Accuracy of Machine Learning in Identification of Dental Implant Systems in Radiographs – A Systematic Review and Meta-Analysis

Abstract

Machine learning has played a promising role in medical diagnosis. The aim of this systematic review was to evaluate the accuracy of machine learning in identification of dental implant systems from radiographs. This systematic review was conducted by searching four electronic databases, PubMed, SCOPUS, Cochrane Library, and Google Scholar. Inclusion criteria were studies that used machine learning for implant identification. Our search yielded 87,189 studies, of which a total of eight studies were found which used machine learning for implant identification. Of the included studies, three studies provided the required data to conduct meta-analysis. The overall pooled estimate of accuracy of the three included studies was 95.43%. Machine learning appears to be practically efficient in implant recognition. The findings of this review suggested an inadequate reporting of studies due to a lack of standardized guidelines for reporting and conducting the studies that investigate machine learning in implant identification. This could limit the reliable interpretation of the reported accuracy.