Sectorization of the Axial Image of Cervical Vertebrae, a Heuristic Approach for Automatic Analysis in Artificial Intelligence

1987 ◽  
pp. 84-87
Author(s):  
A. Wackenheim
Keyword(s):  
Artificial Intelligence ◽  
Cervical Vertebrae ◽  
Heuristic Approach ◽  
Automatic Analysis ◽  
Axial Image

Prediction of hand‐wrist maturation stages based on cervical vertebrae images using artificial intelligence

2021 ◽  
Author(s):  
Dong‐Wook Kim ◽  
Jinhee Kim ◽  
Taesung Kim ◽  
Taewoo Kim ◽  
Yoon‐Ji Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cervical Vertebrae ◽  
Maturation Stages

scholarly journals Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models

2020 ◽  
Vol 49 (5) ◽  
pp. 20190441 ◽  
Author(s):  
Hakan Amasya ◽  
Derya Yildirim ◽  
Turgay Aydogan ◽  
Nazan Kemaloglu ◽  
Kaan Orhan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cervical Vertebrae ◽  
Clinical Decision ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Human Observer ◽  
Ann Model ◽  
Visual Evaluation ◽  
Cervical Vertebral Maturation

Objectives: This study aimed to develop five different supervised machine learning (ML) classifier models using artificial intelligence (AI) techniques and to compare their performance for cervical vertebral maturation (CVM) analysis. A clinical decision support system (CDSS) was developed for more objective results. Methods: A total of 647 digital lateral cephalometric radiographs with visible C2, C3, C4 and C5 vertebrae were chosen. Newly developed software was used for manually labelling the samples, with the integrated CDSS developed by evaluation of 100 radiographs. On each radiograph, 26 points were marked, and the CDSS generated a suggestion according to the points and CVM analysis performed by the human observer. For each sample, 54 features were saved in text format and classified using logistic regression (LR), support vector machine, random forest, artificial neural network (ANN) and decision tree (DT) models. The weighted κ coefficient was used to evaluate the concordance of classification and expert visual evaluation results. Results: Among the CVM stage classifier models, the best result was achieved using the ANN model (κ = 0.926). Among cervical vertebrae morphology classifier models, the best result was achieved using the LR model (κ = 0.968) for the presence of concavity, and the DT model (κ = 0.949) for vertebral body shapes. Conclusions: This study has proposed ML models for CVM assessment on lateral cephalometric radiographs, which can be used for the prediction of cervical vertebrae morphology. Further studies should be done especially of forensic applications of AI models through CVM evaluations.

Artificial Intelligence-Based Quality Management and Detection System for Personalized Learning

2021 ◽  
Author(s):  
Haixia Yu ◽  
Jidong Wang ◽  
Mohanraj Murugesan ◽  
A. B. M. Salman Rahman
Keyword(s):  
Artificial Intelligence ◽  
Quality Management ◽  
Teaching And Learning ◽  
Detection System ◽  
Personalized Learning ◽  
Heuristic Approach ◽  
Performance Ratio ◽  
Student Profile ◽  
Detection Systems ◽  
Learning Path

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.

scholarly journals Automatic Analysis of Synchrotron Radiated X-ray Scattering Data of Crystalline Polymers Using Artificial Intelligence for Science

Seikei-Kakou ◽  
2021 ◽  
Vol 33 (10) ◽  
pp. 362-364
Author(s):  
Toshiki Tokuhira ◽  
Yuki Tanaka ◽  
Yohei Nakanishi ◽  
Kazuki Mita ◽  
Hiroshi Godai ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Automatic Analysis ◽  
Scattering Data ◽  
X Ray ◽  
Crystalline Polymers ◽  
X Ray Scattering ◽  
Ray Scattering

Artificial Intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A Systematic Review

2021 ◽  
pp. 102165
Author(s):  
Vilson Soares de Siqueira ◽  
Moisés Marcos Borges ◽  
Rogério Gomes Furtado ◽  
Colandy Nunes Dourado ◽  
Ronaldo Martins da Costa
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Automatic Analysis ◽  
Medical Decisions

Heuristic approach to the artificial intelligence design challenge

1988 ◽  
Vol 11 (5) ◽  
pp. 406-411
Author(s):  
Ting-Yi Sung ◽  
Her-Jiun Sheu ◽  
Denis Naddef
Keyword(s):  
Artificial Intelligence ◽  
Heuristic Approach ◽  
Design Challenge

scholarly journals Usage and comparison of artificial intelligence algorithms for determination of growth and development by cervical vertebrae stages in orthodontics

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Hatice Kök ◽  
Ayse Merve Acilar ◽  
Mehmet Said İzgi
Keyword(s):  
Artificial Intelligence ◽  
Decision Tree ◽  
Growth And Development ◽  
Cervical Vertebrae ◽  
Reference Points ◽  
Support Vector ◽  
Linear Measurements ◽  
K Nearest Neighbors ◽  
Stable Algorithm ◽  
Average Rank

Abstract Background Growth and development can be determined by cervical vertebrae stages that were defined on the cephalometric radiograph. Artificial intelligence has the ability to perform a variety of activities, such as prediction-classification in many areas of life, by using different algorithms, In this study, we aimed to determine cervical vertebrae stages (CVS) for growth and development periods by the frequently used seven artificial intelligence classifiers, and to compare the performance of these algorithms with each other. Methods Cephalometric radiographs, that were obtained from 300 individuals aged between 8 and 17 years were included in our study. Nineteen reference points were defined on second, third, and 4th cervical vertebrae, and 20 different linear measurements were taken. Seven algorithms of artificial intelligence that are frequently used in the field of classification were selected and compared. These algorithms are k-nearest neighbors (k-NN), Naive Bayes (NB), decision tree (Tree), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and logistic regression (Log.Regr.) algorithms. Results According to confusion matrices decision tree, CSV1 (97.1%)–CSV2 (90.5%), SVM: CVS3 (73.2%)–CVS4 (58.5%), and kNN: CVS 5 (60.9%)–CVS 6 (78.7%) were the algorithms with the highest accuracy in determining cervical vertebrae stages. The ANN algorithm was observed to have the second-highest accuracy values (93%, 89.7%, 68.8%, 55.6%, and 78%, respectively) in determining all stages except CVS5 (47.4% third highest accuracy value). According to the average rank of the algorithms in predicting the CSV classes, ANN was the most stable algorithm with its 2.17 average rank. Conclusion In our experimental study, kNN and Log.Regr. algorithms had the lowest accuracy values. SVM-RF-Tree and NB algorithms had varying accuracy values. ANN could be the preferred method for determining CVS.

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