Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Subramanian, Aravind Kumar; Chen, Yong; Almalki, Abdullah; Sivamurthy, Gautham; Kafle, Dashrath

doi:10.1155/2022/1880113

Cited by 44 publications

(34 citation statements)

References 59 publications

(64 reference statements)

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“…Ainda a respeito do CEFBOT (RadioMemory), com exceção do terceiro fator, todos os outros fatores, apresentaram valores considerados "quase perfeito" (Landis & Koch, 1977). No que tange a confiabilidade do WebCeph TM Na Ortodontia, as principais considerações para um tratamento ortodôntico eficaz são: um diagnóstico preciso, um excepcional plano de tratamento e um bom prognóstico (Subramanian et al, 2022). Dado que muitos tratamentos são irreversíveis ou causam efeitos secundários permanentes, tais como: reabsorção radicular e recessão gengival.…”

Section: Resultsunclassified

Avaliação da performance de dois softwares com inteligência artificial por meio das medidas geradas pela análise de Mcnamara em telerradiografia cefalométrica lateral

Souza

Silva

Filho

et al. 2022

RSD

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O objetivo do trabalho foi comparar a performance de dois softwares com IA em telerradiografia cefalométrica lateral, por meio da avaliação da reprodutibilidade e confiabilidade das medidas lineares e angulares da análise de McNamara. Foram marcadas 30 telerradiografias cefalométricas por meio do método digital pelo examinador no Radiocef (RadioMemory). Posteriormente, a amostra foi marcada por meio da IA dos softwares CEFBOT (RadioMemory) e WebCephTM (AssembleCircle), para avaliação da reprodutibilidade e confiabilidade, em relação ao examinador e os softwares em questão. Para calibrar o examinador e avaliar a confiabilidade das marcações do examinador, CEFBOT, e WebCephTM utilizou o Coeficiente de Correlação Intraclasse (ICC), bem como, o teste ANOVA e pós teste de Tukey avaliou a reprodutibilidade dos softwares, por meio dos pontos cefalométricos que compõem a análise de McNamara. O ICC médio do examinador, CEFBOT e do WebCeph foram 0.960, 0.940 e 0.954, respectivamente, indicando concordância quase perfeita. Ao comparar CEFBOT com examinador, observou-se diferença estatística (p<0.01) apenas na medida A-N perpendicular. Quanto ao WebCephTM, ao comparar com o examinador houve diferença significativa entre os fatores dois ao seis e o dez. E comparado ao CEFBOT, houve divergência nos mesmos fatores somado ao fator onze. Além disso, o WebCephTM não identificou as medidas Nfa-Nfp e Bfa-Bfp. O CEFBOT apresentou reprodutibilidade e confiabilidade na identificação dos pontos cefalométricos determinados pela análise de McNamara, mas necessitando de supervisão humana. O WebCeph apresentou concordância quase perfeita nas marcações, porém seis medidas apresentaram-se diferentes do examinador e duas não foram realizadas pela aplicação.

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Section: Resultsunclassified

Avaliação da performance de dois softwares com inteligência artificial por meio das medidas geradas pela análise de Mcnamara em telerradiografia cefalométrica lateral

Souza

Silva

Filho

et al. 2022

RSD

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“…Owing to the increased capacity of computing power to analyze and extract features from a large amount of data in a short amount of time, various AI-based cephalometric analysis algorithms have been developed, with mean error values ranging from 1.1 to 4.09 mm. [5][6][7] Despite advancements in automated cephalometric analysis technologies, however, there is limited evidence regarding the clinical validity of cephalometric analyses based on automatically detected landmarks. Many studies have yielded large ranges of error associated with cephalometric landmarks and measurements.…”

Section: Backg Rou N Dmentioning

confidence: 99%

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks

Kang

Kim

et al. 2023

Orthod Craniofacial Res

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BackgroundThis study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements.MethodsIn total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi‐centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x‐ and y‐axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed.ResultsThe mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI‐based and human localization were observed for all cephalometric variables except SNA, pog‐Nperp, facial angle, SN‐GoGn, FMA, Bjork sum, U1‐SN, U1‐FH, IMPA, L1‐NB (angular) and interincisal angle.ConclusionsThe errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.

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“…OneCeph, Hyderabad, India; CellmatIQ, Hamburg, Germany; WebCeph, Republic of Korea; AudaxCeph, Ljubljana, Slovenia) more recent studies have focused on evaluating their accuracy [ 15 , 29 – 33 ]. While the benefits of artificial intelligence in recognizing cephalometric landmarks have been acknowledged [ 34 , 35 ], the need for further research regarding its accuracy in different clinical settings was recognized [ 36 – 38 ]. Previous studies tested the frameworks only on radiographs of patients with permanent dentition [ 24 , 30 , 33 ] or did not mention these characteristic of the datasets at all [ 15 – 17 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network

Popova

Stocker

Khazaei³

et al. 2023

BMC Oral Health

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Background One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. Methods For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. Results The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. Conclusions The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.

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Cephalometric Analysis in Orthodontics Using Artificial Intelligence—A Comprehensive Review

Cited by 44 publications

References 59 publications

Avaliação da performance de dois softwares com inteligência artificial por meio das medidas geradas pela análise de Mcnamara em telerradiografia cefalométrica lateral

Avaliação da performance de dois softwares com inteligência artificial por meio das medidas geradas pela análise de Mcnamara em telerradiografia cefalométrica lateral

Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks

Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network

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