Description

Intensive Care Unit-Acquired Weakness (ICUAW) is one of the most common neuromuscular complications in patients treated in intensive care. With increasing disease severity and especially in analgosedated, ventilated and delirious patients with limited ability to cooperate during the clinical examination, the detection and follow-up of ICUAW is limited to impossible. The clinical diagnosis and severity assessment of ICUAW is usually carried out with the help of established diagnostic methods (e.g. clinical-neurological examination, Medical Research Council-Sum Score, electrophysiological examinations), which, however, cannot be carried out regularly if the patient does not cooperate, thus delaying the diagnosis of ICUAW and making follow-up more difficult. Neuromuscular ultrasound (NMUS), on the other hand, is an easy-to-use, non-invasive examination option that is largely independent of patient compliance and is increasingly being investigated in patients with ICUAW. It was shown that NMUS can detect ICUAW and is helpful in assessing the severity of muscular weakness. However, the standardized recording and follow-up by means of scoring procedures (e.g. the 4-stage Heckmatt Scala) is assessed as partially subjective by the examiner and each individual ultrasound image must be taken with the human eye, taking into account various image parameters. To overcome these diagnostic limitations, artificial intelligence (AI) could be a useful extension or even an alternative.

AI is already being used in a variety of ways in medical diagnostics (e.g. in the detection of tumors and organ assessment), and increasingly also in the analysis of ultrasound images. In this study, the investigators aim to use AI, specifically Convolutional Neural Networks (CNNs), to classify ultrasound images into different categories based on muscle weakness. The main benefit of using AI for such tasks lies in the automation it provides. Once an AI model has been trained on an initial set of images, it can quickly categorize new, unseen images, significantly reducing the time and human effort required for diagnosis. AI models can analyze large amounts of data quickly and consistently, which is especially beneficial in a clinical intensive care setting. By applying AI, this study aims to train the detection and classification of muscle weakness in patients treated in intensive care. However, one challenge with AI models is their "black box" nature, where the decision-making process is not transparent. To solve this problem, the investigators will use explainable AI techniques (XAI) such as Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize the specific areas of the ultrasound images that the AI model focuses on in its analysis. This not only helps validate the AI decisions, but also provides insights into the morphological changes in the muscles that come with different degrees of weakness.

By integrating AI and XAI, the study team aims to not only automate the detection and categorization of muscle weakness, but also improve our understanding of the underlying morphological changes in muscles. This dual approach could lead to more accurate and reliable diagnostics and ultimately improve outcomes for patients in intensive care.