American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2021
Evaluation of the efficiency of computerized algorithms to formulate a decision support system for deepbite treatment planning.
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El-Dawlatly Mostafa M, Abdelmaksoud Ahmed R, Amer Omar M, El-Dakroury Amr E, Mostafa Yehya A
Аннотация
INTRODUCTION: This study aimed to evaluate the efficiency of a newly constructed computer-based decision support system (DSS) on the basis of artificial intelligence technology and designed to plan treatment for patients with a deep overbite.
METHODS: With the help of information technology, a DSS was developed specifically for treatment planning of deepbite malocclusion. The program inputs were the components and the contributing factors used commonly by the orthodontic clinicians in deepbite diagnosis. The program outputs were the treatment planning options for deepbite treatment. A total of 357 decisions made by the algorithm were evaluated for accuracy by comparing them to the actual treatment changes of 51 patients with a well-treated deepbite.
RESULTS: The decisions made by the algorithm were precise, with 94.4% having a very good agreement with actual treatment changes determined using Cohen's kappa coefficient.
CONCLUSIONS: The constructed DSS was shown to be an efficient tool for planning treatment of deep overbite malocclusion in the permanent dentition; thus, the artificial intelligence could be used to formulate a customized plan for orthodontic clinicians.
METHODS: With the help of information technology, a DSS was developed specifically for treatment planning of deepbite malocclusion. The program inputs were the components and the contributing factors used commonly by the orthodontic clinicians in deepbite diagnosis. The program outputs were the treatment planning options for deepbite treatment. A total of 357 decisions made by the algorithm were evaluated for accuracy by comparing them to the actual treatment changes of 51 patients with a well-treated deepbite.
RESULTS: The decisions made by the algorithm were precise, with 94.4% having a very good agreement with actual treatment changes determined using Cohen's kappa coefficient.
CONCLUSIONS: The constructed DSS was shown to be an efficient tool for planning treatment of deep overbite malocclusion in the permanent dentition; thus, the artificial intelligence could be used to formulate a customized plan for orthodontic clinicians.