Systemic sclerosis accurately screened on laptop
The rare autoimmune disease systemic sclerosis could be diagnosed more quickly with a new deep neural network operating on a standard laptop.
This is the claim of the chair of the Biomedical Engineering Department at the University of Houston, whose proposed network can immediately differentiate between images of healthy skin and skin with systemic sclerosis (SSc), which is characterised by hardened or fibrous skin and internal organs.
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"Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterisation of SSc," reports Metin Akay, John S. Dunn Endowed Chair Professor of biomedical engineering. The work is published in the IEEE Open Journal of Engineering in Medicine and Biology.
"We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive and accurate screening tool for SSc."
According to the University of Houston, early diagnosis of SSc is critical but often elusive. Studies have shown that organ involvement could occur far earlier than expected in the early phase of the disease, but early diagnosis and determining the extent of disease progression pose a significant challenge for physicians, resulting in delays in therapy and management.
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