Forensic age estimation of the knee by post-mortem DR, CT, and MR imaging: a comparative study.
Apameh Khatam-LashgariMette Lønstrup HarvingChiara VillaNiels LynnerupSara Tangmose LarsenPublished in: International journal of legal medicine (2024)
It is believed by many that reference data for age estimation purposes must be imaging-modality specific. A study from our department has however proven otherwise. We therefore found it interesting to investigate this further by looking at the level of agreement between different imaging modalities. The aim of this study was to investigate the level of agreement between the three radiological modalities, computed tomography (CT), magnetic resonance imaging (MRI), and digital radiography (DR), in assessing the ossification of the epiphyses of the knee. A total of 34 deceased individuals of 10-25 years of age, brought in for a medicolegal autopsy at our department, were scanned by CT, MRI, and DR. The ossification stages of the three bones of the right knee, distal femoral, proximal tibial, and proximal fibular epiphysis were assessed using the established combined staging method by Schmeling et al. and Kellinghaus et al. Analysis of the results by Cohen's weighted kappa showed a good agreement between CT and DR (K = 0.61-0.70), and MRI and DR (K = 0.68-0.79) but only moderate agreement between CT and MRI (K = 0.55-0.57). This leads us to conclude that different radiological images cannot be used interchangeably for age estimation purposes, so reference material needs to be imaging-modality specific. However, to make a more general conclusion research on a larger population is needed.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- computed tomography
- magnetic resonance
- dual energy
- total knee arthroplasty
- diffusion weighted imaging
- image quality
- editorial comment
- high resolution
- positron emission tomography
- knee osteoarthritis
- tertiary care
- fluorescence imaging
- high intensity
- machine learning
- toll like receptor
- deep learning
- artificial intelligence
- nuclear factor
- inflammatory response
- optical coherence tomography
- kidney transplantation
- electronic health record
- convolutional neural network
- data analysis