Latent Disentanglement in Mesh Variational Autoencoders Improves the Diagnosis of Craniofacial Syndromes and Aids Surgical Planning

The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to simulate the outcome of a range of craniofacial surgical procedures. This opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes

Facing Africa: Describing Noma in Ethiopia.

Noma affects the most marginalized communities in the world, beginning as oral ulceration and rapidly progressing to orofacial gangrene. With a mortality rate estimated to be as high as 90% and with very few able to access treatment in its active phase, very little is understood about the disease. This retrospective review of patients treated by Facing Africa for deformity and functional impairment secondary to noma between May 2015 and 2019 highlights some of the difficulties encountered by those afflicted. Eighty new patients with historical noma defects were identified and were seen over the course of nine surgical missions, with notes providing valuable geographical, socioeconomic, and psychosocial information. The mean self-reported age of onset was 5 years and 8 months, with a median time of 18 years from onset to accessing treatment. Before intervention, 65% covered their face in public, 59% reported difficulty eating, 81% were unhappy with their appearance, and 71% experienced bullying. We aimed at emphasizing the significant burden, both psychologically and physically of noma, demonstrating the disparity between recent decades of progress in the well-being of Ethiopians in general and the access to health care and mental health support for some of those most in need