A Deep Learning Approach to Submerged Teeth Classification and Detection
Condition: Artificial Intelligence; Submerging Tooth
Study type: Observational

Summary: 
      Objectives: The study aimed to compare the success and reliability of an artificial
      intelligence application in the detection and classification of submerged teeth in
      orthopantomography (OPG).

      Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify
      submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN
      architecture, processed the radiograph to define the boundaries of submerged molars. A
      separate testing set was used to evaluate the diagnostic performance of the system and
      compare it to the expert level.

      Results: The success rate of classification and identification of the system is high when
      evaluated according to the reference standard. The system was extremely accurate in
      performance comparison with observers.

      Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable
      to that of experts. It is useful to diagnose submerged molars with an artificial intelligence
      application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.
    

Detailed description: 
      Pre-processing, Training, and Classification The study was conducted with balanced data sets.
      The case and control data sets were randomly divided into two parts, the training group (27
      case group/27 control group) and the test group (10 case group/10 control group) to prevent
      the use of the visuals in the training group for retesting. The testing data set was not seen
      by the system during the training phase.

      All 2943-by-1435 pixel images in the data set were resized to 971 by 474 pixels prior to
      training. All OPG images used include the whole dentitions. The training and test data sets
      were used to estimate and generate weight factors for the optimal CNN algorithm. An arbitrary
      sequence was generated using open-source Python programming (Python 3.6.1, Python Software
      Foundation, Wilmington, DE, USA, https://www.python.org/) language and OpenCV, NumPy, Pandas,
      and Matplotlib libraries. In this study, Tensorflow for model development was used to
      classify submerged primary molars. InceptionV3 architecture was used as transfer learning,
      and the transfer values were saved in the cache. Then, fully connected layer and softmax
      classifiers were combined to form the final model layers. The training was carried out using
      7000 steps with 16G RAM and a PC equipped with NVIDIA GeForce GTX 1050.
    

Intervention name(s): deep learning
Intervention type(s): Diagnostic Test
Intervention description(s): the deep learning method is a field of study involving artificial neural networks and similar machine learning algorithms with many hidden layers.
Observational model: Other
Time perspective: Retrospective
Sampling method: Probability Sample
Study population: 
        The data set included OPGs of 19000 children aged 5-12 years that were collected between
        January 2016 and December 2018.
      
Keywords: artificial intelligence; infraocclusion; submerged tooth