# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 4 Discussion  
The aim of the current study was to assess the effectiveness of ML in detecting the diagnostic quality of BW radiographs so it can be used as a decision-support tool to help technicians check the quality of the taken radiographs faster and with less effort [6; 16]. The diagnostic quality of the BW depends on many factors and features, however, this study focused on the assessment of the contact areas as an important feature of the quality of the BW images. The detection of different radiographic errors regarding the contact area is subject to the discriminatory skills of examiners which varies significantly and is heavily related to examiner experience. Therefore, the application of ML to detect radiographic errors will aid in reducing this variation significantly. Scoring proximal contact areas were developed through stages--from a comprehensive list to a more compact encompassing one--in this project. The objective was to identify prominent features in bitewing radiographs for a successful classification of diagnostic and non-diagnostic images and to yield a less complex model for the AI program. Initially, we identified eleven possible proximal contact area presentations based on tooth structure, amount of proximal contact overlap, and the acquired radiographic field. Then, to facilitate cross-tabulation and coding of proximal contact areas, these scores were further grouped in terms of diagnostic value into four basic groups. Finally, images were labeled based on the most prominent proximal contact area features to aid in identifying the diagnostic value. This included proximal contact overlap within tooth (enamel, DEJ, and dentin) or within restoration (with or without clear margins). In this study, Python and Python-friendly environments were used to build the model, which is a popular and powerful interpreted IT language. The descriptive analysis of the dataset revealed class imbalance, which means over-representation of some labels while others were under-represented. The class \"overlap within enamel\" was over-represented and the class \"overlap within restoration (clear margin) was adequately represented in the dataset, while the classes \"overlap within restoration (unclear margin), \"overlap within dentin\", and \"overlap at DEJ\" were all under-represented in the dataset. Thus, it is expected that the model will be more efficient at detecting the well-represented classes compared to the under-represented classes. Additionally, most of the assessed proximal contact areas, if they had errors, were still of diagnostic value (overlap within enamel or within restoration with clear margin). This could partly be because all assessed radiographs in this study were taken with the holder technique. Indeed, it was reported that bitewing radiographs that were acquired by the holder technique (Rinn XCP film positioning devices) had fewer horizontal errors than those that were taken by the loop technique (conventional method) [22]. As for ML performance analysis, recall, precision, and F1 score are all highly affected by the class imbalance and thus are not suitable evaluation metrics for the current model. Thus, log loss was the best evaluation metric to indicate our model accuracy. The loss is calculated on training and validation and its interpretation is how well the model is doing for these two sets. Unlike other accuracy metrics, loss is not a percentage. It is a summation of the errors made for each example in training or validation sets. The lower the loss, the better the accuracy of the model. Log loss nearer to 0 indicates high accuracy, whereas if the log loss was away from 0 then it indicates low accuracy. The built model in the current study had a training loss of 0.15, which indicates high accuracy. The current findings have several impacts in the field of dentistry. The availability of a machine learning model to assess the quality of radiographs will reduce the errors of misreading the radiographs from both the unskilled technicians and dentists.