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Teeth periapical lesion prediction using machine learning techniques
... It showed the use of AI in dental research increases from one paper in 2008 to 20 in 2019. It includes detection and diagnosis of dental caries, proximal dental caries, VRF, apical lesions and so on [9][10][11][12][13][14][15][16][17][18][19][20]. ...
... It showed the use of AI in dental research increases from one paper in 2008 to 20 in 2019. It includes detection and diagnosis of dental caries, proximal dental caries, VRF, apical lesions and so on [9][10][11][12][13][14][15][16][17][18][19][20]. Most cases of AI medical applications mention deep learning used in their study [21][22][23]. ...
... In most cases, deep learning (DL) is achieved by neural networks (NNs). These neural networks include artificial neural networks (ANNs) [10][11][12], convolutional neural networks [13][14][15][16][17][18], Bayesian neural networks (BNNs) [19] and probabilistic neural networks (PNNs) [20]. ANN is the kernel architecture in this study. ...
Predicting the risk of root fractures following root canal therapy requires diagnosis of the dental history and status of patients. However, dental history is a kind of categorical data type that is not easy to combine with numerical data to obtain good performance in deep learning. The accuracy of support vector machine (SVM) and artificial neural networks (ANNs) is 71.7% and 73.1%, respectively. In this study, a three-stage fusion neural network (TSFNN) is proposed to improve the multiple types of clinical data in the dental field based on ANNs. Clinical data were obtained from 145 teeth, comprising 97 fractured teeth and 48 nonfractured teeth. Each dataset contained 17 items, which were divided into 10 categorical items and 7 numerical items. TSFNN combines numerical and categorical NN with batch normalization and embedding layer techniques and can produce the accuracy of 82.1% and a 19.1% improvement in F1-score. It shows impressive performance in predicting the risk of root fracture. Furthermore, due to the limited amount of clinical data, it is believed that such a pilot study can effectively improve the results when the amount of clinical data is insufficient.
... In comparison with traditional methods, ML models offer higher accuracy and consistency as they can automatically process and analyse a large quantity of dental radiographs, through learning the patterns and characteristics associated with certain dental condition, identifying signs that might not be visible for dentists' eyes with reliable diagnostic results. Different ML models are applied in various dental symptoms, based on various types of algorithms to analyse radiographs (panoramic X-ray [65], periapical [66], cephalometric [67], etc.), intraoral [68] or histopathological images [69], facilitating timely intervention, and development of personalised preventive strategies. Moreover, predictive and differentiate analytics on the management of temporomandibular disorders (TMD) [70] or progression [71] of dental diseases such as periodontal and apical lesions (cysts, granulomas and abscesses) also guide clinicians in selecting the most appropriate treatment plans tailored to individual patients. ...
Background: Machine learning (ML) is transforming dentistry by setting new standards for precision and efficiency in clinical practice, while driving improvements in care delivery and quality. Objectives: This review: (1) states the necessity to develop ML in dentistry for the purpose of breaking the limitations of traditional dental technologies; (2) discusses the principles of ML-based models utilised in dental clinical practice and care; (3) outlines the application respects of ML in dentistry; and (4) highlights the prospects and challenges to be addressed. Data and sources: In this narrative review, a comprehensive search was conducted in PubMed/MEDLINE, Web of Science, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Conclusions: Machine Learning has demonstrated significant potential in dentistry with its intelligently assistive function, promoting diagnostic efficiency, personalised treatment plans and related streamline workflows. However, challenges related to data privacy, security, interpretability, and ethical considerations were highly urgent to be addressed in the next review, with the objective of creating a backdrop for future research in this rapidly expanding arena. Clinical significance: Development of ML brought transformative impact in the fields of dentistry, from diagnostic, personalised treatment plan to dental care workflows. Particularly, integrating ML-based models with diagnostic tools will significantly enhance the diagnostic efficiency and precision in dental surgeries and treatments.
... K-nearest neighbour It is a classification and regression model which operates on the principle of proximity, identifying the 'k' closest data instances to the new query point and predicting the label based on the predominant category among these neighbours (Sajad et al., 2019) The diagnosis of periapical lesions in teeth (Mahmoud et al., 2016) Linear regression It is a method that establishes a linear relationship between a dependent variable and one or more independent variables through a fitted linear equation. It is a critical tool in research for quantifying and predicting the influence of independent factors on an outcome of interest (Su et al., 2012) Predicting case difficulty in endodontic microsurgery (Qu et al., 2023), and assessing the impact of different demographic variables on the volumetric changes in external cervical resorption defects Decision tree Decision trees are a type of model used for making predictions. ...
The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.
... Accuracy is an important evaluation indicator in neural network models. To calculate the accuracy, the concepts of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) are introduced [49]. ...
With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.
... There were unexpected findings, with a mean accuracy of roughly 77.2%, and the authors stated that the reference method was better than this approach, but that the results may be improved in future work utilizing optimization techniques. [29] Setzer et al. [30] concluded that a DL algorithm trained in a constrained CBCT setting produced high lesion detection accuracy. Enhanced versions of AI may improve overall voxel-matching accuracy, thus, revealing an accuracy of 93% for this study. ...
Aim: With the help of developments in artificial intelligence (AI), picture archiving systems, and computer-aided diagnostic systems, dentists have been able to augment the quality of treatment and ensure a favorable outcome, by improving and facilitating the delivery of appropriate dental care. There has been a breakthrough in designing the diagnosis, treatment plans, and predicting prognoses recently, which has helped to explore newer options for better treatment. Materials and Methods: A literature search was conducted using MeSH terms in a variety of databases, including PubMed, Cochrane, Scopus, and Web of Science, to gather information on “Artificial intelligence (AI) in endodontics.” Unpublished data, literature written in other languages, and articles with only abstracts were discarded. Forty-one relevant articles were included. Results: Since there were not many papers referring to AI in endodontics, papers published relating to AI in dentistry were also referred. The search showed that the use of AI in dentistry, specifically in endodontics, has enormous promise. Although useful, AI has its disadvantages as well as the need for long-term studies. Conclusion: AI, consisting of a sequence of algorithms, work on a concept that mimics the human brain and thinking. AI in endodontics has been used widely in locating apical foramina, identifying periapical pathologies, diagnosis of vertical root fractures, evaluating the outcome of regenerative procedures and retreatments, and assessment of root morphologies and difficulties associated with canal preparations. Being a potential game changer and beginning something called a “fourth industrial revolution,” AI has what it takes to revolutionize endodontics with time.
... The frequent benefits of AI application in endodontics are mentioned as analyzing root canal anatomy, detecting root fractures, and periapical lesions, precise assessment of working length, predicting the viability of dental pulp stem cells, and predicting the success rate of retreatment procedures [101]. [105][106][107]. Therefore, the possible application of AI for periapical lesions detection in the future should be further examined [102]. ...
Statement of the Problem
In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation.
Purpose
Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages.
Materials and Method
For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence”, "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated.
Results
In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed.
Conclusion
As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
... Applicability concerns were found to be high for 53% of the studies regarding data selection but were low for most studies regarding the index test (79%) and reference standard (73%). [13] low/low low/low low/low low 3. [14] high/low low/low low/low low 4. [15] low/low low/high high/high low 5. [16] low/low low/low low/low low 6. [17] high/high low/high high/high low 7. [18] high/high low/low high/low low 8. [19] low/low low/high low/low low 9. [20] low/low low/low low/high low 10. [21] high/high low/low high/low low 11. ...
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
... This review has shown one great challenge with the current literature on AI in dentistry; the presence of computer-based papers without proper understanding of relevant input data from a clinical point of view. Also, there are several models which are not trained based on clinical rigorous data [49,50] (Table 3). Therefore the outputs of such models are unreliable regardless of the accuracy metrics reported [48]. ...
Objectives:
To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations.
Material and methods:
This review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features . The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays.
Results:
The initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis.The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1-3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias.
Conclusions:
AI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically.
... The frequent benefits of AI application in endodontics are mentioned as analyzing root canal anatomy, detecting root fractures, and periapical lesions, precise assessment of working length, predicting the viability of dental pulp stem cells, and predicting the success rate of retreatment procedures [101]. [105][106][107]. Therefore, the possible application of AI for periapical lesions detection in the future should be further examined [102]. ...
Statement of the Problem: In recent years, the use of Artificial Intelligence has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, this novel technology lacks frequent prominent elements which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation.
Purpose: Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages.
Materials and Method: For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence," "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated.
Results: In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, 3 controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed.
Conclusion: As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
Keywords: Artificial Intelligence, Dentistry, Machine learning, Deep learning, Diagnostic System
... These steps are followed for each experiment performed on the dataset for the classification using a machine and deep learning. [7]. the main pattern in dental disease detection is all the parameters should be addressed to classify the diseases [8] because, the disease type and severity is not common in all data, it was varied based on different patients [9]. ...
The rapid advancement of Machine Learning (ML) and Deep Learning (DL) technologies has revolutionized healthcare, particularly in the domains of disease prediction and diagnosis. This study provides a comprehensive review of ML and DL applications across sixteen diverse diseases, synthesizing findings from research conducted between 2015 and 2024. We explore these technologies’ methodologies, effectiveness, and clinical outcomes, highlighting their transformative potential in healthcare settings. Although ML and DL demonstrate remarkable accuracy and efficiency in disease prediction and diagnosis, challenges including quality of data, interpretability of models, and their integration into clinical workflows remain significant barriers. By evaluating advanced approaches and their outcomes, this review not only underscores the current capabilities of ML and DL but also identifies key areas for future research. Ultimately, this work aims to serve as a roadmap for advancing healthcare practices, enhancing clinical decision making, and strengthening patient outcomes through the effective and responsible implementation of AI-driven technologies.
Graphical Abstract
The severity of periodontitis can be analyzed by calculating the loss of alveolar crest (ALC) level and the level of bone loss between the tooth’s bone and the cemento-enamel junction (CEJ). However, dentists need to manually mark symptoms on periapical radiographs (PAs) to assess bone loss, a process that is both time-consuming and prone to errors. This study proposes the following new method that contributes to the evaluation of disease and reduces errors. Firstly, innovative periodontitis image enhancement methods are employed to improve PA image quality. Subsequently, single teeth can be accurately extracted from PA images by object detection with a maximum accuracy of 97.01%. An instance segmentation developed in this study accurately extracts regions of interest, enabling the generation of masks for tooth bone and tooth crown with accuracies of 93.48% and 96.95%. Finally, a novel detection algorithm is proposed to automatically mark the CEJ and ALC of symptomatic teeth, facilitating faster accurate assessment of bone loss severity by dentists. The PA image database used in this study, with the IRB number 02002030B0 provided by Chang Gung Medical Center, Taiwan, significantly reduces the time required for dental diagnosis and enhances healthcare quality through the techniques developed in this research.
Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.
Diabetes type 2 remains a pressing worldwide health subject, highlighting the need for advanced early detection methods. In this study, we performed a comprehensive analysis of current literature presented at conferences and journals, focusing on the effectiveness of machine learning techniques for the early detection of diabetes type 2. Our review included thorough examination of various papers, examining the methodologies, and assessing the accuracy of these methods. We diagnosed developments and patterns within the application of machine-learning algorithms for diabetes detection. Our study synthesizes these findings and proposes a complete framework utilizing present-day system-getting-to-know algorithms. Via rigorous comparative evaluation, we encouraged precise algorithms with demonstrated efficacy. We also delved into the combination of novel technologies, enhancing the accuracy and reliability of diabetes prediction methods. The proposed framework no longer only showcases promising accuracy quotes but also addresses the realistic elements of implementation, ensuring actual global effectiveness. Additionally, we explored the socio-financial effect of early diabetes detection and underscored the significance of timely interventions in reducing healthcare expenses and enhancing affected person outcomes. This review serves as a treasured resource for researchers, practitioners, and policymakers, offering insights into the ultra-modern advancements in device learning applications for diabetes type 2 early detection. By amalgamating cutting-edge technology with insightful analysis, our studies contribute to the continued efforts to combat diabetes and improve public fitness on a global scale.
Flood management in a large reservoir is a problem of optimization with multiple objectives. The procedure necessitates controlled releases to safeguard downstream residents, preserving reservoir storage to allow for future floods, and securing water for conservation purposes. The only way to satisfy all of the objectives is for a reservoir to run at maximum efficiency. There are a variety of optimization strategies available, ranging from classical techniques to meta-heuristic optimization techniques. In this paper, the application of meta-heuristic techniques for the operation of a large reservoir for approaching target storage with flood control and maintaining space for upcoming floods is presented. The paper is focused on the application of two different techniques, viz. the genetic algorithms (GA) and teaching–learning-based optimization (TLBO) for flood operation with a major objective of target storage achievement in flood conditions at the reservoir. The current study makes use of actual data from the Sardar Sarovar Reservoir Project on the River Narmada. The main priorities are meeting the target storage at the end of the flood wave, the dam safety, and power generation. The comparative analysis of the results shows that the TLBO outperforms another method.KeywordsFlood managementOptimizationReservoir operationStorageTeachingLearning based optimization
Cognitive radio systems have emerged as a pivotal enabler in 5G and upcoming 6G systems. Future generation wireless networks in order to improve upon the quality of service (QoS) parameters need to avail the channel state information. Off late, machine learning-based approaches are being used to analyze the channel state information (CSI) owing to the fact that the CSI is generally extremely complex in nature for real-time critical applications. In this proposed approach, a cognitive-OFDM system is designed under Gaussian noise conditions. The channel sensing information is applied to a gradient descent-based deep neural network (DNN) which is used for channel estimation. The deep learning-based approach design is applied with 70% data for training, 15% for testing, and 15% for validation, respectively. The channel estimation information is utilized for frequency domain equalization and is performed with and without addition of pilot bits. The performance of the proposed gradient descent-based deep learning approach is evaluated in terms of the training epochs, time, mean square error, and gradient. The truncation of the estimation is governed by the stabilization of error during training or the elapse of maximum number of iterations which is 1000 in this case. The mean square error for channel estimation and the bit error rate (BER) have been chosen as the primary performance metrics. It has been shown that the proposed system outperforms existing systems in terms of BER and MSE.KeywordsCognitive radio systemsChannel estimationChannel State Information (CSI)EpochsDeep learningMean Square Error (MSE)Bit Error Rate (BER)
Aim:
This comprehensive review is aimed at evaluating the diagnostic and prognostic accuracy of artificial intelligence in endodontic dentistry.
Introduction:
Artificial intelligence (AI) is a relatively new technology that has widespread use in dentistry. The AI technologies have primarily been used in dentistry to diagnose dental diseases, plan treatment, make clinical decisions, and predict the prognosis. AI models like convolutional neural networks (CNN) and artificial neural networks (ANN) have been used in endodontics to study root canal system anatomy, determine working length measurements, detect periapical lesions and root fractures, predict the success of retreatment procedures, and predict the viability of dental pulp stem cells. Methodology. The literature was searched in electronic databases such as Google Scholar, Medline, PubMed, Embase, Web of Science, and Scopus, published over the last four decades (January 1980 to September 15, 2021) by using keywords such as artificial intelligence, machine learning, deep learning, application, endodontics, and dentistry.
Results:
The preliminary search yielded 2560 articles relevant enough to the paper's purpose. A total of 88 articles met the eligibility criteria. The majority of research on AI application in endodontics has concentrated on tracing apical foramen, verifying the working length, projection of periapical pathologies, root morphologies, and retreatment predictions and discovering the vertical root fractures.
Conclusion:
In endodontics, AI displayed accuracy in terms of diagnostic and prognostic evaluations. The use of AI can help enhance the treatment plan, which in turn can lead to an increase in the success rate of endodontic treatment outcomes. The AI is used extensively in endodontics and could help in clinical applications, such as detecting root fractures, periapical pathologies, determining working length, tracing apical foramen, the morphology of root, and disease prediction.
Diabetes is a serious and progressive condition that is rapidly increasing in incidence and currently ranks third on the list of all causes of mortality throughout the globe. A key challenge for any nation, but particularly for one that is undergoing substantial change is the high diabetes prevalence rate. Research in the field of epidemiology has demonstrated that obesity and Type II diabetes are the result of a combination of genetic predisposition and lifestyle factors such as bad eating habits and a lack of physical exercise. This article presents machine learning and feature selection enabled framework for diabetes type 2 prediction. This article uses artificial neural network for classification and prediction of diabetes type 2 data. Input data used in experiment is gathered from Pima Indian Diabetes Dataset. Results are compared on the basis of certain parameters like- accuracy, sensitivity, specificity. Accuracy of artificial neural network is better for classification and prediction of type 2 diabetes.
The use of AI (artificial intelligence) for auxiliary diagnosis of medical images is a current development trend, where the automatic recognition of lesion location is a key problem in AI-assisted diagnosis of medical images. In the past, most of the studies on AI-assisted diagnosis of dental caries based on X-ray image primarily focused on the classification of abnormalities rather than the detection and localization of dental caries, which is not conducive to expanding the clinical application of this technique. Accordingly, in this work, an AI-assisted diagnosis method is developed that employs Faster-RCNN to predict the number and locations of caries lesions based on periapical films. To support the application of this diagnosis method, an open Web platform for detection of dental caries is constructed in the following way: First, clinical samples acquired by professional doctors are collected and labeled to build the sample space and label space. Then, a caries detection model is built through training and testing on samples. Finally, the contents of Web platform are set up, the layout is configured through HTML/CSS programming, and the model is deployed on Alibaba cloud server using the Flask framework. After the diagnosis platform is put into operation, doctors and patients can upload the medical images to be tested onto the website of platform, where test results can be obtained. These test results can help doctors diagnose the diseases with higher efficiency and precision.
In this work, we aim to develop and validate an AI-assisted method for identifying the history of root canal therapy by using periapical films. First, we propose a pre-processing method to extract the regions of interest (ROI) containing the root canals. Then, in order to improve the generalization ability, data augmentation is adopted to expand the dataset. Finally, three machine learning methods, including SIFT-SVM, CNN, and transfer learning are used. All the models are validated based on the receiving operating characteristic (ROC) curve analysis. The accuracies for the three machine learning methods are above 95%. The AUC, sensitivity, and specificity of the best model are also presented and analyzed.
Background and objective:
Datamining (DM) has, over the last decade, received increased attention in the medical domain and has been widely used to analyze medical datasets in order to extract useful knowledge and previously unknown patterns. However, historical medical data can often comprise inconsistent, noisy, imbalanced, missing and high dimensional data. These challenges lead to a serious bias in predictive modeling and reduce the performance of DM techniques. Data preprocessing is, therefore, an essential step in knowledge discovery as regards improving the quality of data and making it appropriate and suitable for DM techniques. The objective of this paper is to review the use of preprocessing techniques in clinical datasets.
Methods:
We performed a systematic map of studies regarding the application of data preprocessing to healthcare and published between January 2000 and December 2017. A search string was determined on the basis of the mapping questions and the PICO categories. The search string was then applied in digital databases covering the fields of computer science and medical informatics in order to identify relevant studies. The studies were initially selected by reading their titles, abstracts and keywords. Those that were selected at that stage were then reviewed using a set of inclusion and exclusion criteria in order to eliminate any that were not relevant. This process resulted in 126 primary studies.
Results:
Selected studies were analyzed and classified according to their publication years and channels, research type, empirical type and contribution type. The findings of this mapping study revealed that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade. A significant number of the selected studies used data reduction and cleaning preprocessing tasks. Moreover, the disciplines in which preprocessing have received most attention are: cardiology, endocrinology and oncology.
Conclusions:
Researchers should develop and implement standards for an effective integration of multiple medical data types. Moreover, we identified the need to perform literature reviews.
Clinical databases often comprise noisy, inconsistent, missing, imbalanced and high dimensional data. These challenges may reduce the performance of DM techniques. Data preprocessing is, therefore, essential step in order to use DM algorithms on these medical datasets as regards making it appropriate and suitable for mining. The objective is to carry out a systematic mapping study in order to review the use of preprocessing techniques in clinical datasets. As results, 110 papers published between January 2000 and March 2017 were, selected, analyzed and classified according to publication years and channels, research type and the preprocessing tasks used. This study shows that researchers have paid a considerable amount of attention to preprocessing in medical DM in last decade and a significant number of the selected studies used data reduction and cleaning preprocessing tasks.
This paper presents the comprehensive study of the various statistical measures and their application in digital image processing at root level. We have simulated the majority of statistical measures and reviewed their existing applications. Also we have explored and proposed their importance in some more research area of digital image processing. We have done their comparative analysis with the help of MATLAB simulation to ease the selection of statistical parameter for a specific image processing technique like image enhancement, denoising, restoration, edge detection etc.
Automated diagnosis of glaucoma disease is focused on the analysis of the retinal images to localize, perceive and evaluate the optic disc. Clinical decision support system (CDSS) is used for glaucoma classification in human eyes. This process depends mainly on the feature type that can be morphological or non-morphological. It is originated in the retinal image analysis technique that used color feature, texture features, extract structure, or contextual. This work proposes an empirical study on a narrative automated glaucoma diagnosis classification system based on both Grid Color Moment method as a feature vector to extract the color features (non-morphological) and neural network classifier. Consequently, these features are fed to the back propagation neural network (BPNN) classifier for automated diagnosis. The proposed system was tested using an open RIM-ONE database with accurate gold standards of the optic nerve head. This work classifies both normal and abnormal defected retina with glaucoma images. The experimental results achieved an accuracy of 87.47%. Thus, the proposed system can detect the early glaucoma stage with good accuracy.
Early prediction of breast density is clinically significant as there is an association between the risk of breast cancer development and breast density. In the present work, the performance of two computer aided diagnostic (CAD) systems has been compared for classification of breast tissue density. The work has been carried out on MIAS dataset with 322 mammographic images consisting of 106 fatty and 216 dense images. The ROIs have been selected from densest region (i.e., the center of each image, ignoring the pectoral muscle) of each mammogram. The total dataset consisted of 322 ROIs (106 fatty ROIs and 216 dense ROIs). Five statistical texture features namely, mean, standard deviation, entropy, kurtosis and skewness are evaluated from Laws’ texture energy images resulting from Laws’ masks of length 5, 7 and 9. The texture feature vectors computed from Laws’ masks of different lengths are then subjected to principal component analysis (PCA) for reduction in feature space dimensionality. The SVM and PNN classifiers are used for the classification task. It is observed that the highest classification accuracy of 92.5 % is achieved with first four principal components derived from texture features computed with Laws’ masks of length 7 by using PNN classifier and the highest classification accuracy of 94.4 % is achieved with first four principal components derived from texture features computed with Laws’ masks of length 5 by using SVM classifier. It can be concluded that the first four principal components derived from Laws’ texture energy images resulting from Laws’ masks of length 5 are sufficient to account for textural changes exhibited by fatty and dense mammograms. The promising results obtained by the proposed CAD design indicate its usefulness to assist radiologists for breast density classification.
In this paper, we review the current activity of image classification methodologies and techniques. Image classification is a complex process which depends upon various factors. Here, we discuss about the current techniques, problems as well as prospects of image classification. The main focus will be on advanced classification techniques which are used for improving classification accuracy. Additionally, some important issues relating to classification performance are also discussed.
According to recent researches, glaucoma, an optic nerve disease, is considered as one of the major causes which can lead to blindness. It has affected a huge number of people worldwide. Rise in intraocular pressure of the eye leads to the disease resulting in progressive and permanent visual loss. Texture of normal retinal image and glaucoma image is different. Here texture property of the total image has been extracted from both with and without glaucoma image. In this work, Haralick features have been used to distinguish between normal and glaucoma affected retina. Extracted features have been utilized to train the back propagation neural network. Classification of glaucoma affected eye is successfully achieved with an accuracy of 96%.
Automated and accurate classification of computed tomography (CT) images is an integral component of the analysis and interpretation of neuro imaging. In this paper, we present the wavelet-based statistical texture analysis method for the classification of brain tissues into normal, benign, malignant tumor of CT images. Comparative studies of texture analysis method are performed for the proposed texture analysis method and spatial gray level dependence matrix method (SGLDM). Our proposed system consists of five phases (i) image acquisition, (ii) discrete wavelet decomposition (DWT), (iii) feature extraction, (iv) feature selection, and (v) analysis of extracted texture features by classifier. A wavelet-based statistical texture feature set is derived from two level discrete wavelet transformed approximation (low frequency part of the image) sub image. Genetic algorithm (GA) and principal component analysis (PCA) are used to select the optimal texture features from the set of extracted features. The support vector machine (SVM) is employed as a classifier. The results of SVM for the texture analysis methods are evaluated using statistical analysis and receiver operating characteristic (ROC) analysis. The experimental results show that the proposed system is able to achieve higher classification accuracy effectiveness as measured by sensitivity and specificity.
Graphical Abstract
In this chapter, preprocessing techniques used in document images as an initial step in character recognition systems were presented. Future research aims at new applications such as online character recognition used in mobile devices, extraction of text from video images, extraction of information from security documents and processing of historical documents. The objective of such research is to guarantee the accuracy and security of information extraction in real time applications. Even though many methods and techniques have been developed for preprocessing there are still problems that are not solved completely and more investigations need to be carried out in order to provide solutions. Most of preprocessing techniques are application-specific and not all preprocessing techniques have to be applied to all applications. Each application may require different preprocessing techniques depending on the different factors that may affect the quality of its images, such as those introduced during the image acquisition stage. Image
To perform a systematic literature review and meta-analysis on the effectiveness of interventions used in the management of acute apical abscess in the permanent dentition.
Electronic databases were searched from their inception to March 2002. These searches, combined with manual searching, yielded 85 citations, of which 35 were relevant. Independent application of inclusion criteria by 3 reviewers yielded 8 eligible randomized controlled studies. Data on population, interventions, outcomes (reduction of pain or swelling or both, as reported by patients or clinicians) and methodological quality were determined by independent triplicate review. Disagreements were resolved by consensus.
All papers included in the meta-analysis compared an antibiotic with an active control, a placebo or no pharmacotherapy as an adjunct for patients who had received concomitant therapy (i.e., incision and drainage, endodontic therapy or extraction). The 8 trials were randomized; in 3 of these, the method of randomization was described and appropriate. Five studies were double-blinded, and 2 of these described the method of blinding. Four trials described withdrawals, but none included an intention-to-treat analysis. Six studies compared 2 antibiotics. For the outcomes "absence of infection"and "absence of pain" the pooled odds ratios (ORs) were not statistically significant; for the outcome "absence of pain and infection," 3 studies showed an equivalent treatment effect in both treatment and control groups. One open-label study (with a quality score of 2) showed a result favouring azithromycin over co-amoxiclav (OR 0.58, 95% confidence interval 0.35-0.96). Two studies compared adjunctive antibiotic therapy with placebo; no benefit to patients was demonstrated with this intervention.
In the management of localized acute apical abscess in the permanent dentition, the abscess should be drained through a pulpectomy or incision and drainage. This analysis indicated that antibiotics are of no additional benefit. In the event of systemic complications (e.g., fever, lymphadenopathy or cellulitis), or for an immunocompromised patient, antibiotics may be prescribed in addition to drainage of the tooth.
While endodontic (dentoalveolar) abscesses can cause significant morbidity, in susceptible individuals they can pose life-threatening problems. This paper provides an overview of the more serious sequelae of endodontic abscesses, and provides examples of ‘high risk’ situations in practice in which these serious complications are more likely to occur.
Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored. Povzetek: Podan je pregled metod strojnega učenja.
Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image. One of the most popular methods is wiener filter. In this work four types of noise (Gaussian noise , Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter . Further results have been compared for all noises.
Family physicians commonly encounter patients with dental infections, such as dental caries and periodontal disease. Dental caries is caused by bacteria that destroy the enamel and dentin; it can be detected by an oral examination that shows stained pits or fissures on the tooth surface. Use of fluoride is the most effective prevention measure for dental caries. Untreated caries may progress to pulpitis and, eventually, to necrosis of the pulp. In irreversible pulpitis, the tooth dies and the patient may have a localized abscess that can spread to surrounding tissue. Periodontal infections are caused by bacteria in the subgingival dental plaque. In gingivitis, the inflamed gums bleed easily with brushing or flossing; the condition can be controlled with good oral hygiene. Periodontitis is characterized by a loss of supportive bone structure caused by chronic gingivitis; it is also associated with some systemic diseases. Localized periodontitis is treated with mechanical debridement and good oral hygiene, whereas generalized periodontitis requires adjunct antibiotic therapy. Pericoronitis results when food particles become trapped under the gum of an impacted tooth. This condition can be controlled by removal of food debris and good oral hygiene. For patients in whom dental infections are disseminated and have invaded the deeper oral spaces, antibiotic treatment should be initiated at the time of referral.
Noise Reduction in Images using Enhanced Average Filter
- H Prateek Singh
- A Nigam
- A Kumar
- A Gautam
- N Bhardwaj
- Singh
Classification Of Iris Plant Using Feedforward Neural Network
- S Vyas
- D Upadhyay
Detection and Classification of Brain Tumors
- N V Chavan
- B D Jadhav
Comparative study of data mining classification methods in cardiovascular disease prediction 1
- Kumari Milan
- Sunila Godara
Cohen's pathways of the pulp.
- K Hargreaves
- S Cohen
- L Berman
Survey on Image Enhancement Techniques for DigitalImages
- M Kaur Khehra
- M Devgun