Identification of an Alternate Maxillary Apical Base Landmark from Pre-existing Substitutions

Patel, Kunal; Kulkarni, Narayan; Singh, Varun Pratap; Parikh, Kartik

doi:10.5455/aim.2014.22.347-349

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…A-point is a landmark located on the curve of the premaxilla and is often influenced by the head position which makes tracing difficult. It has been discussed in preceding studies that A-point is one of the most common landmarks suffering from errors in identification [29,32]. Articulare, the intersection of the external dorsal contour of the mandibular condyle and the temporal bone, is an example of the cephalostat affecting the performance.…”

Section: Discussionmentioning

confidence: 99%

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

et al. 2020

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Background Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.

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Section: Discussionmentioning

confidence: 99%

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

et al. 2020

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“…Point A has been associated with varying magnitudes of positioning error in previous studies. 20,21 This is attributable to the nature of point A, which has been consistently shown to be associated with high error values. Perillo et al 15 reported a high level of variation in the localization of point A and demonstrated that the linear measurement from nasion to point A had the lowest inter-examiner reliability.…”

Section: Discussionmentioning

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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“…Among the hard tissue landmarks, MREs for few landmarks, including the landmark 17 (posterior nasal spine), 23 (lower incisor), 30 (articular), 19 (pterygomaxillary ssure) and 21 (upper incisor), are especially high even for the best performing method [30]. The reason why these landmarks are di cult to be identi ed precisely may be due to image complexity caused by the difference of X-ray projection between the left and right side of the head structure [31].…”

Section: Discussionmentioning

confidence: 99%

“…The reason why these landmarks are di cult to be identi ed precisely may be due to image complexity caused by the difference of X-ray projection between the left and right side of the head structure [31]. As for index of upper and lower incisors, open root apexes and malocclusion with dental crowding occasionally exist in patients with malocclusion, thus diminishing the accuracy of AI detection [30]. When considering the reason that, condyle, gonion and articular could be marked on two mandibular angle contours due to the limitation of the 2D lateral cephalogram.…”

Section: Discussionmentioning

confidence: 99%

Is automatic cephalometric software using artificial intelligence better than orthodontist expert in landmark identification

Cheng

Ungvijanpunya

et al. 2023

Preprint

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Background: To evaluate the techniques used for automatic digitization of cephalograms, highlighting the strengths and weaknesses of each one and review the percentage of success in localising each cephalometric point.、 Methods: Lateral cephalograms were digitized and traced by three calibrated senior orthodontic residents with or without artificial intelligence (AI) assistance. The same radiographs of 43 patients were uploaded to AI-based machine learning program MyOrthoX, Angelalign and Digident. Image J was used to extract x- and y-coordinates for 32 cephalometric points: 11 soft tissue landmarks and 21 hard tissue landmarks. The mean radical errors (MRE) were assessed radical to the threshold of 1.0mm,1.5mm, and 2 mm to compare the successful detection rate (SDR). One-way ANOVA analysis at significance level of P < .05 was used to compare MRE and SDR. The SPSS (IBM-vs. 27.0) and PRISM (GraphPad-vs.8.0.2) software were used for the data analysis. Results: Experimental results showed that three methods were able to achieve detection rates greater than 85% using the 2 mm precision threshold, which is the acceptable range in clinical practice. The Angelalign group even achieved a detection rate greater than 78.08% using the 1.0 mm threshold. A marked difference of time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark. Conclusions: AI assistance may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and in research settings.

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Identification of an Alternate Maxillary Apical Base Landmark from Pre-existing Substitutions

Cited by 7 publications

References 8 publications

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks

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

Is automatic cephalometric software using artificial intelligence better than orthodontist expert in landmark identification

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