Applied machine learning and artificial intelligence in rheumatology.

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient's opinion and the rheumatologist's empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.

Reduction in ulnar pressure distribution when walking with forearm crutches with a novel cuff design: Cross-sectional intervention study on the biomechanical efficacy of an ulnar recess.

Walking with crutches is an effective way of reducing the load on the lower extremity and is often indicated after injury or surgery. However, walking with forearm crutches with conventional cuffs can trigger symptoms including tenosynovitis in the biceps tendon, ulnar neuropraxia at the wrist, pain, or skin hematoma. The purpose of this study was to test the hypothesis that a crutch cuff design with an ulnar recess reduces ulnar pressure during walking with forearm crutches. The pressure distribution between the forearm and crutch cuff was measured in 15 healthy participants for crutch walking with conventional and novel cuffs, respectively. Relative peak pressure in the proximal medial region compared to the overall peak pressure was reduced by 8.6% when walking with crutches with the novel cuff design compared to conventional cuffs (p < 0.001). Relative peak pressure in the distal intermediate and lateral regions were increased by 3.3% and 3.7% for the novel compared with conventional cuffs, respectively (p < 0.001 for both). Hence, the novel crutch cuffs shifted regions of high pressure away from the proximal ulnar region towards more distal regions that are covered by more soft tissue.

Hardware Implementation of a Performance and Energy-optimized Convolutional Neural Network for Seizure Detection.

We present for the first time a µW-power convolutional neural network for seizure detection running on a low-power microcontroller. On a dataset of 22 patients a median sensitivity of 100% is achieved. With a false positive rate of 20.7 fp/h and a short detection delay of 3.4 s it is suitable for the application in an implantable closed-loop device.