AI identifies biological sex using dental X-rays

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A team of researchers from India has made progress toward the development and training of an algorithm that can determine biological sex from dental X-rays with 94 per cent accuracy. This application of what is referred to as a deep-learning method demonstrates the potential of such an approach to augmenting conventional evidence in criminal and other investigations.

The team at the Mepco Schlenk Engineering College in Tamilnadu, India, explain in a paper published in the International Journal of Biomedical Engineering and Technology, that their algorithm comprises three components: image pre-processing, gradient-based recursive threshold (GBRT) segmentation, and classification. 

Initially, they used a so-called prime magic square filter during the image pre-processing step to remove unwanted noise. The prime magic square filter uses a special grid of numbers overlaid on the image within the computer and compares pixel values in the image with the corresponding values in the grid to determine what are distortions or compression artifacts, which contribute to image noise and so can be brushed away to give a clean and accurate image for the subsequent analysis.

The GBRT segmentation technique refines the images, enhancing the algorithm’s ability to extract relevant information. Finally, the classification stage utilises a Resnet50 neural network, a widely adopted deep learning architecture. 

The team trained the algorithm with 3000 dental X-rays for which the individual’s biological sex was known. This allowed the algorithm to discern the biological sex associated with dental X-rays presented to it in which the biological sex of the individual is not known.

For the purposes of testing the team used 1000 images, a subset of the original collection where sex was known to determine whether the system would correctly assign biological sex. Teeth and jawbones are sexually dimorphic in humans to varying degrees but there are also marked effects of nutrition and socioeconomics on how our jaws and teeth grow. The new system can see through these potential discrepancies based on its training with the X-ray images.

Within the specific context of legal proceedings, there is now a need to assess the algorithm more rigorously so that reliability of the data, potential algorithm biases, and the need for expert interpretation should be taken into account. 

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