27,719 research outputs found

    Effectiveness of Integrative Korean Medicine Treatment in Patients with Traffic-Accident-Induced Acute Low Back Pain and Mild Adult Scoliosis

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    We investigated the effectiveness of integrative Korean medicine treatment in patients with pre-existing scoliosis who received inpatient care for traffic-accident-induced acute LBP. We selected 674 patients diagnosed with scoliosis between 1 January 2015, and 30 June 2021, using lumbar spine (L-spine) imaging, across four Korean medicine hospitals in Korea for a retrospective chart review and sent them a questionnaire-based follow-up survey. The primary outcome was a numeric rating scale (NRS) score of LBP. The secondary outcomes were the Oswestry Disability Index (ODI), 5-level EuroQol 5-dimension (EQ-5D-5L), and patient global impression of change (PGIC) scores. In total, 101 patients responded to the follow-up survey. NRS scores decreased from 4.86 (4.71–5.02) to 3.53 (3.17–3.90) from admission to discharge, subsequently decreasing to 3.01 (2.64–3.38) (p p < 0.001), respectively. Approximately 87.1% of patients were satisfied with their inpatient care. There were no significant differences in the degree of improvement according to the severity of scoliosis. Integrative Korean medicine treatment can improve pain, lumbar dysfunction, and quality of life in patients with traffic-accident-induced acute low back pain and pre-existing mild scoliosis

    Klasifikacija i grupiranje pacijenata s križoboljom analizom površinskih mioelektričnih signala

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    Low back pain (LBP) is a global health problem phenomenon. Most patients are categorized as non-specific, thus requiring an individualized approach which still poses a major challenge. Myoelectric recordings from two pairs of lumbar muscle sites were collected during an isometric trunk extension exercise. Ninety-one subjects were included in the study; 29 patients with non-specific chronic LBP (CLBP), 25 patients with radiculopathy (RLBP), and 37 control healthy subjects (HS). Six best-performing time-domain raw features were employed to model contextual secondary feature groups. Neuromuscular LBP characteristics were described with coordination, co-activation, trends, and frequency-based fatigue measures. The initial large secondary feature set was significantly reduced by employing neighborhood component analysis (NCA), alongside preserving features in the original interpretable domain. Support vector machines (SVM) and k-nearest neighbor (kNN) consistently resulted in high classification accuracy, among the 23 classifiers exploited. Based on thirteen NCA components, from CLBP vs. HS model, CLBP subgrouping was performed by exploiting Hierarchical clustering analysis (HCA), resulting in seven CLBP subgroups. The formal procedure for assigning new CLBP patients, to one of the subgroups, was enabled by introducing a new distance metric CLBP Pattern Distance (CPD). As a final step, for each CLBP subgroup, a corresponding medical interpretation was proposed, thus enabling a direct connection between mathematical procedure and the deeper inference from the medical perspective to account for the complex multifactorial interaction among different neuromotor control, biomechanical, or even psychological aspects.Križobolja (LBP) je prepoznata kao globalni zdravstveni problem. Većina pacijenata se kategorizira kao nespecifična, stoga je nužan individualizirani pristup koji još uvijek predstavlja veliki izazov. Elektromiografske snimke, s dva para pozicija lumbalnih mišića, prikupljene su tijekom izometrijske vježbe ekstenzije trupa. U istraživanje je bio uključen devedeset i jedan ispitanik; 29 bolesnika s nespecifičnim kroničnim LBP-om (CLBP), 25 bolesnika s radikulopatijom (RLBP) te 37 kontrolnih zdravih ispitanika (HS). Analizom dobivene najučinkovitije jednostavne značajke iz vremenske domene, njih šest, upotrijebljene su za modeliranje grupa kontekstualnih sekundarnih značajki. Neuromuskularne karakteristike LBP-a su opisane kroz grupe značajki koordinacije, koaktivacije, trendova i mjera umora temeljenih na frekvencijskoj analizi. Inicijalno veliki skup sekundarnih značajki je značajno reduciran tehnikom analize komponenti metodom susjedstva (NCA), uz očuvanje značajki u izvornoj interpretabilnoj domeni. Metoda potpornih vektora (SVM) i klasifikatori temeljeni na k-najbližem susjedstvu (kNN) konzistentno su rezultirali visokim klasifikacijskim točnostima, iz skupa 23 upotrijebljena klasifikatora. Koristeći trinaest NCA komponenti, dobivenih iz modela diferencijacije CLBP u odnosu na HS, provedeno je podgrupiranje CLBP pacijenata koristeći metodu grupiranja hijerarhijskom analizom, što je rezultiralo s ukupno sedam CLBP podskupina. Formalni postupak za dodjelu novih pacijenata s CLBP-om u jednu od sedam podskupina, osiguran je uvođenjem nove metrike udaljenosti, CLBP Pattern Distance (CPD). Kao posljednji korak, za svaku podskupinu CLBP-a predloženo je odgovarajuće medicinsko tumačenje, čime je omogućena izravna veza između matematičkog postupka i dubljeg zaključivanja iz medicinske perspektive, a kako bi se objasnila složena multifaktorijalna interakcija između različitih aspekata neuromotoričkih kontrole, biomehaničkih ili čak psiholoških aspekata

    Klasifikacija i grupiranje pacijenata s križoboljom analizom površinskih mioelektričnih signala

    No full text
    Low back pain (LBP) is a global health problem phenomenon. Most patients are categorized as non-specific, thus requiring an individualized approach which still poses a major challenge. Myoelectric recordings from two pairs of lumbar muscle sites were collected during an isometric trunk extension exercise. Ninety-one subjects were included in the study; 29 patients with non-specific chronic LBP (CLBP), 25 patients with radiculopathy (RLBP), and 37 control healthy subjects (HS). Six best-performing time-domain raw features were employed to model contextual secondary feature groups. Neuromuscular LBP characteristics were described with coordination, co-activation, trends, and frequency-based fatigue measures. The initial large secondary feature set was significantly reduced by employing neighborhood component analysis (NCA), alongside preserving features in the original interpretable domain. Support vector machines (SVM) and k-nearest neighbor (kNN) consistently resulted in high classification accuracy, among the 23 classifiers exploited. Based on thirteen NCA components, from CLBP vs. HS model, CLBP subgrouping was performed by exploiting Hierarchical clustering analysis (HCA), resulting in seven CLBP subgroups. The formal procedure for assigning new CLBP patients, to one of the subgroups, was enabled by introducing a new distance metric CLBP Pattern Distance (CPD). As a final step, for each CLBP subgroup, a corresponding medical interpretation was proposed, thus enabling a direct connection between mathematical procedure and the deeper inference from the medical perspective to account for the complex multifactorial interaction among different neuromotor control, biomechanical, or even psychological aspects.Križobolja (LBP) je prepoznata kao globalni zdravstveni problem. Većina pacijenata se kategorizira kao nespecifična, stoga je nužan individualizirani pristup koji još uvijek predstavlja veliki izazov. Elektromiografske snimke, s dva para pozicija lumbalnih mišića, prikupljene su tijekom izometrijske vježbe ekstenzije trupa. U istraživanje je bio uključen devedeset i jedan ispitanik; 29 bolesnika s nespecifičnim kroničnim LBP-om (CLBP), 25 bolesnika s radikulopatijom (RLBP) te 37 kontrolnih zdravih ispitanika (HS). Analizom dobivene najučinkovitije jednostavne značajke iz vremenske domene, njih šest, upotrijebljene su za modeliranje grupa kontekstualnih sekundarnih značajki. Neuromuskularne karakteristike LBP-a su opisane kroz grupe značajki koordinacije, koaktivacije, trendova i mjera umora temeljenih na frekvencijskoj analizi. Inicijalno veliki skup sekundarnih značajki je značajno reduciran tehnikom analize komponenti metodom susjedstva (NCA), uz očuvanje značajki u izvornoj interpretabilnoj domeni. Metoda potpornih vektora (SVM) i klasifikatori temeljeni na k-najbližem susjedstvu (kNN) konzistentno su rezultirali visokim klasifikacijskim točnostima, iz skupa 23 upotrijebljena klasifikatora. Koristeći trinaest NCA komponenti, dobivenih iz modela diferencijacije CLBP u odnosu na HS, provedeno je podgrupiranje CLBP pacijenata koristeći metodu grupiranja hijerarhijskom analizom, što je rezultiralo s ukupno sedam CLBP podskupina. Formalni postupak za dodjelu novih pacijenata s CLBP-om u jednu od sedam podskupina, osiguran je uvođenjem nove metrike udaljenosti, CLBP Pattern Distance (CPD). Kao posljednji korak, za svaku podskupinu CLBP-a predloženo je odgovarajuće medicinsko tumačenje, čime je omogućena izravna veza između matematičkog postupka i dubljeg zaključivanja iz medicinske perspektive, a kako bi se objasnila složena multifaktorijalna interakcija između različitih aspekata neuromotoričkih kontrole, biomehaničkih ili čak psiholoških aspekata

    An advanced deep learning models-based plant disease detection: A review of recent research

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    Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

    Lumbar spine segmentation in MR images: a dataset and a public benchmark

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    This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain. It was collected from four different hospitals and was divided into a training (179 patients) and validation (39 patients) set. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. We set up a continuous segmentation challenge to allow for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation, and improve the diagnostic value of lumbar spine MRI

    Smart Farm-Care using a Deep Learning Model on Mobile Phones

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    Deep learning and its models have provided exciting solutions in various image processing applications like image segmentation, classification, labeling, etc., which paved the way to apply these models in agriculture to identify diseases in agricultural plants. The most visible symptoms of the disease initially appear on the leaves. To identify diseases found in leaf images, an accurate classification system with less size and complexity is developed using smartphones. A labeled dataset consisting of 3171 apple leaf images belonging to 4 different classes of diseases, including the healthy ones, is used for classification. In this work, four variants of MobileNet models - pre-trained on the ImageNet database, are retrained to diagnose diseases. The model’s variants differ based on their depth and resolution multiplier. The results show that the proposed model with 0.5 depth and 224 resolution performs well - achieving an accuracy of 99.6%. Later, the K-means algorithm is used to extract additional features, which helps improve the accuracy to 99.7% and also measures the number of pixels forming diseased spots, which helps in severity prediction. Doi: 10.28991/ESJ-2023-07-02-013 Full Text: PD

    Deep learning based intelligent system for robust face spoofing detection using texture feature measurement

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    The use of biometric structures in our everyday lives is becoming increasingly frequent. Biometrics play a crucial role in various applications, including crime analysis, person identification, and verification. Among different biometrics, the face provides a rich set of features. However, face spoofing is a continuous threat in real-world environments, leading to abnormal behavior in security systems. Conventional face spoofing analysis methods have often failed to achieve optimal performance in detecting face spoofing, faking, and attacks due to limitations in low-level feature extraction. To address this issue, this research introduces a novel technique called Face Spoofing Detection (FSD) based on Deep Learning Convolutional Neural Network (DLCNN), named NLBP-Net. In this technique, features from face images are extracted using Local Binary Pattern (LBP). The periocular area, which remains untouched by the gradient process, is a section of the human face that stands out as being highly unique. Furthermore, the extracted features are trained using the advanced Visual Geometry Group 16 (VGG16) methods. The trained model effectively classifies spoofing and faking attempts in random face images. Simulations conducted on a standard dataset demonstrate that the proposed NLBP-Net outperforms other methods across several metrics. NLBP-Net achieves an accuracy of 99.593%, sensitivity of 99.633%, specificity of 99.137%, recall of 99.224%, precision of 99.057%, and F1-score of 99.057%

    Circular shift combination local binary pattern (CSC-LBP) method for dorsal finger crease classification

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    Biometric technology has drawn increasing attention and significance importance in recent years. In biometric security systems, personal identification and verification rely on their physical, behavioral, and biological characteristics. In this study, a new hand-based modality called dorsal finger creases is proposed for biometric classification. This modality is located on the dorsal surface of the finger, between the proximal knuckle and distal knuckle of the finger. However, it requires a specific feature extraction approach to extract the modality information on the selected region. Therefore, we have proposed a method for extracting the underlying features of the dorsal finger creases, called circular shift combination local binary pattern (CSC-LBP). The concept of CSC-LBP is to compute the local binary pattern within a 3 × 3 spatial window for each neighborhood pixel separately. Further, the concept of combination approach is applied on the individually computed eight LBP values to obtain the more discriminative feature vector. A multiclass support vector machine classifier is used for evaluating the effectiveness of the proposed CSC-LBP operator. Extensive experiments on self-collected datasets demonstrate the high classification accuracy and effectiveness of the proposed CSC-LBP method and confirm the usefulness of dorsal finger creases for personal recognition

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Research on texture image feature extraction method

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    In this paper, we give several classical feature extraction methods, including grayscale co-generation matrix, Gabor and wavelet transform features, and local binary pattern series features. We introduce the basic principles of these feature extraction algorithms and some derivative methods respectively. Finally, we analyze the advantages and disadvantages of the existing feature extraction methods: grayscale covariance matrix can analyze the arrangement rules of image texture and extract local spatial features of the image, filtering methods and local feature extraction methods are widely used, but the extracted features do not provide a good description of the image structure; and the multi-feature fusion operation brings huge computational effort. Therefore, the future developable directions are proposed based on the existing problems and difficulties in processing texture images
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