Clinical applicability of automated cephalometric landmark identification: Part II - Number of images needed to re-learn various quality of images

Aim To estimate the number of cephalograms needed to re-learn for different quality images, when artificial intelligence (AI) systems are introduced in a clinic. Settings and sample population A total of 2385 digital lateral cephalograms (University data [1785]; Clinic F [300]; Clinic N [300]) were used. Using data from the university and clinics F and N, and combined data from clinics F and N, 50 cephalograms were randomly selected to test the system's performance (Test-data O, F, N, FN). Materials and methods To examine the recognition ability of landmark positions of the AI system developed in Part I (Original System) for other clinical data, test data F, N and FN were applied to the original system, and success rates were calculated. Then, to determine the approximate number of cephalograms needed to re-learn for different quality images, 85 and 170 cephalograms were randomly selected from each group and used for the re-learning (F85, F170, N85, N170, FN85 and FN170) of the original system. To estimate the number of cephalograms needed for re-learning, we examined the changes in the success rate of the re-trained systems and compared them with the original system. Re-trained systems F85 and F170 were evaluated with test data F, N85 and N170 from test data N, and FN85 and FN170 from test data FN. Results For systems using F, N and FN, it was determined that 85, 170 and 85 cephalograms, respectively, were required for re-learning. Conclusions The number of cephalograms needed to re-learn for images of different quality was estimated.

Automated summary

The study estimated the number of cephalograms needed to re-learn for different quality images when artificial intelligence systems are introduced in a clinic by testing different data sets and comparing the success rates.