A comprehensive artificial intelligence framework for dental diagnosis and charting

Background: The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods: The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results: The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions: The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention

Classification and Numbering of Dental Radiographs for an Automated Human Identification System

 Dental based human identification is commonly used in forensic. In a case of large scale investigation, manual identification needs a large amount of time. In this paper, we developed an automated human identification system based on dental radiographs. The system developed has two main stages. The first stage is to arrange a database consisting of labeled dental radiographs. The second stage is the searching process in the database in order to retrieve the identification result. Both stages use a number of image processing techniques, classification methods, and a numbering system in order to generate dental radiograph’s features and patterns. The first technique is preprocessing which includes image enhancement and binarization, single tooth extraction, and feature extraction. Next, we performed dental classification process which aims to classify the extracted tooth into molar or premolar using the binary support vector machine method. After that, a numbering process is executed in accordance with molar and premolar pattern obtained in the previous process. Our experiments using 16 dental radiographs that consist of 6 bitewing radiographs and 10 panoramic radiographs, 119 teeth objects in total, has shown good performance of classification. The accuracy value of dental pattern classification and dental numbering system are 91.6 % and 81.5% respectively

Novel Techniques for Automated Dental Identification

Automated dental identification is one of the best candidates for postmortem identification. With the large number of victims encountered in mass disasters, automating the process of postmortem identification is receiving an increased attention. This dissertation introduces new approaches for different stages of Automated Dental Identification system: These stages include segmentations, classification, labeling, and matching:;We modified the seam carving technique to adapt the problem of segmenting dental image records into individual teeth. We propose a two-stage teeth segmentation approach for segmenting the dental images. In the first stage, the teeth images are preprocessed by a two-step thresholding technique, which starts with an iterative thresholding followed by an adaptive thresholding to binarize the teeth images. In the second stage, we adapt the seam carving technique on the binary images, using both horizontal and vertical seams, to separate each individual tooth. We have obtained an optimality rate of 54.02% for the bitewing type images, which is superior to all existing fully automated dental segmentation algorithms in the literature, and a failure rate of 1.05%. For the periapical type images, we have obtained a high optimality rate of 58.13% and a low failure rate of 0.74 which also surpasses the performance of existing techniques. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). A dental chart is a key to avoiding illogical comparisons that inefficiently consume the limited computational resources, and may mislead decision-making. We tackle this composite problem using a two-stage approach. The first stage, utilizes low computational-cost, appearance-based features, using Orthogonal Locality Preserving Projections (OLPP) for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and hence to assign each tooth a number corresponding to its location in the dental chart, even in the presence of a missed tooth. The experimental results of teeth classification show that on a large dataset of bitewing and periapical films, the proposed approach achieves overall classification accuracy of 77% and teeth class validation enhances the overall teeth classification accuracy to 87% which is slightly better than the performance obtained from previous methods based on EigenTeeth the performance of which is 75% and 86%, respectively.;We present a new technique that searches the dental database to find a candidate list. We use dental records of the FBI\u27s Criminal Justice Service (CJIC) ADIS database, that contains 104 records (about 500 bitewing and periapical films) involving more than 2000 teeth, 47 Antemortem (AM) records and 57 Postmortem (PM) records with 20 matched records.;The proposed approach consists of two main stages, the first stage is to preprocess the dental records (segmentation and teeth labeling classification) in order to get a reliable, appearance-based, low computational-cost feature. In the second stage, we developed a technique based on LaplacianTeeth using OLPP algorithm to produce a candidate list. The proposed technique can correctly retrieve the dental records 65% in the 5 top ranks while the method based on EigenTeeth remains at 60%. The proposed approach takes about 0.17 seconds to make record to record comparison while the other method based on EigenTeeth takes about 0.09 seconds.;Finally, we address the teeth matching problem by presenting a new technique for dental record retrieval. The technique is based on the matching of the Scale Invariant feature Transform (SIFT) descriptors guided by the teeth contour between the subject and reference dental records. Our fundamental objective is to accomplish a relatively short match list, with a high probability of having the correct match reference. The proposed technique correctly retrieves the dental records with performance rates of 35% and 75% in the 1 and 5 top ranks respectively, and takes only an average time of 4.18 minutes to retrieve a match list. This compares favorably with the existing technique shape-based (edge direction histogram) method which has the performance rates of 29% and 46% in the 1 and 5 top ranks respectively.;In summary, the proposed ADIS system accurately retrieves the dental record with an overall rate of 80% in top 5 ranks when a candidate list of 20 is used (from potential match search) whereas a candidate size of 10 yields an overall rate of 84% in top 5 ranks and takes only a few minutes to search the database, which compares favorably against most of the existing methods in the literature, when both accuracy and computational complexity are considered

Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

Classification of dental x-ray images

Forensic dentistry is concerned with identifying people based on their dental records. Forensic specialists have a large number of cases to investigate and hence, it has become important to automate forensic identification systems. The radiographs acquired after a person is deceased are called the Post-mortem (PM) radiographs, and the radiographs acquired while the person is alive are called the Ante-mortem (AM) radiographs. Dental biometrics automatically analyzes dental radiographs to identify the deceased individuals. While, ante mortem (AM) identification is usually possible through comparison of many biometric identifiers, postmortem (PM) identification is impossible using behavioral biometrics (e.g. speech, gait). Moreover, under severe circumstances, such as those encountered in mass disasters (e.g. airplane crashes and natural disasters such as Tsunami) most physiological biometrics may not be employed for identification, because of the decay of soft tissues of the body to unidentifiable states. Under such circumstances, the best candidates for postmortem biometric identification are the dental features because of their survivability and diversity.;In my work, I present two different techniques to classify periapical images as maxilla (upper jaw) or mandible (lower jaw) images and we show a third technique to classify dental bitewing images as horizontally flipped/rotated or horizontally un-flipped/un-rotated. In our first technique I present an algorithm to classify whether a given dental periapical image is of a maxilla (upper jaw) or a mandible (lower jaw) using texture analysis of the jaw bone. While the bone analysis method is manual, in our second technique, I propose an automated approach for the identification of dental periapical images using the crown curve detection Algorithm. The third proposed algorithm works in an automated manner for a large number of database comprised of dental bitewing images. Each dental bitewing image in the data base can be classified as a horizontally flipped or un-flipped image in a time efficient manner

Dental Biometrics: Human Identification Using Dental Radiograph

Biometric is the science and innovation of measuring and analyzing biological information.In information technology, biometric refers to advancements that measures and analyzes human body attributes,for example,DNA, eye retinas, fingerprints and irises,face pattern,voice patterns,and hand geometry estimations,for identification purposes.The primary motivation behind scientific dentistry is to distinguish expired people,for whom different method for recognizable proof(e.g.,unique finger impression,face,and so on.)are not accessible.Dental elements survives most of the PM events which may disrupt or change other body tissues,e.g. casualties of motor vehicles mishaps,fierce violations,and work place accident,whose bodies could be deformed to such a degree,that identification even by a family member is neither desirable nor reliable.Dental Biometric utilises dental radiographs to distinguish casualties.The radiographs procured after the casualty's demise are called post-mortem radiograph and the radiograph obtained when the casualty was alive is called ante-mortem radiograph.The objective of dental biometric is to match the unidentified individual's post-mortem radiograph against a database of labelled antemortem radiograph.This thesis proposes a novel method for the contour extraction from dental radiographs.The proposed algorithm of Active Contour Model or the Snake model is used for this purpose. A correctly detected contour is essential for proper feature extraction.This thesis only works on the contour detection.The method has been tested on some radiographs images and is found to produce desired output.However,the input radiograph image may be of low quality,may suffer a clear separation between two adjacent teeth.In that case the method will not be able to produce a satisfactory result.There is a need of pre-processing(e.g. contrast enhancement) before the active contour detection model can be applie

PARAMETER SIGMOID TRANSFORM CONTRAST ENHANCEMENT FOR DENTAL RADIOGRAPH CLASSIFICATION AND NUMBERING SYSTEM

Dental record is a method that is used to identify a person. The identification process needs a system that could recognize each individual tooth automatically. The similar intensity level between the teeth and the gums is one of the main problem in tooth identification in a dental radiograph. The intensity problem could influence the segmentation process of the system. In this paper, we proposed a new contrast enhancement by using parameter sigmoid transform to increase the segmentation accuracy. There are five main steps in this method. The first step is to fix the contrast of the image with the proposed method. The next steps are to segment the teeth using horizontal and vertical integral projection, feature extraction, and classification using Support Vector Machine (SVM). The last step is teeth numbering. The experiment result using the proposed method have an accuracy rate of 88% for classification and 73% for teeth numbering