ArticleThe Angle orthodontist2024
Digitization and validation of the open bite checklist manifesto: a step toward artificial intelligence.
Article in The Angle orthodontist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Is mandibular posterior dento-alveolar intrusion essential in treatment of skeletal open bite in adult patients? A single center randomized clinical trial.BMC oral health · 2025Trial
- Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
2 authors.
Funding
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Abstract
objectivesTo introduce and validate newly designed computer software to aid in the diagnosis of anterior open bite (AOB). MATERIALS AND
methodsThe software was constructed based on the algorithm of a standardized open bite checklist, which considered skeletal, dental, and soft tissue components, as well as smile characteristics. Feeding the software with this input yielded a digital form output (DFO) in the guise of a diagnostic report characterizing the AOB phenotype, contributing components, severity, associated problems, and functional factors. For validation, DFO was compared to a conventional form output (CFO), created in a standardized manner according to expert opinions. Agreement between the DFO and CFO in terms of AOB phenotype was the primary outcome, while the secondary outcome was the number of missing diagnostic components in either method.
resultsPercentage of agreement between CFO and DFO was 82.2%, with a kappa coefficient of 0.78, which is considered a good level of agreement. There was a statistically significant relationship between the number of missing diagnostic components in CFO and level of disagreement, which rendered the DFO more reliable.
conclusionsNewly constructed software represents an efficient and valid diagnostic tool for AOB and its contributing components. There was good agreement between CFO and DFO, with the latter being more comprehensive and reliable. The algorithm built in the software can be used as the basis for a future artificial intelligence model to aid in the diagnosis of AOB.
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