# Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs
## 1 Introduction  
Bitewing radiographs are a vital adjunct to clinical examination for diagnosis and treatment planning of dental and supporting tissue diseases [1]. A balance between risks and benefits of acquiring radiographs, following standards of justification for radiation dose protection, optimizing, and restricting radiation dose, should be achieved [2]. Reducing the radiation dose could be partly achieved by ensuring the acquisition of diagnostically acceptable radiographic images from the first attempt. However, bitewing radiographs are sometimes re-taken for diagnostic purposes. Indeed, [PERSON] et al., 2021, demonstrated ina systematic review that the rejection rate of bitewings because of its limited diagnostic value was 9%. The most common reasons for rejection were positional errors and cone cuts. It was further suggested that reject rates were higher with digital imagining, specifically when using intraoral sensors [3]. The decision-making process involved in identifying the diagnostic value of bitewing images is complex and is highly dependent on the operator's/technician's knowledge and experience. In general, bitewing images should equally capture both maxillary and mandibular crowns and crestal bone. The premolar bitewings should capture the distal surfaces of both maxillary and mandibular canines, while molar bitewings should capture the distal surface of the second premolar and the distal surface of the last standing molar in the mouth. Additionally, there should be no overlap of proximal surfaces in contact areas [4]. One possible adjunct that may improve this decision-making process could be the use of Artificial Intelligence (AI) programs in the field. AI refers to any computer or technology capable of mimicking human thought processes such as problem-solving. Additionally, it can refer to the capacity of computers that can learn from data and represent their own intelligence so that issues can be solved from this learning [5]. This science was first developed by [PERSON], who established the first AI laboratory in 1957 [6]. AI varies in types and classifications depending on its complexity in performance. Machine learning (ML) is a type of AI that makes software application more accurate in predicting outcomes without programming. Deep Learning (DL) is another type of AI defined as \"computational systems that learn over time based on experience\" [7; 8; 9; 10]. Both ML and DL are considered as implementations of computational methods (i.e., algorithms) through which models can be generated from any given data, and those models can then be used for further prediction of new data sets with similar properties [11]. The differentiation between ML and DL can be made based on the complexity of the network used to extract features from the data. It is also related to the complexity of the data itself. ML is usually appropriate for regression or classification of simple numerical data. DL is recommended for the analysis of large collections of images or other complex data [8; 9; 10; 11]. Clinical decision-support systems (CDSS) are software-based AI programs. They can be based on either ML or so-called rules-based expert systems [7; 12]. CDSS are designed to provide the health care providers with specialist assistance in their daily activities, helping with tasks that depend on data and information exploitation [7]. There are many types of CDSS systems, such as artificial neural networks (ANNs), fuzzy expert systems, evolutionary computation, and hybrid intelligent systems. They vary in methods of handling data and the complexity of the analysis process [7; 10]. Nowadays, the application of AI is increasing in multiple dental and medical applications with promising results [7; 10]. Radiographic images are considered an important source of data for developing AI models in the health professional fields. Therefore, it is important to consider the factors in the quality of radiographic images that can affect the appearance of structures [7].