Machine classification of dental images with visual search

The authors performed this study to assess the performance of a computer-based classification system that uses gaze locations of observers to define the subspace for machine learning.

Thirty-two dental radiographs were classified by an expert viewer into four categories of disease of the periapical region: no disease (normal tooth), mild disease (widened periodontal ligament space), moderate disease (destruction of the lamina dura), and severe disease (resorption of bone in the periapical area). There were eight images in each category. Six observers independently viewed the images while their eye gaze position was recorded. They then classified the images into one of the four categories. A sample of image space was used as input to a machine learning routine to develop a machine classifier. Sample space was determined with three techniques: visual gaze, random selection, and constrained random selection. K analyses were used to compare classification accuracies with the three sampling techniques.

With use of the expert classification as a standard of reference, observers classified images with 57% accuracy, and the machine classified images with 84% accuracy by using the same gaze-selected features and image space. Results of kappa analyses revealed mean values of 0.78 for gaze-selected sampling, 0.69 for random sampling, 0.68 for constrained random selection, and 0.44 for observers. The use of sample space selected with the visual gaze technique was superior to that selected with both random-selection techniques and by the observers.

Machine classification of dental images improves the accuracy of individual observers using gaze-selected image space.