Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques

Avuçlu, Emre; Başçiftçi, Fatih

doi:10.1016/j.chaos.2019.01.023

Cited by 22 publications

(6 citation statements)

References 24 publications

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“…44 Avuçlu E et al (2019) used 1315 dental x-ray images for the identification of age and gender based on the tooth morphology using a CNN called multilayer perceptron neural network (MLPNN). 32 This yielded a 100% accurate correct classification of the dental radiographic images which could be used in the identification of individuals. CT skull and hand-wrist radiographs have predicted the age of an individual with utmost accuracy.…”

Section: Oral Medicinementioning

confidence: 98%

“…Several deep learning systems have been employed for this feat such as U-Net (CNN), MLPNN (Mutli layer perceptron neural network (ANN)), AlexNet (CNN), DetectNet (CNN), VGG16 (Visual geometry group also called Oxford Net with 16 layer depth(CNN)), ResNet50(Residual neural network(ANN)), Inception-v3 (CNN), EfficientNet-B0 (CNN), InceptionResNet-v2 (CNN), Xception (CNN), and MobileNet (CNN). 31,32 Various studies have hypothesized the potential of AI to assist in the diagnosis of oral lesions. A deep learning network, EfficientNet-B0 has been trained to classify oral lesions into benign and malignant using real time clinical images.…”

Section: Intelligent Systems In Dentistrymentioning

confidence: 99%

“… Classification of malignant and benign lesion 31  Correlation between tobacco, alcohol abuse and development of oral premalignant lesion 33  Identification and classification of facial pain 34  Detection of facial nerve paralysis 35  Predicting xerostomia post radiotherapy 36  Detection of TMJ disc perforation 38 Oral Radiology  Classification of dental radiographs 39  Tooth identification from radiographs 40  Diagnosis of Sjogren's syndrome 42,43 Forensic Odontology  Identification of age and gender by tooth morphology 32  Identification of age from hand-wrist radiograph 45,46 Oral Surgery  Diagnosis of maxillary sinusitis 47  Identification of supernumerary teeth 49  Prediction of swelling following extraction of third molar 51 Oral Cancer  Early diagnosis of oral cancer 53  Prediction of oral cancer survival rates 55 Prosthodontics  Identification of prosthesis and restorations 57  Classification of implant systems based on radiographs 58 Orthodontics  To decide the need for extraction in the treatment plan 59,60  Identification of various orthodontic problems such as overbite, crossbite,etc 61  Identification of radiographic landmarks 63 Endodontics  Diagnosis of accessory root canal 64  Identification of apical constriction 65  Identification of early dental carious lesions 67 Periodontics  Prediction of oral malodour 68  Development of smart tooth brush 69,70  Prediction of periodontal bone loss 71 Artificial intelligence has been employed in various aspect of oral radiology. The large pools of dental radiographs often gets accumulated in the clinical setting, which can be efficiently classified in a retrospective pattern.…”

Section: Oral Medicinementioning

confidence: 99%

See 2 more Smart Citations

Intelligent Systems for Precision Dental Diagnosis and Treatment Planning – A Review

KRİSHNAN

Vinayachandran

2022

Cumhuriyet Dental Journal

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Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.

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Section: Oral Medicinementioning

confidence: 98%

Section: Intelligent Systems In Dentistrymentioning

confidence: 99%

Section: Oral Medicinementioning

confidence: 99%

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Intelligent Systems for Precision Dental Diagnosis and Treatment Planning – A Review

KRİSHNAN

Vinayachandran

2022

Cumhuriyet Dental Journal

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“…Segmentation of teeth is usually a necessary step in the analysis of dental images such as for lesion detection [5], age or gender determination [6] and human identification [7]. Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in oral medicine.…”

Section: Introductionmentioning

confidence: 99%

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

HELLİ

Hamamcı

2022

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Automatic teeth segmentation in panoramic x-ray images is an important research subject of the image analysis in dentistry. In this study, we propose a post-processing stage to obtain a segmentation map in which the objects in the image are separated, and apply this technique to tooth instance segmentation with U-Net network. The post-processing consists of grayscale morphological and filtering operations, which are applied to the sigmoid output of the network before binarization. A dice overlap score of 95.4±0.3% is obtained in overall teeth segmentation. The proposed post-processing stages reduce the mean error of tooth count to 6.15%, whereas the error without post-processing is 26.81%. The performances of both segmentation and tooth counting are the highest in the literature, to our knowledge. Moreover, this is achieved by using a relatively small training dataset, which consists of 105 images. Although the aim in this study is to segment tooth instances, the presented method is applicable to similar problems in other domains, such as separating the cell instances.

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“…Image control system was designed with image processing techniques [18][19]. Emre et al used image processing and artificial intelligence techniques to determine age and gender from dental images [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Kenar Bulma Algoritmaları Kullanarak Diş Röntgen Görüntülerinden Yaş ve Cinsiyet Belirleme

Başçiftçi¹,

Avuçlu²

2020

El-Cezeri Fen Ve Mühendislik Dergisi

Self Cite

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Age determination in forensic medicine is a very important issue in terms of criminal and law. In case of any disaster or unlawfulness, identification may be required. In such cases, the competent authorities ask the forensic medicine institution for determining the age. Forensic medicines must make the most accurate process of determining the age. In this study, a database was created manually with a total of 1313 teeth images to determine age and gender. Images in this database are pre-processed with image processing techniques. Numerical data were obtained according to the tanimoto similarity rates of images. This numerical data is saved in the XML file. Comparing is done with the data on this XML file. In addition to the age determination process, gender determination process was also carried out. Age determination was performed with + -0 error. The application was developed in C # programming language.

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Novel approaches to determine age and gender from dental x-ray images by using multiplayer perceptron neural networks and image processing techniques

Cited by 22 publications

References 24 publications

Intelligent Systems for Precision Dental Diagnosis and Treatment Planning – A Review

Intelligent Systems for Precision Dental Diagnosis and Treatment Planning – A Review

Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing

Kenar Bulma Algoritmaları Kullanarak Diş Röntgen Görüntülerinden Yaş ve Cinsiyet Belirleme

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