrists diagnosed the presence of depressive disorders using the 5th edition of the Mental Disorder Diagnosis and Statistics Manual (DSM-5). Resting-state functional magnetic resonance imaging (rs-fMRI) was performed to investigate the association between n-3 FA intake and brain functional connectivity. Functional connectivity of the right middle frontal cortex (default mode network) and the right middle temporal pole (frontoparietal network) was positively associated with depressive symptom scores and negatively associated with n-3 FA intakes. In conclusion, high n-3 FA intake decreases the risk of depressive symptoms and modifies the brain functional connectivity in middle-aged women.

Park KH, et al. J Clin Neurol. 2017 Oct;13(4):387-393. https://doi.org/10.3988/jcn.2017.13.4.387