Beata Kawala

Background: Cephalometric reference values describe incisor orientation but do not establish whether the root movement required to achieve them is appropriate for an individual alveolar housing. Objectives: This study aimed to propose a structural compatibility framework in which cephalometric normalization is a conditional treatment objective for permanent maxillary and mandibular incisors. Materials and Methods: A narrative conceptual analysis integrated published anatomical, cephalometric, and biomechanical evidence. The framework combines intended movement with root orientation, root position, alveolar dimensions, cortical clearance, and maxillary root–incisive canal relationships. No patient-level measurements or clinical validation were performed. Results: The conceptual synthesis distinguishes selecting an angular target from assessing the root trajectory needed to reach it. Axis discordance (Δθ) is retained as a candidate orientation descriptor, not an independent measure of biological risk. Existing anatomical measurements are organized to identify potential conflicts between angular correction and local structural constraints. Conclusions: Achieving a cephalometric reference value should not, by itself, be interpreted as biological improvement. The proposed framework makes the reconciliation of angular objectives, root trajectory, and tissue preservation explicit. Measurement reliability, added value over existing assessment, and clinical benefit require separate evaluation.

Background/Objectives: Cephalometric analysis remains the principal tool for diagnosing sagittal jaw discrepancies, and its reproducibility depends on the precision with which landmarks are identified along both coordinate axes. This study is a secondary, class-stratified analysis of a cephalometric dataset whose pooled analysis has been published previously. Its aim was to establish whether inter-rater agreement for 12 landmarks and for six sagittal measurements (ANB, Wits, Tau, Yen, Sar and W) differs between skeletal Classes I, II and III. Methods: Twenty-four lateral cephalograms (n = 8 per skeletal class) were assessed twice, seven days apart, by 15 orthodontists trained in a 5-h calibration course. No new measurements were collected for the present work. Agreement was quantified using the two-way random-effects intraclass correlation coefficient ICC(2,1) for absolute agreement. Results: Horizontal coordinates showed excellent agreement in every class (ICC 0.90–0.96), whereas vertical coordinates were markedly more variable (ICC 0.52–0.94). Among the sagittal measurements, Wits and Yen showed the most class-independent agreement (ICC 0.87–0.90 and 0.76–0.86); Tau, Sar and W reached their highest values in Class III (0.93–0.95) and ANB its lowest in Class I (0.55, 95% CI 0.32–0.84). The methodologically important observation is that the ICC and the standard error of measurement (SEM) need not order measurements in the same way: a measurement that ranks patients consistently (high ICC) does not necessarily locate an individual patient precisely. For a threshold-based decision such as skeletal-class assignment, absolute precision is the relevant property, yet published reliability tables rarely report it. Conclusions: Inter-rater agreement for the six measurements was not uniform across skeletal classes in this dataset. Because every class-specific estimate rests on eight cephalograms with wide and overlapping confidence intervals, and because ANB and Wits both contributed to the class assignment itself, these findings are exploratory and hypothesis-generating.

Background/Objectives: Cephalometric analysis remains the principal tool for diagnosing sagittal jaw discrepancies. Its clinical reliability depends on the accuracy of landmark identification and the resulting horizontal and vertical dispersion in the Cartesian system. The aim of this study was to assess the inter-rater reliability of 12 cephalometric landmarks and of six measurements used for sagittal discrepancy assessment (ANB, Wits, Tau, Yen, Sar and W) across three skeletal classes. Methods: Twenty-four lateral cephalograms (eight per skeletal class) were assessed twice, seven days apart, by 15 orthodontists trained in a 5-hour calibration course. Landmark coordinates were normalized against a reference value derived from two expert raters. Reliability of horizontal (X) and vertical (Y) coordinates and of the resulting sagittal measurements was quantified using the intraclass correlation coefficient ICC(2,1). Between-class differences were tested with Fisher Z-transformed Z-tests; the Benjamini–Hochberg false-discovery-rate (FDR) procedure was applied to control for multiple comparisons. Results: Horizontal coordinates of all landmarks showed excellent inter-rater agreement (ICC 0.91-0.96) regardless of skeletal class. Vertical coordinates showed considerably greater variability (ICC 0.52-0.94). Among sagittal measurements, Wits demonstrated the highest reliability across all classes (ICC 0.87-0.90), followed by Yen (ICC 0.76-0.86). Tau, Sar and W reached near-perfect agreement only in Class III patients (ICC 0.93-0.95). The ANB angle showed the lowest reliability, particularly in Class I (ICC =0.55, 95% CI 0.32-0.84). Conclusions: Vertical and horizontal dispersion of cephalometric landmarks materially influences the diagnostic accuracy of sagittal measurements. Wits and Yen are the most reliable parameters across all skeletal classes, whereas Tau, Sar and W are particularly trustworthy in Class III. The traditional reliance on the ANB angle as a gold standard should be reconsidered, as it was the least reproducible measurement in our cohort.

Automated cephalometric landmark detection using deep learning has the potential to transform routine orthodontic diagnosis. However, the clinical relevance of AI localization accuracy depends critically on how detection errors propagate into derived angular measurements and skeletal classifications. This study presents a systematic clinical validation of 14 YOLO-based model configurations, evaluating the effects of architecture (YOLOv5/YOLOv11), bounding box size (40-150 px), dataset scale (235-4255 images) and training duration on landmark detection accuracy with specific focus on the four clinically critical landmarks that define the ANB angle: Sella (S), Nasion (N), A-point (A) and B-point (B). The best-performing model (YOLOv11s, 40×40 px bounding box, 4255 training images) achieved a mean radial error of 3.10 ± 1.00 mm and a Successful Detection Rate of 87.2% at the 4 mm threshold for S, N, A, and B. Despite this error magnitude, ANB-based skeletal classification demonstrated 96.9% concordance with expert assessments (95% bootstrap CI: 93.8–99.2%, n = 130 classifications), with all discordances confined to borderline cases within 1^◦ of diagnostic thresholds. Notably, the localization accuracy achieved by the best AI models falls within the inter-operator variability range reported for experienced human clinicians (1.5–3.5 mm), indicating that the AI system has reached a threshold of clinical equivalence for skeletal classification purposes. Bounding box size emerged as the single most influential hyperparameter, with a 3.4-fold increase in mean radial error from 40×40 to 150×150 px configurations. These findings support the clinical deployment of YOLO-based AI systems for automated ANB-based skeletal classification, while highlighting the need for human oversight in borderline cases.

Gingival recession is commonly linked to alveolar bone dehiscence, inflammatory burden, traumatic brushing, or excessive orthodontic forces. However, recession is also observed in some patients despite apparently mild or “biologically acceptable” loading, particularly in thin periodontal phenotypes. Here, we propose the Gingival Creep Failure Theory, a hypothesis-driven conceptual framework in which gingival soft tissues undergo time-dependent viscoelastic deformation (creep) under sustained or repetitive tensile microstrain. Over time, accumulated deformation and microstructural fatigue may reduce recoil capacity and shift the gingival margin apically once tissue-level tolerance is exceeded. Gingival connective tissue is modeled as a fiber-reinforced, fluid-rich viscoelastic composite whose response depends on collagen architecture, cross-linking, proteoglycan-mediated hydration, and vascular support. In thin phenotypes characterized by reduced connective tissue volume and altered extracellular matrix (ECM) organization, creep progression is hypothesized to accelerate, lowering the threshold at which fatigue-related microdamage translates into clinically detectable marginal migration. Evidence from collagenous connective tissue biomechanics supports the plausibility that sub-failure sustained or cyclic loading can produce cumulative deformation and incomplete recovery; however, direct creep–fatigue data for human gingiva remain limited, underscoring the need for targeted validation studies. This hypothesis integrates soft tissue mechanics with periodontal phenotype biology and orthodontic loading patterns and proposes creep and microstructural fatigue as plausible time-dependent contributors to gingival recession in susceptible phenotypes. Because direct in vivo gingival strain and creep–fatigue measurements remain limited, the model should be interpreted as hypothesis-generating and in need of targeted clinical and experimental validation.

Peri-implant marginal bone stability remains a major determinant of long-term implant success, yet clinical studies report early marginal bone changes ranging from near-stable conditions in some protocols to approximately 1–2 mm during the first year in more traditional series, underscoring considerable biological variability. In the present review, a remodeling-dominant state refers to turnover-led peri-implant adaptation with limited net cortical gain, whereas modeling-driven apposition refers to uncoupled surface bone addition and cortical reinforcement. We conducted a structured narrative review of PubMed/MEDLINE, Scopus, and Web of Science for literature published between 2000 and February 2026 and qualitatively synthesized direct peri-implant evidence, craniofacial/oral non-implant evidence, and extrapolative mechanobiology from long-bone and systemic models. The available literature supports osteocyte-centered SOST/Wnt regulation as biologically plausible for peri-implant cortical adaptation; however, direct human peri-implant molecular validation remains limited. Based on this synthesis, we propose a hypothesis-generating framework in which mechanical signal profile, microenvironmental stability, and host-related factors influence the probability of transition from a remodeling-dominant to a modeling-dominant peri-implant state. This framework should therefore be interpreted as a testable conceptual model rather than a validated peri-implant mechanism. Its main value lies in organizing current evidence and defining priorities for translational studies that integrate molecular, imaging, and biomechanical endpoints.

Background: Orthodontic tooth movement is traditionally explained through mechanical deformation of the periodontal ligament (PDL); however, increasing evidence indicates that immune mechanisms critically shape bone remodeling outcomes. Mechanical stimuli influence immune cell recruitment, cytokine release, and phenotypic polarization, but these components are rarely integrated into a unified framework. Conceptual framework: We propose the Osteoimmune Axis Model, a conceptual framework describing how mechanical loading may bias immune polarity and thereby gate periodontal remodeling. Compressive loading appears to favor an M1 macrophage/Th17-dominant program associated with pro-inflammatory cytokines and enhanced RANKL-mediated osteoclastogenesis. In contrast, tensile or physiological strains may favor M2 macrophages and regulatory T cells (Treg), supporting IL-10, TGF-β, angiogenesis, extracellular-matrix repair, and osteoblastic activity. Stromal cells are proposed to act as mechanosensors and immune amplifiers that shape cytokine gradients and feedback loops. Predictions: The model predicts that identical forces may produce divergent outcomes depending on immune baseline; load duration may be more destructive than peak magnitude; tensile strain may stabilize M2/Treg pathways; thin periodontal phenotypes may shift toward the catabolic pole at lower mechanical loads; ROS may amplify immune-mediated bone loss; and immunomodulation may raise the threshold for pathological remodeling. Conclusion: The Osteoimmune Axis integrates mechanobiology and immunology into a testable framework for explaining variability in orthodontic periodontal remodeling and for generating hypothesis-driven, immune-aware risk assessment.