The Angle orthodontist · 2024
Digitization and validation of the open bite checklist manifesto: a step toward artificial intelligence.
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Akl Heba E, Mostafa Yehya A
Аннотация
OBJECTIVES: To introduce and validate newly designed computer software to aid in the diagnosis of anterior open bite (AOB).
MATERIALS AND METHODS: The 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.
RESULTS: Percentage 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.
CONCLUSIONS: Newly 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.
MATERIALS AND METHODS: The 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.
RESULTS: Percentage 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.
CONCLUSIONS: Newly 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.