A Fuzzy Inference System for Skeletal Age Assessment in Living Individual

Mansourvar, Marjan; Asemi, Asefeh; Raj, Ram Gopal; Kareem, Sameem Abdul; Antony, Chermaine Deepa; Idris, Norisma; Baba, Mohd Sapiyan

doi:10.1007/s40815-016-0232-7

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…The studies on the use of artificial intelligence in hand‐wrist age determination are much more advanced in automation 29‐33 . However, according to our knowledge, studies on cephalometry and cervical vertebra are very limited.…”

Section: Discussionmentioning

confidence: 99%

“…Computer-aided systems have been developed to minimize the interobserver differences that may arise in determining growth and The studies on the use of artificial intelligence in hand-wrist age determination are much more advanced in automation. [29][30][31][32][33] However, according to our knowledge, studies on cephalometry and cervical vertebra are very limited. The different algorithm's success was compared previously.…”

Section: Discussionmentioning

confidence: 99%

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Determination of growth and development periods in orthodontics with artificial neural network

Kök

İzgi²,

Acilar

2020

Orthod Craniofacial Res

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Background We aimed to determine the growth‐development periods and gender from the cervical vertebrae using the artificial neural network (ANN). Setting and Sample Population The cephalometric and hand‐wrist radiographs obtained from 419 patients aged between 8 and 17 years were included in our study. Materials and Methods Our retrospective study consisted of 419 patients’ cephalometric and hand‐wrist radiographs. The cephalometric radiographs were divided into six cervical vertebrae stages (CVS). Correlations were evaluated between hand‐wrist maturation level, CVS, and ages. Twenty‐seven vertebral reference points are marked on the cephalometric radiograph, and 32 linear measurements were taken. With the combination of these measurements, 24 different data sets were formed to train ANN. Thus, 24 different ANN models for the determination of the growth‐development periods were obtained. According to the results, seven ANN models that have a high success level and clinically applicable were selected. Also, an ANN model was done by all measurements and age for the determination of gender from cervical vertebrae. Results Significantly positive correlations between hand‐wrist maturation level, CVS and ages were detected. The ANN‐7 model (32 linear measurements and age) accuracy value was found 0.9427. The highest model accuracy, 0.8687, with the least linear measurements, was obtained by drawing 13 linear measurements, using vertical measurements and indents. Gender was determined using ANN (0.8950) on cervical vertebrae data. Conclusion The growth‐development periods and gender were determined from the cervical vertebrae by using ANN. The success of the ANN algorithm has been satisfactory. Further studies are needed for a fully automatic decision support system.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Determination of growth and development periods in orthodontics with artificial neural network

Kök

İzgi²,

Acilar

2020

Orthod Craniofacial Res

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“…In particular, this method takes into account a set of specific ROIs divided into epiphysis/metaphysis ROIs and carpal ROIs. Very recently, ConvNets have shown to be successful for bone age estimation and there are some published applications based on the GP method [84][85][86][87][88] and on the TW2 method [59,[89][90][91]. Table 2 includes an overview of the main AI-based approaches employed in the literature for age estimation.…”

Section: Age Estimation From Skeletal Structuresmentioning

confidence: 99%

“…In [89] the proposal is based on a neural network and a fuzzy filter output. In [90], a fuzzy inference system is used for age assessment. More recently, Spampinato et al [91] proposed and tested several DL approaches.…”

Section: Age Estimation From Skeletal Structuresmentioning

confidence: 99%

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

et al. 2020

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This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.

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“…CNNs have been applied to image recognition tasks since the late 1980s [14,15]. Image classification, which can be defined as categorizing images into one of several predefined classes, is a fundamental problem in computer vision CNNs are a type of feedforward neural network in machine learning.…”

Section: Deep Convolution Neural Networkmentioning

confidence: 99%