1. Application of artificial intelligence in the dental field: A literature review
- Author
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Yuki Iwawaki, Takahiro Kishimoto, Tetsuo Ichikawa, Takashi Matsuda, and Takaharu Goto
- Subjects
Artificial neural network ,Mean squared error ,Receiver operating characteristic ,business.industry ,Computer science ,0206 medical engineering ,030206 dentistry ,02 engineering and technology ,Cochrane Library ,020601 biomedical engineering ,Field (computer science) ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,Data quality ,Oral and maxillofacial surgery ,Dentistry (miscellaneous) ,Artificial intelligence ,Oral Surgery ,Medical diagnosis ,business - Abstract
Purpose The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field,focusing on the evaluation criteria and architecture types. Study selection Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on "oral and maxillofacial surgery." Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusions Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning.
- Published
- 2022