scholarly journals Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eun-Gyu Ha ◽  
Kug Jin Jeon ◽  
Young Hyun Kim ◽  
Jae-Young Kim ◽  
Sang-Sun Han
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
Histogram Equalization ◽  
Permanent Dentition ◽  
Clinical Use ◽  
Image Preprocessing ◽  
Test Dataset ◽  
Panoramic Radiographs ◽  
Proposed Model ◽  
External Test

AbstractThis study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

scholarly journals Digital Signature of Document using Human Palm

2019 ◽  
Vol 9 (1) ◽  
pp. 4293-4298
Keyword(s):  
Digital Signature ◽  
Histogram Equalization ◽  
Acceptance Rate ◽  
Equal Error Rate ◽  
False Acceptance Rate ◽  
Image Preprocessing ◽  
Time Period ◽  
Proposed Model ◽  
Cubic Spline Function ◽  
Two Sides

A digital signature is a checksum which depends on the time period during which it was produced. Human palm biometric is one of the fastest, accurate, reliable and secure biometric techniques for identification and verification because it provides automatic authentication of an individual based on unique features in palm structure. In this paper, an efficient digital signature model for the document is proposed by using human palm. Human palm can give unique features which can be used in generating a secure digital signature. Therefore, this model consists of two sides: the embedding side and extracting side. The embedding side includes (1) image preprocessing stage:(color to grayscale and histogram equalization). (2) feature extraction stage:(GLCM (Haralick) algorithm). (3) Generating digital signature stage:(Elliptic Curve and Cubic Spline function with MD5 algorithm). While the extracting side contains extracting signature stage and matching stage. The accuracy of the generated digital signature by the proposed model is 100%, however false acceptance rate (FAR) is 0%, false reject rate (FRR) is 0%, and equal error rate (ERR) is 0%.

EVALUATION OF OCCURRENCE OF HYPERDONTIA IN NON-SYNDROMIC POPULATION FROM BIHAR REGION

2019 ◽  
Vol 3 (10) ◽  
Author(s):  
Dr. Minti Kumari ◽  
Dr. Madhuri Kumari ◽  
Dr Anurag Rai ◽  
Dr. Navin Kumar
Keyword(s):  
Panoramic Radiograph ◽  
Permanent Dentition ◽  
Dental Anomalies ◽  
Supernumerary Teeth ◽  
Panoramic Radiographs ◽  
Future Studies ◽  
Asian Populations ◽  
Males And Females ◽  
The Individual ◽  
Clinical Records

It is evident that hyperdontia is more common in the permanent dentition than in the primary. There is a considerable difference between males and females in the prevalence of these teeth in permanent dentition; hyperdontia is twice as common in males as in females. However, this approximation varies in terms of location, other associating syndromes that may be present, and the ethnicity of the individual. In terms of ethnicity, it can be seen that hyperdontia is in fact less common in Caucasian than in Asian populations. There is evidence to show that an individual is more likely to have hyperdontia if other members of their family also have the condition. Hence the present study was planned for evaluation of occurrence of hyperdontia in non-syndromic  population from Bihar Region. The present study was planned in Public Health Dentistry, Patna Dental College and Hospital, Patna, Bihar. Total 195 patients referred to Department of Dentistry were evaluated in the present study. Panoramic radiographs and clinical records of patients above the age of 18 years and without any syndromic features were selected for the study.  All the radiographs were examined for the presence of supernumerary teeth, their location, morphology, and number. Morphologically, teeth were classified as conical, tuberculate, supplemental, and odontoma. Early diagnosis of dental anomalies can prevent some esthetic, orthodontic, and periodontal problems, and knowledge of the prevalence and distribution of the anomalies may help clinicians to the detection of these anomalies at early stages. Our study evaluated the prevalence of selected dental anomalies; future studies should investigate the prevalence of dental anomalies of all types. Keywords: Hyperdontia, non-syndromic, panoramic radiograph, supernumerary teeth, etc.

ICD10Net: An Artificial Intelligence Algorithm with Medical Background Conducts ICD-10-CM Coding Task with Outstanding Performance (Preprint)

2019 ◽  
Author(s):  
Chin Lin ◽  
Yu-Sheng Lou ◽  
Chia-Cheng Lee ◽  
Chia-Jung Hsu ◽  
Ding-Chung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
General Hospital ◽  
Pearson Correlation ◽  
Model Performance ◽  
International Classification Of Diseases ◽  
Free Text ◽  
Daily Work ◽  
Medical Background ◽  
Icd 10 ◽  
F Measure

BACKGROUND An artificial intelligence-based algorithm has shown a powerful ability for coding the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in discharge notes. However, its performance still requires improvement compared with human experts. The major disadvantage of the previous algorithm is its lack of understanding medical terminologies. OBJECTIVE We propose some methods based on human-learning process and conduct a series of experiments to validate their improvements. METHODS We compared two data sources for training the word-embedding model: English Wikipedia and PubMed journal abstracts. Moreover, the fixed, changeable, and double-channel embedding tables were used to test their performance. Some additional tricks were also applied to improve accuracy. We used these methods to identify the three-chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. Subsequently, 94,483-labeled discharge notes from June 1, 2015 to June 30, 2017 were used from the Tri-Service General Hospital in Taipei, Taiwan. To evaluate performance, 24,762 discharge notes from July 1, 2017 to December 31, 2017, from the same hospital were used. Moreover, 74,324 additional discharge notes collected from other seven hospitals were also tested. The F-measure is the major global measure of effectiveness. RESULTS In understanding medical terminologies, the PubMed-embedding model (Pearson correlation = 0.60/0.57) shows a better performance compared with the Wikipedia-embedding model (Pearson correlation = 0.35/0.31). In the accuracy of ICD-10-CM coding, the changeable model both used the PubMed- and Wikipedia-embedding model has the highest testing mean F-measure (0.7311 and 0.6639 in Tri-Service General Hospital and other seven hospitals, respectively). Moreover, a proposed method called a hybrid sampling method, an augmentation trick to avoid algorithms identifying negative terms, was found to additionally improve the model performance. CONCLUSIONS The proposed model architecture and training method is named as ICD10Net, which is the first expert level model practically applied to daily work. This model can also be applied in unstructured information extraction from free-text medical writing. We have developed a web app to demonstrate our work (https://linchin.ndmctsgh.edu.tw/app/ICD10/).

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.

Design of English reading and learning management system in college education based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Fen Zhang ◽  
Min She
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
College Education ◽  
Personalized Learning ◽  
Learning System ◽  
Model Function ◽  
English Reading ◽  
Function Modules ◽  
Learning Platforms ◽  
Learning Management

English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Abhinav Kumar ◽  
Sanjay Kumar Singh ◽  
K Lakshmanan ◽  
Sonal Saxena ◽  
Sameer Shrivastava
Keyword(s):  
Differential Privacy ◽  
Model Performance ◽  
Healthcare Services ◽  
Cloud Services ◽  
Efficient Computation ◽  
Sensitive Data ◽  
Classification Framework ◽  
Proposed Model ◽  
Diagnostic Framework ◽  
Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs

2021 ◽  
pp. 20200172
Author(s):  
Münevver Coruh Kılıc ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Confusion Matrix ◽  
Forensic Identification ◽  
Deciduous Teeth ◽  
Time Saving ◽  
Panoramic Radiographs ◽  
Intelligence System ◽  
Promising Tool ◽  
Panoramic Images

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 1-12
Author(s):  
Sumit Das ◽  
Manas Kumar Sanyal ◽  
Sarbajyoti Mallik
Keyword(s):  
Artificial Intelligence ◽  
Web Application ◽  
Short Term Memory ◽  
Real Data ◽  
Fake News ◽  
Artificial Intelligence Technique ◽  
News Reports ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

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