Neural networks for classification of cervical vertebrae maturation: a systematic review

To assess the accuracy of identification and/or classification of the stage of cervical vertebrae maturity on lateral cephalograms by neural networks as compared with the ground truth determined by human observers.

Search results from four electronic databases (PubMed [MEDLINE], Embase, Scopus, and Web of Science) were screened by two independent reviewers, and potentially relevant articles were chosen for full-text evaluation. Articles that fulfilled the inclusion criteria were selected for data extraction and methodologic assessment by the QUADAS-2 tool.

The search identified 425 articles across the databases, from which 8 were selected for inclusion. Most publications concerned the development of the models with different input features. Performance of the systems was evaluated against the classifications performed by human observers. The accuracy of the models on the test data ranged from 50% to more than 90%. There were concerns in all studies regarding the risk of bias in the index test and the reference standards. Studies that compared models with other algorithms in machine learning showed better results using neural networks.

Neural networks can detect and classify cervical vertebrae maturation stages on lateral cephalograms. However, further studies need to develop robust models using appropriate reference standards that can be generalized to external data.