Using Natural Language Processing to Develop an Automated Orthodontic Diagnostic System
This work addresses the challenge of automating orthodontic diagnostics for dentists, but it is incremental as it applies existing NLP methods to a new domain-specific dataset.
The researchers tackled the problem of automatically designing orthodontic treatment plans from free-form medical certificates written by dentists, achieving an F1-score of 0.585 for extracting orthodontic problems and a correlation coefficient of 0.584 for treatment prioritization.
We work on the task of automatically designing a treatment plan from the findings included in the medical certificate written by the dentist. To develop an artificial intelligence system that deals with free-form certificates written by dentists, we annotate the findings and utilized the natural language processing approach. As a result of the experiment using 990 certificates, 0.585 F1-score was achieved for the task of extracting orthodontic problems from findings, and 0.584 correlation coefficient with the human ranking was achieved for the treatment prioritization task.