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. However, endoscopic classification of ulcers by deep learning has not been attempted. The aim of our study was to develop a deep learning algorithm for automated grading of CD ulcers on CE. METHODS: We retrospectively collected CE images of CD ulcers from our CE database. In experiment 1, the severity of each ulcer was graded by 2 capsule readers based on the PillCam CD classification (grades 1-3 from mild to severe), and the inter-reader variability was evaluated. In experiment 2, a consensus reading by 3 capsule readers was used to train an ordinal convolutional neural network (CNN) to automatically grade images of ulcers, and the resulting algorithm was tested against the consensus reading. A pretraining stage included training the network on images of normal mucosa and ulcerated mucosa. RESULTS: Overall, our dataset included 17,640 CE images from 49 patients; 7391 images with mucosal ulcers and 10,249 normal images. A total of 2598 randomly selected pathologic images were further graded from 1 to 3 according to ulcer severity in the 2 different experiments. In experiment 1, overall inter-reader agreement occurred for 31% of the images (345 of 1108) and 76% (752 of 989) for distinction of grades 1 and 3. In experiment 2, the algorithm was trained on 1242 images. It achieved an overall agreement for consensus reading of 67% (166 of 248) and 91% (158 of 173) for distinction of grades 1 and 3. The classification accuracy of the algorithm was 0.91 (95% confidence interval, 0.867-0.954) for grade 1 versus grade 3 ulcers, 0.78 (95% confidence interval, 0.716-0.844) for grade 2 versus grade 3, and 0.624 (95% confidence interval, 0.547-0.701) for grade 1 versus grade 2. CONCLUSIONS: CNN achieved high accuracy in detecting severe CD ulcerations. CNN-assisted CE readings in patients with CD can potentially facilitate and improve diagnosis and monitoring in these patients.

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. Here, we visualize and analyze the above etiological mechanism using a new cell-scale modeling framework. Specifically, we consider here the deformation-inflicted and inflammatory contributors to the damage progression in a medical device-related pressure ulcer scenario, forming under a continuous positive airway pressure ventilation mask at the microarchitecture of the nasal bridge. We demonstrate the detrimental effects of exposure to high-level continuous external strains, which causes deformation-inflicted cell damage almost immediately. This in turn induces localized edema, which exacerbates the cell-scale mechanical loading state and thereby progresses cell damage further in a nonlinear, escalating pattern. The cell-scale quantitative description of the damage cascade provided here is important not only from a basic science perspective, but also for creating awareness among clinicians as well as industry and regulators with regards to the need for improving the design of skin-contacting medical devices.