Machine learning in maxillofacial radiology: A review

Radiology is one of the branches of medical science that has made rapid progress over the past decades. The newer imaging modalities are highly accurate and use less of ionizing radiation. Therefore, when a radiologist interprets a radiographic image a lot of additional information is displayed on the images compared to the conventional imaging modalities. Artificial intelligence (AI) could be a possible modality which can reduce the workload of the radiologist thus allowing more time for the imaging of challenging cases. The field of Dentomaxillofacial radiology can also be benefited with AI since the number of qualified maxillofacial radiologist are lesser in number. AI can be helpful in various diagnostic procedures involving maxillofacial radiology. The applications could vary from age estimation using radiographs which could be beneficial for orthodontic and pedodontics purposes to radiographic detection of caries. The AI can perform a number of tasks, which could reduce the workload on a maxillofacial radiologist. However, there is a growing anxiety that AI may reduce the relevance of a radiologist in the near future. The purpose of this review is to focus on analyzing the benefits and drawbacks of AI and its application in the field of maxillofacial radiology.