Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees

Suhail, Sameera; Harris, Kayla; Sinha, Gaurav; Schmidt, Maayan; Durgekar, Sujala Ganapati; Mehta, Shivam; Upadhyay, Madhur

doi:10.3390/bioengineering9110617

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…There is an AI functionality that determines the quality of 2D cephalometric X-rays, which could eliminate lower-quality X-rays from being further evaluated due to a possible distortion of the analysis [56]. On top of that, machine learning has found use in both lateral and 3D cephalogram analysis to provide ever-improving quality in landmark localisation [57,58].…”

Section: Artificial Intelligence Tools and Datasetsmentioning

confidence: 99%

“…Nowadays, the question is not whether CBCT scans are accurate, but how automated processes can aid professionals in landmark detection, skeletal classification, scan analysis and CBCT data management [57,58,60,61]. Based on current research, it has been concluded that AI can be of great use in assessing mandibular shape asymmetry as well as in the screening of upper airways to measure multiple parameters [62,63].…”

Section: Artificial Intelligence Tools and Datasetsmentioning

confidence: 99%

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AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

Tomášik,

Zsoldos,

Oravcová

et al. 2024

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In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices.

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Section: Artificial Intelligence Tools and Datasetsmentioning

confidence: 99%

Section: Artificial Intelligence Tools and Datasetsmentioning

confidence: 99%

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

Tomášik,

Zsoldos,

Oravcová

et al. 2024

View full text Add to dashboard Cite

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Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model’s explainability

S. Murthy

et al. 2023

Dentomaxillofacial Radiology

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Objectives The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. Methods We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability. Results The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others. Conclusions The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.

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Assessing the Co-relation between Mandibular Flare and Thickness of Lingual Cortex in Relation to the Third Molars: A Retrospective Cross-sectional Study

Babu,

Srnivasa Prasad,

Menon

et al. 2024

Journal of Pharmacy and Bioallied Sciences

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Aim: To assess the co-relation between mandibular flare and thickness of lingual cortex in relation to the third molars. Materials and Methods: Retrospectively obtained computed tomography (CT) data of 26 patients was used after classifying them into respective skeletal malocclusion groups (classes I, II, and III). Thickness of lingual cortex was measured at crestal, middle, and apical levels in mandibular third molar region. Two angular and two linear measurements were used to measure mandibular flare. Angular measurements included the angle between condylion (Co) and menton (Me), and between gonion (Go) and menton (Me). Linear measurements included bigonial and bicondylion widths. Results: The two angular measurements did not differ significantly among the three skeletal malocclusion groups. Contrastingly, bicondylion width differed significantly among the three groups. Class II group showed no significant correlation between mandibular flare and lingual cortical thickness. Class III group demonstrated a significant negative correlation of linear and angular measurements with cortical bone thickness. Bicondylion width was significantly more in Class III group than in other skeletal groups, which proved an increased mandibular flare in patients with Class III malocclusion. Conclusion: Increase in mandibular flare was associated with decreased thickness of lingual cortical bone.

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Learning Cephalometric Landmarks for Diagnostic Features Using Regression Trees

Cited by 3 publications

References 28 publications

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model’s explainability

Assessing the Co-relation between Mandibular Flare and Thickness of Lingual Cortex in Relation to the Third Molars: A Retrospective Cross-sectional Study

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