scholarly journals Prediction of Accuracy and Screw Size by Pedicle Anatomic Parameters and Screws in Idiopathic Scoliosis With Freehand Screw Placement Based on Machine Learning

2021 ◽  
Author(s):  
Qiyuan Dong ◽  
Zhou Huang ◽  
Yidan Sun ◽  
Yan Zeng ◽  
Zhongqiang Chen
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Idiopathic Scoliosis ◽  
Pedicle Screw ◽  
Goodness Of Fit ◽  
Classification Model ◽  
Screw Placement ◽  
Transverse Diameter ◽  
Morphological Parameters ◽  
Screw Penetration

Abstract Study Design: A retrospective study.Objective: To investigate a machine learning algorithm to explore the influence of pedicle morphological parameters and pedicle screw size on safe screw placement in the treatment of idiopathic scoliosis with freehand. And a model was built to guide the selection of screwMethods: We analyzed 52 patients with idiopathic scoliosis who underwent correction surgery in our hospital from June 2012 to December 2019, including 17 males and 35 females aged 10-20 years. All pedicle screws were placed by freehand. Preoperative and postoperative X-ray and CT scans of whole spine were performed to measure Cobb Angle and pedicle morphological parameters, including transverse diameter of the pedicle, sagittal diameter of the pedicle, length of the pedicle channel, rotation angle of vertebrae, angle of the sagittal plane of pedicle and angle of the horizontal plane of pedicle. Screw penetration grading was also evaluated. Random forest were used to build a machine learning model to help the decision making of choosing an appropriate screw based on pedicle parameters and screw size.Results: A total of 888 screws and pedicles were included. The satisfactory rate of screw placement was 88.5%. The pedicle screw size was analyzed and predicted based on screw penetration and pedicle morphological parameters. The AUROC of random forest classification model achieved 0.712. The goodness of fit(R2) was 0.546.Conclusion: Our model could provide guidance for the doctor to choose the length of the screw before surgery, and the classification model could also give a preliminary prediction of whether there would be anterior screw penetration based on the pedicle parameters.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Evaluation of thoracic pedicle screw placement in adolescent idiopathic scoliosis

2009 ◽  
Vol 18 (12) ◽  
pp. 1892-1897 ◽  
Author(s):  
Ahmet Yılmaz Şarlak ◽  
Bilgehan Tosun ◽  
Halil Atmaca ◽  
Hasan Tahsin Sarisoy ◽  
Levent Buluç
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Idiopathic Scoliosis ◽  
Pedicle Screw ◽  
Screw Placement ◽  
Pedicle Screw Placement ◽  
Thoracic Pedicle Screw

scholarly journals Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Yi Guo ◽  
Yushan Liu ◽  
Wenjie Ming ◽  
Zhongjin Wang ◽  
Junming Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Focal Cortical Dysplasia ◽  
Cortical Dysplasia ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Seizure Onset ◽  
Glioneuronal Tumors ◽  
Patients With Epilepsy

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

scholarly journals Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5280
Author(s):  
Irini Furxhi ◽  
Finbarr Murphy
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Sampling Technique ◽  
Exposure Dose ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Tissue Specific

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

scholarly journals CPT Data Interpretation Employing Different Machine Learning Techniques

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 265
Author(s):  
Stefan Rauter ◽  
Franz Tschuchnigg
Keyword(s):  
Machine Learning ◽  
Grain Size ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Cone Penetration ◽  
Tip Resistance ◽  
Machine Learning Models

The classification of soils into categories with a similar range of properties is a fundamental geotechnical engineering procedure. At present, this classification is based on various types of cost- and time-intensive laboratory and/or in situ tests. These soil investigations are essential for each individual construction site and have to be performed prior to the design of a project. Since Machine Learning could play a key role in reducing the costs and time needed for a suitable site investigation program, the basic ability of Machine Learning models to classify soils from Cone Penetration Tests (CPT) is evaluated. To find an appropriate classification model, 24 different Machine Learning models, based on three different algorithms, are built and trained on a dataset consisting of 1339 CPT. The applied algorithms are a Support Vector Machine, an Artificial Neural Network and a Random Forest. As input features, different combinations of direct cone penetration test data (tip resistance qc, sleeve friction fs, friction ratio Rf, depth d), combined with “defined”, thus, not directly measured data (total vertical stresses σv, effective vertical stresses σ’v and hydrostatic pore pressure u0), are used. Standard soil classes based on grain size distributions and soil classes based on soil behavior types according to Robertson are applied as targets. The different models are compared with respect to their prediction performance and the required learning time. The best results for all targets were obtained with models using a Random Forest classifier. For the soil classes based on grain size distribution, an accuracy of about 75%, and for soil classes according to Robertson, an accuracy of about 97–99%, was reached.

Effectiveness of the Thoracic Pedicle Screw Placement Using the Virtual Surgical Training System: A Cadaver Study

2018 ◽  
Vol 15 (6) ◽  
pp. 677-685 ◽  
Author(s):  
Yang Hou ◽  
Yanping Lin ◽  
Jiangang Shi ◽  
Huajiang Chen ◽  
Wen Yuan
Keyword(s):  
Pedicle Screw ◽  
Surgical Training ◽  
Pedicle Screws ◽  
Cadaver Study ◽  
Training System ◽  
Screw Placement ◽  
Control Group ◽  
Pedicle Screw Placement ◽  
Thoracic Pedicle Screw ◽  
Screw Penetration

Abstract BACKGROUND The virtual simulation surgery has initially exhibited its promising potentials in neurosurgery training. OBJECTIVE To evaluate effectiveness of the Virtual Surgical Training System (VSTS) on novice residents placing thoracic pedicle screws in a cadaver study. METHODS A total of 10 inexperienced residents participated in this study and were randomly assigned to 2 groups. The group using VSTS to learn thoracic pedicle screw fixation was the simulation training (ST) group and the group receiving an introductory teaching session was the control group. Ten fresh adult spine specimens including 6 males and 4 females with a mean age of 58.5 yr (range: 33-72) were collected and randomly allocated to the 2 groups. After exposing anatomic structures of thoracic spine, the bilateral pedicle screw placement of T6-T12 was performed on each cadaver specimen. The postoperative computed tomography scan was performed on each spine specimen, and experienced observers independently reviewed the placement of the pedicle screws to assess the incidence of pedicle breach. RESULTS The screw penetration rates of the ST group (7.14%) was significantly lower in comparison to the control group (30%, P < .05). Statistically significant difference in acceptable rates of screws also occurred between the ST (100%) and control (92.86%) group (P < .05). In addition, the average screw penetration distance in control group (2.37 mm ± 0.23 mm) was significantly greater than ST group (1.23 mm ± 0.56 mm, P < .05). CONCLUSION The virtual reality surgical training of thoracic pedicle screw instrumentation effectively improves surgical performance of novice residents compared to those with traditional teaching method, and can help new beginners to master the surgical technique within shortest period of time.

The Position of the Aorta Relative to the Spine for Pedicle Screw Placement in the Correction of Idiopathic Scoliosis

2012 ◽  
Vol 25 (4) ◽  
pp. E82-E86 ◽  
Author(s):  
Jiaming Liu ◽  
Jianxiong Shen ◽  
Jianguo Zhang ◽  
Shugang Li ◽  
Hong Zhao ◽  
...  
Keyword(s):  
Idiopathic Scoliosis ◽  
Pedicle Screw ◽  
Screw Placement ◽  
Pedicle Screw Placement

scholarly journals Machine learning in the diagnosis of Myocardial Infarction with Non-Obstructive Coronary Arteries

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
M J Espinosa Pascual ◽  
P Vaquero Martinez ◽  
V Vaquero Martinez ◽  
J Lopez Pais ◽  
B Izquierdo Coronel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Obstructive Coronary Artery Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector

Abstract Introduction Out of all patients admitted with Myocardial Infarction, 10 to 15% have Myocardial Infarction with Non-Obstructive Coronaries Arteries (MINOCA). Classification algorithms based on deep learning substantially exceed traditional diagnostic algorithms. Therefore, numerous machine learning models have been proposed as useful tools for the detection of various pathologies, but to date no study has proposed a diagnostic algorithm for MINOCA. Purpose The aim of this study was to estimate the diagnostic accuracy of several automated learning algorithms (Support-Vector Machine [SVM], Random Forest [RF] and Logistic Regression [LR]) to discriminate between people suffering from MINOCA from those with Myocardial Infarction with Obstructive Coronary Artery Disease (MICAD) at the time of admission and before performing a coronary angiography, whether invasive or not. Methods A Diagnostic Test Evaluation study was carried out applying the proposed algorithms to a database constituted by 553 consecutive patients admitted to our Hospital with Myocardial Infarction. According to the definitions of 2016 ESC Position Paper on MINOCA, patients were classified into two groups: MICAD and MINOCA. Out of the total 553 patients, 214 were discarded due to the lack of complete data. The set of machine learning algorithms was trained on 244 patients (training sample: 75%) and tested on 80 patients (test sample: 25%). A total of 64 variables were available for each patient, including demographic, clinical and laboratorial features before the angiographic procedure. Finally, the diagnostic precision of each architecture was taken. Results The most accurate classification model was the Random Forest algorithm (Specificity [Sp] 0.88, Sensitivity [Se] 0.57, Negative Predictive Value [NPV] 0.93, Area Under the Curve [AUC] 0.85 [CI 0.83–0.88]) followed by the standard Logistic Regression (Sp 0.76, Se 0.57, NPV 0.92 AUC 0.74 and Support-Vector Machine (Sp 0.84, Se 0.38, NPV 0.90, AUC 0.78) (see graph). The variables that contributed the most in order to discriminate a MINOCA from a MICAD were the traditional cardiovascular risk factors, biomarkers of myocardial injury, hemoglobin and gender. Results were similar when the 19 patients with Takotsubo syndrome were excluded from the analysis. Conclusion A prediction system for diagnosing MINOCA before performing coronary angiographies was developed using machine learning algorithms. Results show higher accuracy of diagnosing MINOCA than conventional statistical methods. This study supports the potential of machine learning algorithms in clinical cardiology. However, further studies are required in order to validate our results. FUNDunding Acknowledgement Type of funding sources: None. ROC curves of different algorithms

Automatic Identification of Rock Formation Type While Drilling Using Machine Learning Based Data-Driven Models

2021 ◽  
Author(s):  
Enrique Z. Losoya ◽  
Narendra Vishnumolakala ◽  
Samuel F. Noynaert ◽  
Zenon Medina-Cetina ◽  
Satish Bukkapatnam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Prediction Accuracy ◽  
Classification Model ◽  
Data Driven ◽  
Classification Algorithms ◽  
Rock Formation ◽  
Mechanical Specific Energy ◽  
Formation Type

Abstract The objective of this study is to present a novel rock formation identification model using a data-driven modeling approach. This study explores the use of real-time drilling data to train and validate a classification model to improve the efficiency of the drilling process by reducing Mechanical Specific Energy (MSE). In this study, we demonstrate the feasibility of a layer-based determination and change detection of properties of rock formation currently being drilled as accurately and fast as possible. Data for this study was collected from a custom-built lab-scale drilling rig equipped with multiple sensors. The experiment was conducted by drilling through an arrangement of different rock formations of varying rock strength properties. Data was recorded and stored at a frequency of 2 kHz, then filtered, processed, and downsampled to extract relevant features. This dataset was used to train an Artificial Neural Network and other machine learning classification algorithms. Feature selection was made first with ten most notable features found by Random Forest, and the second set with derived measurements and down-sampled dynamic features from the sensors. The classification analysis was divided into two steps: the best predictors/features extraction and classification model building. The models were trained using multiple classification algorithms, namely logistic regression, linear discriminant analysis (LDA), Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN). It was found that random forest and ANN performed the best with prediction accuracy of 99.48% and 99.58%, respectively, for the data set with ten most prominent features. The high prediction rate accuracy for the most prominent predictors suggests that if the high-frequency data can be processed in real-time, predicting what formation we are drilling in is possible to achieve in near real-time. This can lead to significant savings for drilling companies as optimal drilling parameters can be computed, and in turn, optimized Mechanical Specific Energy can be obtained in real-time. Since the rock formation identification is time-consuming, we also describe here an alternative approach using slightly less accurate but equally powerful dynamic predictors. In this case, we show that our dynamic predictor models with RF and ANN yielded prediction accuracy of 96.30% and 95.61%, respectively. Both the prominent feature and dynamic predictor approaches are described in detail in this paper. Our results suggest that accurately predicting rock formation type in real-time while drilling is very much feasible with lesser computational cost and complexity. This study provides the building blocks for the development of a completely autonomous downhole device and Electronic Device Recorders (EDR) that reduces the need for highly sophisticated sensors or data transmission processes downhole.

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