Effect of Novel Laser Shoe Photobiomodulation on Neuropathic Pain and Plantar Pressure Profile in Type 2 Diabetes Mellitus with Peripheral Neuropathy.

BACKGROUND: Photobiomodulation (PBM) or Low-level laser is used to treat diabetic foot complications. The existing method of laser application to the foot did not cover the foot's entire area to improve the foot's microcirculation. Therefore, we have developed a cost-effective Special LASER Shoe device, focusing exclusively on the entire foot region to manage neuropathic pain and other symptoms in individuals with type 2 diabetes mellitus. OBJECTIVE: The main objective of the present study was to evaluate the effect of this newly developed special laser shoe PBM on neuropathic pain and plantar pressure profile in individuals with type 2 diabetes mellitus with neuropathy. METHODS: We included 60 participants with diabetic peripheral neuropathy of both genders and age more than 20 years. Participants were treated with PBM by a specially designed novel Laser Shoe. Outcomes were clinical variables like Vibration Perception Threshold (VPT), Visual Analogue Scale (VAS), Michigan neuropathy screening instrument A&B, Ankle-Brachial Index (ABI), and Static dynamic gait parameters. RESULTS: Participants were with an average age of 62, and the average duration of diabetes was 11 years. Analysis showed a significant difference in VPT, VAS, Michigan neuropathic screening inventory, and ankle-brachial index. (P < 0.05). CONCLUSION: We conclude that Novel laser shoe photobiomodulation using 'Laser Shoe' effectively reduces peripheral neuropathic pain. It is also effective in reducing average and maximum plantar pressure. Reduction in neuropathic pain and improvement in plantar pressure distribution can reduce further complications.

Identification of Contractions from Electrohysterography for Prediction of Prolonged Labor.

The analysis of the uterine electrical activity and its propagation patterns could potentially predict the risk of prolonged/arrested progress of labor. In our study, the Electrohysterography (EHG) signals of 83 participants in labor at around 3-4 cm of cervical dilatation, were recorded for about 30 minutes each. These signals were analyzed for predicting prolonged labor. Out of the 83 participants, 70 participants had normal progress of labor and delivered vaginally. The remaining 13 participants had prolonged/ arrested progress of labor and had to deliver through a cesarean section. In this paper, we propose an algorithm to identify contractions from the acquired EHG signals based on the energy of the signals. The role of contraction consistency and fundal dominance was evaluated for impact on progress of the labor. As per our study, the correlation of contractions was higher in case of normal progress of labor. We also observed that the upper uterine segment was dominant in cases with prolonged/arrested progress of labor.

Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images.

Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.

Image Processing Approach for Detection of Leukocytes in Peripheral Blood Smears.

Peripheral blood smear analysis is a gold-standard method used in laboratories to diagnose many hematological disorders. Leukocyte analysis helps in monitoring and identifying health status of a person. Segmentation is an important step in the process of automation of analysis which would reduce the burden on hematologists and make the process simpler. The segmentation of leukocytes is a challenging task due to variations in appearance of cells across the slide. In the proposed study, an automated method to detect nuclei and to extract leukocytes from peripheral blood smear images with color and illumination variations is presented. Arithmetic and morphological operations are used for nuclei detection and active contours method is for leukocyte detection. The results demonstrate that the proposed method detects nuclei and leukocytes with Dice score of 0.97 and 0.96 respectively. The overall sensitivity of the method is around 96%.

Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study.

White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the result depends on skill and experience of the hematologist. In this paper we present a comparative study of feature extraction using two approaches for classification of white blood cells. In the first approach, features were extracted using traditional image processing method and in the second approach we employed AlexNet which is a pre-trained convolutional neural network as feature generator. We used neural network for classification of WBCs. The results demonstrate that, classification result is slightly better for the features extracted using the convolutional neural network approach compared to traditional image processing approach. The average accuracy and sensitivity of 99% was obtained for classification of white blood cells. Hence, any one of these methods can be used for classification of WBCs depending availability of data and required resources.

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images.

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.