ReviewThe Kaohsiung journal of medical sciences2026
Harnessing Artificial Intelligence to Predict Dental Crowding in Modern Orthodontic Practice: A Comprehensive Review.
Review in The Kaohsiung journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
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Abstract
Dental crowding is fundamental to orthodontic diagnosis and treatment planning, yet what constitutes "crowding" remains surprisingly heterogeneous. Space analysis quantifies tooth size-arch length discrepancy, irregularity indices characterize alignment displacement, and clinician-assigned severity reflects broader diagnostic interpretation. This narrative review examines the evolution of crowding assessment from conventional space analysis and digital measurement to two-dimensional artificial intelligence (AI), three-dimensional point-cloud learning, and clinician-aware agentic AI. Digitalization improves efficiency and reproducibility but does not resolve fundamental dependencies on how crowding is defined, anatomically represented, and established as a reference standard. Two-dimensional models enable prediction from readily available images but are constrained by projection and scale, whereas three-dimensional approaches directly learn from dental surface representations and can capture local geometric relationships. In a companion study of 495 arches from 284 patients, PointNet++ achieved an R
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