scholarly journals Artificial intelligence for the detection of vertebral fractures on plain spinal radiography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kazuma Murata ◽  
Kenji Endo ◽  
Takato Aihara ◽  
Hidekazu Suzuki ◽  
Yasunobu Sawaji ◽  
...  
Keyword(s):  
Neural Network ◽  
Sensitivity And Specificity ◽  
Vertebral Fractures ◽  
Early Identification ◽  
Delayed Diagnosis ◽  
Diagnostic Errors ◽  
Diagnostic Strategy ◽  
Image Identification ◽  
Orthopedic Surgeons

AbstractVertebral fractures (VFs) cause serious problems, such as substantial functional loss and a high mortality rate, and a delayed diagnosis may further worsen the prognosis. Plain thoracolumbar radiography (PTLR) is an essential method for the evaluation of VFs. Therefore, minimizing the diagnostic errors of VFs on PTLR is crucial. Image identification based on a deep convolutional neural network (DCNN) has been recognized to be potentially effective as a diagnostic strategy; however, the accuracy for detecting VFs has not been fully investigated. A DCNN was trained with PTLR images of 300 patients (150 patients with and 150 without VFs). The accuracy, sensitivity, and specificity of diagnosis of the model were calculated and compared with those of orthopedic residents, orthopedic surgeons, and spine surgeons. The DCNN achieved accuracy, sensitivity, and specificity rates of 86.0% [95% confidence interval (CI) 82.0–90.0%], 84.7% (95% CI 78.8–90.5%), and 87.3% (95% CI 81.9–92.7%), respectively. Both the accuracy and sensitivity of the model were suggested to be noninferior to those of orthopedic surgeons. The DCNN can assist clinicians in the early identification of VFs and in managing patients, to prevent further invasive interventions and a decreased quality of life.

Prediction of Prolonged Ventilation after Coronary Artery Bypass Grafting: Data from an Artificial Neural Network

2017 ◽  
Vol 20 (1) ◽  
pp. 007 ◽  
Author(s):  
Eric Stephen Wise ◽  
David P. Stonko ◽  
Zachary A. Glaser ◽  
Kelly L. Garcia ◽  
Jennifer J. Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Ventilation ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Cardiopulmonary Bypass ◽  
Coronary Artery ◽  
Early Identification ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Prolonged Ventilation

Objectives: The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN).Methods: Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both “traditional” multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters.Results: Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.

Influence of the content of postgraduate education of radiologists on the number of diagnostic pitfalls

2018 ◽  
pp. 109-117
Author(s):  
S. Р. Morozov ◽  
A. V. Kvasyuk ◽  
N. N. Vetsheva ◽  
N. V. Ledikhova ◽  
D. N. Kureshova
Keyword(s):  
Clinical Education ◽  
Professional Education ◽  
Information Service ◽  
Diagnostic Errors ◽  
Postgraduate Education ◽  
Length Of Service ◽  
Professional Activities ◽  
Specialty Differences ◽  
Medical Radiology

Background.Question about the quality and format of postgraduate education of doctors raises increasingly in recent years. Development of professional standards and transition to a system of continuing professional education have allowed professional communities to raise issues of the quality of modern education but there is no clear evidence of the dependence of the level of education and the quality of medical care in the accessible literature. Experts of Research and Practical Center of Medical Radiology carried out the identification of dependence of post-graduate education length for radiologists and the quality of their work that can serve as a rationale for amending the system of doctors training.Patients and methods.The data on education and actual work of 85 radiologists of out-patient and in-patient units of medical organizations of the Moscow Healthcare Department have been analyzed. According to the results of the audit of diagnostic studies, carried out in the “Unified Radiological Information Service” system by the specialists of the Research and Practical Center of Medical Radiology, the final assessment of the work of each radiologist was formed, which reflects the presence or absence of diagnostic discrepancies.Results.Parameters of diagnostic errors depending on the age of doctors, the general length of service and the length of service as radiologist, the duration of postgraduate education in the clinical specialty and the specialty “radiology” have been compared.As a result of the analysis, it was found that the increase in the proportion of diagnostic differences is directly related to the increase in the age of the doctor and does not depend on either the length of service or the time of work in the specialty. Differences between the groups of physicians with the largest (professional retraining after clinical residency) and the smallest (clinical education + radiology) percentage of clinically significant discrepancies are statistically significant (p = 0.05, at the normative value of the Student's test score of 2.16).Conclusion.The inverse relationship between the duration of training of the radiologist in the specialty and the proportion of diagnostic errors, which can serve as a significant justification for making proposals for the exclusion of professional retraining within 576 hours for admission to professional activities of radiologists.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

scholarly journals Differentiating PSP from MSA using MR planimetric measurements: a systematic review and meta-analysis

2021 ◽  
Author(s):  
Beatrice Heim ◽  
Florian Krismer ◽  
Klaus Seppi
Keyword(s):  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Index Test ◽  
Study Quality ◽  
Parkinsonian Syndromes ◽  
Quantitative Mr ◽  
Meta Analyses ◽  
Planimetric Measurements

AbstractDifferential diagnosis of parkinsonian syndromes is considered one of the most challenging in neurology. Quantitative MR planimetric measurements were reported to discriminate between progressive supranuclear palsy (PSP) and non-PSP-parkinsonism. Several studies have used midbrain to pons ratio (M/P) and the Magnetic Resonance Parkinsonism Index (MRPI) in distinguishing PSP patients from those with Parkinson's disease. The current meta-analysis aimed to compare the performance of these measures in discriminating PSP from multiple system atrophy (MSA). A systematic MEDLINE review identified 59 out of 2984 studies allowing a calculation of sensitivity and specificity using the MRPI or M/P. Meta-analyses of results were carried out using random effects modelling. To assess study quality and risk of bias, the QUADAS-2 tool was used. Eight studies were suitable for analysis. The meta‐analysis showed a pooled sensitivity and specificity for the MRPI of PSP versus MSA of 79.2% (95% CI 72.7–84.4%) and 91.2% (95% CI 79.5–96.5%), and 84.1% (95% CI 77.2–89.2%) and 89.2% (95% CI 81.8–93.8%), respectively, for the M/P. The QUADAS-2 toolbox revealed a high risk of bias regarding the methodological quality of patient selection and index test, as all patients were seen in a specialized outpatient department without avoiding case control design and no predefined threshold was given regarding MRPI or M/P cut-offs. Planimetric brainstem measurements, in special the MRPI and M/P, yield high diagnostic accuracy for the discrimination of PSP from MSA. However, there is an urgent need for well-designed, prospective validation studies to ameliorate the concerns regarding the risk of bias.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Hybrid Reference Current Generation Theory for Solar Fed UPFC System

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1527
Author(s):  
R. Senthil Kumar ◽  
K. Mohana Sundaram ◽  
K. S. Tamilselvan
Keyword(s):  
Neural Network ◽  
Power Factor ◽  
Reactive Power ◽  
Voltage Source ◽  
Unity Power Factor ◽  
Current Generation ◽  
Load Voltage ◽  
Reference Current Generation ◽  
Source Current

The extensive usage of power electronic components creates harmonics in the voltage and current, because of which, the quality of delivered power gets affected. Therefore, it is essential to improve the quality of power, as we reveal in this paper. The problems of load voltage, source current, and power factors are mitigated by utilizing the unified power flow controller (UPFC), in which a combination of series and shunt converters are combined through a DC-link capacitor. To retain the link voltage and to maximize the delivered power, a PV module is introduced with a high gain converter, named the switched clamped diode boost (SCDB) converter, in which the grey wolf optimization (GWO) algorithm is instigated for tracking the maximum power. To retain the link-voltage of the capacitor, the artificial neural network (ANN) is implemented. A proper control of UPFC is highly essential, which is achieved by the reference current generation with the aid of a hybrid algorithm. A genetic algorithm, hybridized with the radial basis function neural network (RBFNN), is utilized for the generation of a switching sequence, and the generated pulse has been given to both the series and shunt converters through the PWM generator. Thus, the source current and load voltage harmonics are mitigated with reactive power compensation, which results in attaining a unity power factor. The projected methodology is simulated by MATLAB and it is perceived that the total harmonic distortion (THD) of 0.84% is attained, with almost a unity power factor, and this is validated with FPGA Spartan 6E hardware.

scholarly journals Palliative care and complex chronic conditions since the perspective of healthcare professionals

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
A Doñate-Martínez ◽  
L Llop ◽  
J Garcés
Keyword(s):  
Primary Care ◽  
Palliative Care ◽  
Early Identification ◽  
Chronic Conditions ◽  
Healthcare Professionals ◽  
Care Pathway ◽  
Holistic View ◽  
Complex Chronic Conditions ◽  
Symptom Intensity

Abstract Background According to the WHO, palliative care (PC) is applicable early in the course of illness together with other curative therapies. Early PC has demonstrated beneficial effects on quality of life and symptom intensity among cancer patients. However, PC is not as early integrated on the care pathway of complex chronic conditions (CCC). This abstract presents barriers and needs identified to effectively implement early PC on CCC performed under the EU-funded InAdvance project (ref.: 825750). Methods Semi-structured interviews were performed with 16 healthcare professionals (HPs) from primary care and hospital settings working with older patients with CCC in Valencia (Spain). Results Interviews reported that main needs identified to provide early PC are: (a) coordinated strategies between multi-setting HPs to an early identification of CCC patients in need of PC; (b) adequate resources to attend patients' PC needs from a holistic view, i.e. psychosocial and spiritual needs; and (c) early integration of basic PC at primary care teams. The main barriers identified were: (a) stereotypes associated to the traditional PC approach; (b) poor knowledge from HPs of the PC holistic approach; and (c) lack of specific protocols or pathways for CCC in need of PC. Conclusions Specific skills and resources are the most relevant needs to effectively provide early PC among patients with CCC. First, it is urgent to demystify the negative culture-related vision of PC that is commonly associated to sedation and last days of a person's life. Also, multidisciplinary HPs require specific training to identify and provide early PC tailored to CCC. And, it is required a strategic and multi-setting organizational approach with fluent information flow and coordinated roles. Key messages Healthcare expenditure would be considerably reduced, especially at hospital and emergency units, with an early identification of patients with CCC in need of PC. Empowering primary HPs in PC would improve the quality of care of patients with CCC.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

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