A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors

Tang, Zhi-Ri; Zhu, Rong; Hu, Ruihan; Chen, Yanhua; Wu, Edmond Q.; Wang, Hao; He, Jin; Huang, Qijun; Chang, Sheng

doi:10.1109/tcds.2020.3003377

Cited by 31 publications

(18 citation statements)

References 41 publications

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“…These benefits can facilitate the sharing of decision-making information between clinicians and patients and promote the effective planning and rational use of healthcare services (15,16). In addition, the model can be retrained over time in order to improve predictive accuracy (17).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Wang

Liu

et al. 2021

Front. Public Health

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Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Wang

Liu

et al. 2021

Front. Public Health

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“…The nomogram is an effective quantitative method to assess risk and benefit and is widely used in clinical decision-making in a variety of diseases ( 28 ). In previous studies, several nomograms have been developed and validated to predict specific survival and overall survival in chondrosarcoma ( 29 , 30 ).…”

Section: Discussionmentioning

confidence: 99%

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study

Jin²,

Wu³

et al. 2022

Front. Oncol.

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BackgroundCurrently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma.MethodsClinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers.ResultsEight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients.ConclusionsThe clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

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“…CNN-based counting. With the development of deep learning [11][12][13][14], the CNN-based methods [15][16][17][18] can transform the highly congested images into the densityestimation problem. The point-level labeling is adopted to annotate the objects, and the density map is generated following the Gaussian filter [17].…”

Section: Related Workmentioning

confidence: 99%

A Multilayer Neural Network Merging Image Preprocessing and Pattern Recognition by Integrating Diffusion and Drift Memristors

Cited by 31 publications

References 41 publications

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study

AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

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