Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5–8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.

Mace M. Schuurmans, Michal Muszynski, Xiang Li, Ricards Marcinkevics, Lukas Zimmerli, Diego Monserrat Lopez, Bruno Michel, Jonas Weiss, Rene Hage, Maurice Roeder, Julia E. Vogt, Thomas Brunschwiler

Medicina

20.03.2023

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Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical surgeries. Previous decision support systems for appendicitis focused on clinical, laboratory, scoring and computed tomography data, mainly ignoring abdominal ultrasound, a noninvasive and readily available diagnostic modality. To this end, we developed and validated interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Our methodological contribution is the generalization of concept bottleneck models to prediction problems with multiple views and incomplete concept sets. Notably, such models lend themselves to interpretation and interaction via high-level concepts understandable to clinicians without sacrificing performance or requiring time-consuming image annotation when deployed.

Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Ece Özkan Elsen, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Christian Knorr, Julia E. Vogt

Arxiv

01.03.2023

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Interpretability and explainability are crucial for machine learning (ML) and statistical applications in medicine, economics, law, and natural sciences and form an essential principle for ML model design and development. Although interpretability and explainability have escaped a precise and universal definition, many models and techniques motivated by these properties have been developed over the last 30 years, with the focus currently shifting toward deep learning. We will consider concrete examples of state-of-the-art, including specially tailored rule-based, sparse, and additive classification models, interpretable representation learning, and methods for explaining black-box models post hoc. The discussion will emphasize the need for and relevance of interpretability and explainability, the divide between them, and the inductive biases behind the presented “zoo” of interpretable models and explanation methods.

Ricards Marcinkevics, Julia E. Vogt

WIREs Data Mining and Knowledge Discovery

28.02.2023

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Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.

Ricards Marcinkevics, Ece Özkan Elsen, Julia E. Vogt

Arxiv

23.12.2022

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Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ejection fraction (EF), which is used to diagnose cardiomyopathy. Echocardiography is a popular diagnostic tool in cardiology, with ultrasound being a low-cost, real-time, and non-ionizing technology. However, human assessment of echocardiograms for calculating EF is both time-consuming and expertise-demanding, raising the need for an automated approach. Earlier automated works have been limited to still images or use echocardiogram videos with spatio-temporal convolutions in a complex pipeline. In this work, we propose to generate images from readily available echocardiogram videos, each image mimicking a M(otion)-mode image from a different scan line through time. We then combine different M-mode images using off-the-shelf model architectures to estimate the EF and, thus, diagnose cardiomyopathy. Our experiments show that our proposed method converges with only ten modes and is comparable to the baseline method while bypassing its cumbersome training process.

Thomas Sutter, Sebastian Balzer, Ece Özkan Elsen, Julia E. Vogt

Medical Imaging Meets NeurIPS Workshop 2022

02.12.2022

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