Reasons for extraction obtained by artificial intelligence

Abstract

Artificial intelligence (AI) is the subfield of computer science concerned with projection of intelligent machines, software, and algorithms. By way of computer-assisted monitoring of dental patients, the amount of raw electronic data markedly enlarges, creating the possibility of using AI in scientific analysis. In order to prevent the reasons and diminish the need for dental extraction, it is necessary to always have updated information about the reasons for extraction, so we make use of AI analysis. Our aim was to assess the possible use of AI in the collection, triage, sorting, counting, and analysis of electronic data, drawing scientifically acceptable conclusions. A case-control study of electronic data was done. Data preparation and counting were done using special C# codes. The analysis of impact of non-dental attributes was done using the OLAP analysis and specific detection algorithm. OLAP detected the attribute of age with sensitivity of 44.0% and specificity of 100.0%, and value of attibute of age from 55 to 64. The specific algorithm gave direct reasons: caries (43.77%), periodontal diseases (37.23%), fracture (6.82%), prosthetic reasons (4.31%), impactions (3.12%), orthodontic reasons (2.73%), primary teeth (0.32%), and others (1.7%). The algorithm found that the impact of attributes of gender, age, and job was statistically significant (gender: C²=7.095, df=1, p=0.0077; age: C²'261, df=8, p<0.0001; job: C²'46, df=7, p<0.0001). Using AI, raw electronic data can be successfully collected, triaged, sorted, counted, and analyzed, and utilized AI algorithms can perform non-parametric evaluation of the possible impact of non-dental attributes, producing scientifically valid conclusions.

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