A study to evaluate the effect of ultrasound treatment on nodules in multiple sclerosis patients

Sánchez, Anna Gil; Andrade, Eugenia Lacasa; Marsal, Joan Valls; Tauste, Lourdes Assens; Mingot, C. González; Monge, Jorge Lecina; Puértolas, Margarita Casalilla; Calero, Nuria Sáez; Argemí, Albert Sacristán; Badia, Blanca Guiu; Moncusí, Silvia Peralta; Gonzalo, Hugo; Nogueras, Lara; Pujol, Marta Lordan; Collado, Mónica Nieves i; Brieva, Luís

doi:10.1080/00207454.2016.1186025

Cited by 2 publications

(1 citation statement)

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“…Early symptoms are composed of tingling, weakness, blurred version, numbness, etc. [2]. The traditional MRI scanning based identification may meet with the "normal-appearing white matter" problem.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression

Wu¹,

Lopez²

2017

Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

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Abstract. (Aim) Currently, scholars tend to use computer vision approaches to implement multiple sclerosis (MS) identification. (Method) In this study, we proposed a novel MS slice identification system, based on Haar wavelet transform, principal component analysis, and logistic regression. (Result) Simulation results showed the accuracies of our method using 2-level, 3-level, and 4-level decomposition are 83.25±1.62%, 89.72±1.18%, and 87.65±1.79%, respectively. (Conclusion) Our method with 3-level decomposition achieved the best. BackgroundMultiple sclerosis (MS) is a progressive disease that affects both brain and spinal cord [1]. Early symptoms are composed of tingling, weakness, blurred version, numbness, etc. [2]. The traditional MRI scanning based identification may meet with the "normal-appearing white matter" problem. Hence, it is necessary to develop new methods to identify MS plaques.Traditionally, scholars have proposed many computer vision methods [3][4][5] in identify abnormal brain diseases [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], including MS slice identification. For example, Zhou (2016) [21].used stationary wavelet entropy, combined with several machine learning methods. Zhan and Chen (2016) [22] used biorthogonal wavelet transform and logistic regression. Karaca (2017) [23] used convex combination of infinite kernels.Nevertheless, those methods are too complicated, and their models are difficult to train. In this study, we presented a novel and simple system, which was based on Haar wavelet transform, principle component, and logistic regression. Materials and MethodsThirty-four multiple sclerosis patients and thirty-three healthy subjects were enrolled from China local hospitals from 2013. Experienced radiologists were instructed to select the slice with MS plaques from the 34 patients, and select corresponding slices from 33 healthy subjects. In total, we selected 141 slices from MS patients and 148 slices from healthy controls.Our method contains three-stages. In the first stage, Haar wavelet [24] was chosen to transform the brain slices from spatial domain to wavelet domain [25][26][27][28][29][30][31]. The mother wavelet Haar wavelet λ(b) is defined with the form ofHere b denotes the spatial axis: either horizontal or vertical. The scaling function θ(b) is defined as:International Conference on Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering Research, volume 114We chose to use a two-level Haar wavelet transform. In the second stage, principle component was used to reduce the wavelet coefficients to only cover the threshold (THR) of total variances. We here set THR to 95%.In the third stage, logistic regression (LR) [32] was employed to be the classifier. The logistic regression is simpler and easier to train than multilayer perceptron [33][34][35][36], probabilis...

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Section: Introductionmentioning

confidence: 99%

Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression

Wu¹,

Lopez²

2017

Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

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Comparing the efficacy of continuous and pulsed ultrasound therapies in patients with lateral epicondylitis: A double-blind, randomized, placebo-controlled study

Ünver

Bakılan

Taşçıoğlu

et al. 2021

Turk J Phys Med Rehab

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Objectives The aim of this study was to investigate the efficacy of continuous and pulsed ultrasound therapies in lateral epicondylitis. Patients and methods A total of 51 patients (18 males, 33 females; mean age: 46.52±6.16 years; range, 27 to 64 years) who were diagnosed with lateral epicondylitis between February 2013 and October 2014 were included. The patients were randomized to either continuous ultrasound (n=17), pulsed ultrasound (n=17), or placebo (n=17) groups. First group received 10 sessions of continuous ultrasound therapy. The second group received 10 sessions of pulsed ultrasound therapy in a ratio of 1:4. The third group received 10 sessions of placebo treatment. The pain levels of the patients were evaluated using Visual Analog Scale (VAS). The muscle strength was evaluated using a dynamometer. For functional evaluation, Duruöz’s Hand Index (DHI) and Patient-Rated Tennis Elbow Evaluation (PRTEE) scales were used. Assessments were made at baseline, at the end of therapy, and one month after therapy. The thickness of the common extensor tendon was also measured using ultrasonic imaging at baseline and at the end of therapy. Results At the end of the study, there was a statistically significant improvement in the rest and activation VAS scores, and DHI and PRTEE scores in both continuous and pulsed ultrasound therapy groups, compared to placebo (p<0.05). However, no superiority was found between the continuous and pulsed ultrasound therapy groups (p>0.05). A statistically significant reduction in the common extensor tendon thickness was found only in the pulsed ultrasound therapy group (p<0.05). Conclusion Our study results show that both continuous and pulsed ultrasound applications are effective in the treatment of lateral epicondylitis.

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A study to evaluate the effect of ultrasound treatment on nodules in multiple sclerosis patients

Abstract: Both treatments are useful to improve skin reaction after first-line treatments, but ultrasound in combination with gel achieves a faster reduction in pain and redness, suggesting that ultrasound treatment might be a good analgesic for nodule management in multiple sclerosis patients.

Cited by 2 publications

References 21 publications

Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression

Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression

Comparing the efficacy of continuous and pulsed ultrasound therapies in patients with lateral epicondylitis: A double-blind, randomized, placebo-controlled study

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