density present less damage in drilling process. The developed numerical model proved to be a great tool in this kind of analysis.

The main goal of this paper is to assess the mechanical damage in solid rigid foam materials with similar mechanical properties to the human bone induced by the cutting parameters. In the present study, a three-dimensional dynamic finite element model was developed to simulate the drilling process in solid rigid foam materials and it was validated with experimental results. Using an explicit dynamic numerical simulation, it is possible to obtain large structural deformation with high load intensity in short time frame. The developed model is used to study the effects of different high intensity loads distribution in the solid rigid foam materials. Laboratory tests were produced using biomechanical test blocks instrumented with strain gauges in different surface positions during the drilling process. The comparison between the numerical and the experimental results enables the evaluation and improvements of the cutting process. It was concluded when the feed-rate is higher, the stresses and strains in the solid rigid foam material are lower. The developed numerical model proved to be a great tool in this kind of analysis and available to use in forthcoming tests.

ObjectiveCapsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.DesignWe developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin’s classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.ConclusionWe developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.