scholarly journals The BACH classification of long bone osteomyelitis

2019 ◽  
Vol 8 (10) ◽  
pp. 459-468 ◽  
Author(s):  
Andrew J. Hotchen ◽  
Maria Dudareva ◽  
Jamie Y. Ferguson ◽  
Parham Sendi ◽  
Martin A. McNally
Keyword(s):  
Classification Accuracy ◽  
Soft Tissues ◽  
Multidrug Resistant ◽  
Long Bone ◽  
Test Results ◽  
Clinical Imaging ◽  
The United Kingdom ◽  
Host Status ◽  
Long Bone Osteomyelitis

Objectives The aim of this study was to assess the clinical application of, and optimize the variables used in, the BACH classification of long-bone osteomyelitis. Methods A total of 30 clinicians from a variety of specialities classified 20 anonymized cases of long-bone osteomyelitis using BACH. Cases were derived from patients who presented to specialist centres in the United Kingdom between October 2016 and April 2017. Accuracy and Fleiss’ kappa (Fκ) were calculated for each variable. Bone involvement (B-variable) was assessed further by nine clinicians who classified ten additional cases of long bone osteomyelitis using a 3D clinical imaging package. Thresholds for defining multidrug-resistant (MDR) isolates were optimized using results from a further analysis of 253 long bone osteomyelitis cases. Results The B-variable had a classification accuracy of 77.0%, which improved to 95.7% when using a 3D clinical imaging package (p < 0.01). The A-variable demonstrated difficulty in the accuracy of classification for increasingly resistant isolates (A1 (non-resistant), 94.4%; A2 (MDR), 46.7%; A3 (extensively or pan-drug-resistant), 10.0%). Further analysis demonstrated that isolates with four or more resistant test results or less than 80% sensitive susceptibility test results had a 98.1% (95% confidence interval (CI) 96.6 to 99.6) and 98.8% (95% CI 98.1 to 100.0) correlation with MDR status, respectively. The coverage of the soft tissues (C-variable) and the host status (H-variable) both had a substantial agreement between users and a classification accuracy of 92.5% and 91.2%, respectively. Conclusions The BACH classification system can be applied accurately by users with a variety of clinical backgrounds. Accuracy of B-classification was improved using 3D imaging. The use of the A-variable has been optimized based on susceptibility testing results. Cite this article: A. J. Hotchen, M. Dudareva, J. Y. Ferguson, P. Sendi, M. A. McNally. The BACH classification of long bone osteomyelitis. Bone Joint Res 2019;8:459–468. DOI: 10.1302/2046-3758.810.BJR-2019-0050.R1

Can we predict outcome after treatment of long bone osteomyelitis?

2020 ◽  
Vol 102-B (11) ◽  
pp. 1587-1596
Author(s):  
Andrew J. Hotchen ◽  
Maria Dudareva ◽  
Ruth A. Corrigan ◽  
Jamie Y. Ferguson ◽  
Martin A. McNally
Keyword(s):  
Soft Tissues ◽  
Surgical Debridement ◽  
Bone Involvement ◽  
Long Bone ◽  
Time Points ◽  
Patient Reported ◽  
Host Status ◽  
One Year ◽  
Long Bone Osteomyelitis ◽  
Eq Vas

Aims This study presents patient-reported quality of life (QoL) over the first year following surgical debridement of long bone osteomyelitis. It assesses the bone involvement, antimicrobial options, coverage of soft tissues, and host status (BACH) classification as a prognostic tool and its ability to stratify cases into ‘uncomplicated’ or ‘complex’. Methods Patients with long-bone osteomyelitis were identified prospectively between June 2010 and October 2015. All patients underwent surgical debridement in a single-staged procedure at a specialist bone infection unit. Self-reported QoL was assessed prospectively using the three-level EuroQol five-dimension questionnaire (EQ-5D-3L) index score and visual analogue scale (EQ-VAS) at five postoperative time-points (baseline, 14 days, 42 days, 120 days, and 365 days). BACH classification was applied retrospectively by two clinicians blinded to outcome. Results In total, 71 patients with long-bone osteomyelitis were included. There was significant improvement from time of surgery to one year postoperatively in mean EQ-VAS (58.2 to 78.9; p < 0.001) and mean EQ-5D-3L index scores (0.284 to 0.740; p < 0.001). At one year following surgery, BACH ‘uncomplicated’ osteomyelitis was associated with better QoL compared to BACH ‘complex’ osteomyelitis (mean EQ-5D-3L 0.900 vs 0.685; p = 0.020; mean EQ-VAS 87.1 vs 73.6; p = 0.043). Patients with uncomplicated bone involvement (BACH type B1, cavitary) reported higher QoL at all time-points when compared to complex bone involvement (B2, segmental or B3, osteomyelitis involving a joint). Patients with good antimicrobial options (Ax or A1) gave higher outcome scores compared to patients with multidrug-resistant isolates (A2). The need for microvascular tissue transfer (C1 and C2) did not impact significantly on QoL. Patients without major comorbidities (uncomplicated, H1) reported higher QoL compared to those with significant disease (complex, H2). Conclusion Uncomplicated osteomyelitis, as defined by BACH, gave higher self-reported QoL when compared to complex cases. The bone involvement, antimicrobial options, and host status variables were able to stratify patients in terms of QoL. These data can be used to offer prognostic information to patients who are undergoing treatment for long bone osteomyelitis. Cite this article: Bone Joint J 2020;102-B(11):1587–1596.

Can we predict outcome after treatment of long bone osteomyelitis?

2020 ◽  
pp. 1-10
Author(s):  
Andrew J. Hotchen ◽  
Maria Dudareva ◽  
Ruth A. Corrigan ◽  
Jamie Y. Ferguson ◽  
Martin A. McNally
Keyword(s):  
Soft Tissues ◽  
Surgical Debridement ◽  
Bone Involvement ◽  
Long Bone ◽  
Time Points ◽  
Patient Reported ◽  
Host Status ◽  
One Year ◽  
Long Bone Osteomyelitis ◽  
Eq Vas

Aims This study presents patient-reported quality of life (QoL) over the first year following surgical debridement of long bone osteomyelitis. It assesses the bone involvement, antimicrobial options, coverage of soft tissues, and host status (BACH) classification as a prognostic tool and its ability to stratify cases into ‘uncomplicated’ or ‘complex’. Methods Patients with long-bone osteomyelitis were identified prospectively between June 2010 and October 2015. All patients underwent surgical debridement in a single-staged procedure at a specialist bone infection unit. Self-reported QoL was assessed prospectively using the three-level EuroQol five-dimension questionnaire (EQ-5D-3L) index score and visual analogue scale (EQ-VAS) at five postoperative time-points (baseline, 14 days, 42 days, 120 days, and 365 days). BACH classification was applied retrospectively by two clinicians blinded to outcome. Results In total, 71 patients with long-bone osteomyelitis were included. There was significant improvement from time of surgery to one year postoperatively in mean EQ-VAS (58.2 to 78.9; p < 0.001) and mean EQ-5D-3L index scores (0.284 to 0.740; p < 0.001). At one year following surgery, BACH ‘uncomplicated’ osteomyelitis was associated with better QoL compared to BACH ‘complex’ osteomyelitis (mean EQ-5D-3L 0.900 vs 0.685; p = 0.020; mean EQ-VAS 87.1 vs 73.6; p = 0.043). Patients with uncomplicated bone involvement (BACH type B1, cavitary) reported higher QoL at all time-points when compared to complex bone involvement (B2, segmental or B3, osteomyelitis involving a joint). Patients with good antimicrobial options (Ax or A1) gave higher outcome scores compared to patients with multidrug-resistant isolates (A2). The need for microvascular tissue transfer (C1 and C2) did not impact significantly on QoL. Patients without major comorbidities (uncomplicated, H1) reported higher QoL compared to those with significant disease (complex, H2). Conclusion Uncomplicated osteomyelitis, as defined by BACH, gave higher self-reported QoL when compared to complex cases. The bone involvement, antimicrobial options, and host status variables were able to stratify patients in terms of QoL. These data can be used to offer prognostic information to patients who are undergoing treatment for long bone osteomyelitis.

scholarly journals The Classification of Long Bone Osteomyelitis: A Systemic Review of the Literature

2017 ◽  
Vol 2 (4) ◽  
pp. 167-174 ◽  
Author(s):  
Andrew J. Hotchen ◽  
Martin A. McNally ◽  
Parham Sendi
Keyword(s):  
Systematic Review ◽  
Soft Tissue ◽  
Classification System ◽  
Classification Systems ◽  
Bone Involvement ◽  
Long Bone ◽  
Soft Tissue Coverage ◽  
Host Status ◽  
Long Bone Osteomyelitis

Abstract. Background: Osteomyelitis is a complex disease. Treatment involves a combination of bone resection, antimicrobials and soft-tissue coverage. There is a difficulty in unifying a classification system for long bone osteomyelitis that is generally accepted.Objectives: In this systematic review, we aim to investigate the classification systems for long bone osteomyelitis that have been presented within the literature. By doing this, we hope to elucidate the important variables that are required when classifying osteomyelitis.Methods: A complete search of the Medline, EMBASE, Cochrane and Ovid databases was undertaken. Following exclusion criteria, 13 classification systems for long-bone osteomyelitis were included for review.Results: The 13 classification systems that were included for review presented seven different variables that were used for classification. Ten of them used only one main variable, two used two variables and one used seven variables. The variables included bone involvement (used in 7 classification systems), acute versus chronic infection (used in 6), aetiopathogenesis (used in 3), host status (used in 3), soft tissue (used in 2), microbiology (used in 1) and location of infected bone (used in 1). The purpose of each classification system could be grouped as either descriptive (3 classification systems), prognostic (4) or for management (4). Two of the 13 classification systems were for both prognostic and management purposes.Conclusions: This systematic review has demonstrated a variety of variables used for classification of long bone osteomyelitis. While some variables are used to guide management and rehabilitation after surgery (e.g., bone defect, soft tissue coverage), others were postulated to provide prognostic information (e.g., host status). Finally, some variables were used for descriptive purposes only (aetiopathogenesis). In our view and from today's perspective, bone involvement, antimicrobial resistance patterns of causative micro-organisms, the need for soft-tissue coverage and host status are important variables to include in a classification system.

A Classification of Environmental “Hums” and Low Frequency Tinnitus

1983 ◽  
Vol 2 (2) ◽  
pp. 60-84 ◽  
Author(s):  
R.E. Walford
Keyword(s):  
United Kingdom ◽  
Low Frequency ◽  
Causal Link ◽  
Test Results ◽  
Frequency Distributions ◽  
General Classification ◽  
The United Kingdom ◽  
Airborne Noise ◽  
Take All

Two thousand people in the United Kingdom have complained of hearing a continuous hum, audible to themselves alone. Some of these “hummers” were given acoustic tests comprising hum-matching, normal and low-frequency audiometry, and a tinnitus versus real-airborne-noise distinguishing test. Results were compared with those from seventy-three control subjects (audiometry alone) and with fifty-five hospital out-patients known to have low-frequency tinnitus. It is shown that the frequency distributions of “hummers” hums and throbs correlate closely with those of patients' low-frequency tinnitus, although no causal link is established. A general classification of hum types is proposed and means for distinguishing them are described. Applied to 48 “hummers” these show that ten have low-frequency tinnitus and four are hearing a real airborne noise. The remaining, 4 cannot be classified, mainly because they did not take all the tests. It is not claimed that these figures apply to “hummers” in general since the 48 tested were not a true sample of the “hummer” population.

PENGEMBANGAN PERANGKAT PEMBELAJARAN MENGGUNAKAN PENDEKATAN CONTEXTUAL TEACHING AND LEARNING (CTL) DENGAN INTEGRASI NILAI-NILAI KARAKTER DI SMAN I TUGUMULYO

2014 ◽  
Vol 13 (3) ◽  
Author(s):  
Sri Wahyu Widyaningsih ◽  
Irfan Yusuf
Keyword(s):  
Teaching And Learning ◽  
Lesson Plan ◽  
Development Stage ◽  
Design Stage ◽  
Learning Tools ◽  
Test Results ◽  
Learning Materials ◽  
Is Research ◽  
Efficacy Trials

<p>The research is motivated not yet using CTL approach. In addition, the study provided yet foster the character value of students. This study aimed to the development of learning materials by using CTL approach with the integration of character value are valid, practical, and effective. The type of this research is research and development by using 4-D models. The stages of this research are define, design, and development. The define stage consists of analyzing of curriculum, students, and concept. Then, the learning materials as lesson plan, handout, student’s worksheet, and evaluation, were designed at design stage. The development stage was doing validity, practicality, and effectiveness test. The data of this research was collected by using validation instruments, questionnaire of students and teacher, observation and test instruments. The result of research with validity of the test results showed that the syllabus, lesson plans, teaching materials, worksheets and assessment sheets (cognitive, affective and psychomotor) developed very valid. The test results showed that the learning practicalities developed very practical. Based on the results of efficacy trials, it was stated that the developed learning very effectively used as learning tools are developed to improve the activity and competence of students in the cognitive, affective and psychomotor and behavioral character. And Those, learning materials by using CTL approach with the integration of character values are classification of very valid, very practical, and effective.</p>

scholarly journals Updating OMERACT Core Set of Domains for ANCA-associated Vasculitis: Patient Perspective Using the International Classification of Function, Disability, and Health

2019 ◽  
Vol 46 (10) ◽  
pp. 1415-1420 ◽  
Author(s):  
Nataliya Milman ◽  
Eilish McConville ◽  
Joanna C. Robson ◽  
Annelies Boonen ◽  
Peter Tugwell ◽  
...  
Keyword(s):  
The United States ◽  
International Classification ◽  
Antineutrophil Cytoplasmic Antibodies ◽  
Personal Factors ◽  
Disability And Health ◽  
Individual Interviews ◽  
The United Kingdom ◽  
Anca Associated Vasculitis ◽  
Core Set

Objective.Aspects of antineutrophil cytoplasmic antibodies–associated vasculitis (AAV) prioritized by patients with AAV were described using the International Classification of Function, Disability, and Health (ICF).Methods.Items identified during 14 individual interviews were incorporated into an ICF-based questionnaire administered to participants of 2 vasculitis patient symposia: 36 in the United Kingdom and 63 in the United States.Results.Categories identified as at least “moderately relevant” by ≥ 5% of subjects included 44 body functions, 14 body structures, 35 activities and participation, 31 environmental factors, and 38 personal factors.Conclusion.Identified categories differ from those identified by the current Outcome Measures in Rheumatology (OMERACT) core set and those prioritized by vasculitis experts.

scholarly journals Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 233
Author(s):  
Dong-Woon Lee ◽  
Sung-Yong Kim ◽  
Seong-Nyum Jeong ◽  
Jae-Hong Lee
Keyword(s):  
Dental Implant ◽  
Classification Accuracy ◽  
Reliability And Validity ◽  
Mechanical Complication ◽  
Radiographic Images ◽  
Acceptable Accuracy ◽  
Reliable Detection ◽  
Classification Evaluation ◽  
Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

scholarly journals Classification of SSVEP-based BCIs using Genetic Algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamideh Soltani ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Keivan Maghooli
Keyword(s):  
Genetic Algorithm ◽  
Dimension Reduction ◽  
Classification Accuracy ◽  
Bayesian Method ◽  
Computer Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Effective Dimension ◽  
Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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