Artificial intelligence-based classification of motor unit action potentials in real-world needle EMG recordings.

Objective: To develop an artificial neural network (ANN) for classification of motor unit action potential (MUAP) duration in real-word, unselected and uncleaned needle electromyography (n-EMG) recordings.

Methods: Two nested ANN models were trained, the first discerning muscle rest, contraction and artifacts in n-EMG recordings from 2674 individual muscles from 326 patients obtained as part of daily care. The second ANN model subsequently used segments labeled as contraction for prediction of prolonged, normal and shortened MUAPs.

Radiology report generation for proximal femur fractures using deep classification and language generation models.

Proximal femur fractures represent a major health concern, and substantially contribute to the morbidity of elderly. Correct classification and diagnosis of hip fractures has a significant impact on mortality, costs and hospital stay. In this paper, we present a method and empirical validation for automatic subclassification of proximal femur fractures and Dutch radiological report generation that does not rely on manually curated data.