  Pressure ulcers are a challenge for patients and healthcare professionals. In
the UK, 700,000 people are affected by pressure ulcers each year. Treating them
costs the National Health Service {\pounds}3.8 million every day. Their
etiology is complex and multifactorial. However, evidence has shown a strong
link between old age, disease-related sedentary lifestyles and unhealthy eating
habits. Pressure ulcers are caused by direct skin contact with a bed or chair
without frequent position changes. Urinary and faecal incontinence, diabetes,
and injuries that restrict body position and nutrition are also known risk
factors. Guidelines and treatments exist but their implementation and success
vary across different healthcare settings. This is primarily because healthcare
practitioners have a) minimal experience in dealing with pressure ulcers, and
b) a general lack of understanding of pressure ulcer treatments. Poorly
managed, pressure ulcers lead to severe pain, poor quality of life, and
significant healthcare costs. In this paper, we report the findings of a
clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the
performance of a faster region-based convolutional neural network and mobile
platform that categorised and documented pressure ulcers. The neural network
classifies category I, II, III, and IV pressure ulcers, deep tissue injuries,
and unstageable pressure ulcers. Photographs of pressure ulcers taken by
district nurses are transmitted over 4/5G communications to an inferencing
server for classification. Classified images are stored and reviewed to assess
the model's predictions and relevance as a tool for clinical decision making
and standardised reporting. The results from the study generated a mean average
Precision=0.6796, Recall=0.6997, F1-Score=0.6786 with 45 false positives using
an @.75 confidence score threshold.
