The mechanobiology theory of the development of medical device-related pressure ulcers revealed through a cell-scale computational modeling framework.

Pressure ulcers are localized sites of tissue damage which form due to the continuous exposure of skin and underlying soft tissues to sustained mechanical loading, by bodyweight forces or because a body site is in prolonged contact with an interfacing object. The latter is the common cause for the specific sub-class of pressure ulcers termed 'medical device-related pressure ulcers', where the injury is known to have been caused by a medical device applied for a diagnostic or therapeutic purpose. Etiological research has established three key contributors to pressure ulcer formation, namely direct cell and tissue deformation, inflammatory edema and ischemic damage which are typically activated sequentially to fuel the injury spiral.

Ulcer severity grading in video capsule images of patients with Crohn's disease: an ordinal neural network solution.

Background And Aims: Capsule endoscopy (CE) is an important modality for diagnosis and follow-up of Crohn's disease (CD). The severity of ulcers at endoscopy is significant for predicting the course of CD. Deep learning has been proven accurate in detecting ulcers on CE.