The purpose of this research is to facilitate the use of a deep-learning architecture with the GRBAS scale in clinical practice.
Explore This IssueMarch 2021
During training, Create ML randomly splits data into training and validation sets. The model learns iteratively from the training set, and during each iteration it uses the validation set to check its accuracy. We averaged the training and validation scores of five training sessions; the training datasets were randomly chosen and differed for each session. The metrics for the G scale showed high accuracy for the training data (0.806 SD 0.013). The model also had relatively high accuracy for the R scale (0.812 SD 0.008). Among the five categories, accuracy was lowest for the B scale (0.722 SD 0.016) and highest for the S scale (0.914 SD 0.005). The model had acceptable accuracy for the A scale (0.777 SD 0.010). The application was easy to use, requiring only an iPhone. Each score was displayed for 0.975 s. Although phonations of less than 0.975 s were difficult to evaluate correctly, the evaluations were stable when phonation was stable. However, any noise in the examination room destabilized assessment.