From 3D facial morphometry to clinical decision support: a lean-M.A.S.T.E.R. framework for digital dentistry and orthodontic treatment planning

Introduction Three-dimensional (3D) facial imaging is increasingly used in orthodontics and digital dentistry, but quantitative morphometric information does not automatically translate into reproducible and clinically interpretable decision support. A standardized pathway integrating image acquisition, landmark annotation, morphometric interpretation, visualization, patient communication, and quality monitoring is therefore needed. Methods We developed a Lean-M.A.S.T.E.R. clinical decision-support framework using morphometric evidence derived from three previously published studies based on 3D photogrammetry and landmark-based facial analysis in female reference cohorts. No new individual-level data were collected or reanalyzed. The evidence was translated into a six-phase framework comprising Mapping & Contextualization, Analysis & Diagnosis, Strategic Design, Testing & Adaptive Execution, Embedding & Sustainability, and Resilience & Renewal. Lean tools, Standard Work, process mapping, performance indicators, governance mechanisms, and a prospective validation pathway were incorporated into the model. Results The conceptual and operational outputs of framework development comprised an implementation-oriented architecture linking 3D facial acquisition to morphometric interpretation, clinician-facing decision support, patient communication, performance monitoring, and continuous renewal. These framework outputs included a SIPOC process map, a structured morphometric evidence layer, alternative design configurations, predefined evaluation indicators, and an integrated governance and sustainability matrix. Four prospective hypotheses were defined concerning inter-operator reproducibility, workflow efficiency, clinical decision support, and patient comprehension. A prospective evaluation pathway based on single-center pilot testing, PDSA refinement, and subsequent multicenter validation was also specified. These outputs represent proposed framework components and have not yet been prospectively evaluated. Conclusions The Lean-M.A.S.T.E.R. framework provides a structured and testable bridge between 3D facial morphometry and clinical decision support in digital dentistry and orthodontics. Morphometric descriptors are treated as population- and context-specific information rather than universal aesthetic standards or prescriptive treatment targets. Prospective validation is required to determine the framework's feasibility, reproducibility, robustness, transferability, and sustainability while preserving clinician oversight and patient-centered decision-making.