Automation and deep (machine) learning in temporomandibular joint disorder radiomics. A systematic review.

Deep learned models diagnosed osteoarthritis as accurately as clinicians from 2D and 3D radiographs but, in comparison, performed poorly when detecting disc disorders from MRI datasets. Complexities in clinical classification criteria; non-standardised diagnostic parameters; errors in image acquisition; cognitive, contextual, or implicit biases were influential variables that generally affected analyses of inflammatory joint changes and disc disorders.