Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dataset consisting of radiological images and associated medical concepts and captions extracted from the PMC Open Access subset. It is an updated version of the ROCO dataset published in 2018, and adds 35,705 new images added to PMC since 2018. It further provides manually curated concepts for imaging modalities with additional anatomical and directional concepts for X-rays. The dataset consists of 79,789 images and has been used, with minor modifications, in the concept detection and caption prediction tasks of ImageCLEFmedical Caption 2023. The dataset is suitable for training image annotation models based on image-caption pairs, or for multi-label image classification using Unified Medical Language System (UMLS) concepts provided with each image. In addition, it can serve for pre-training of medical domain models, and evaluation of deep learning models for multi-task learning.

Identifying nanomaterials (NMs) according to European Union legislation is challenging, as there is an enormous variety of materials, with different physico‐chemical properties. The NanoDefiner Framework and its Decision Support Flow Scheme (DSFS) allow choosing the optimal method to measure the particle size distribution by matching the material properties and the performance of the particular measurement techniques. The DSFS leads to a reliable and economic decision whether a material is an NM or not based on scientific criteria and respecting regulatory requirements. The DSFS starts beyond regulatory requirements by identifying non‐NMs by a proxy approach based on their volume‐specific surface area. In a second step, it identifies NMs. The DSFS is tested on real‐world materials and is implemented in an e‐tool. The DSFS is compared with a decision flowchart of the European Commission’s (EC) Joint Research Centre (JRC), which rigorously follows the explicit criteria of the EC NM definition with the focus on identifying NMs, and non‐NMs are identified by exclusion. The two approaches build on the same scientific basis and measurement methods, but start from opposite ends: the JRC Flowchart starts by identifying NMs, whereas the NanoDefiner Framework first identifies non‐NMs.

Tracking of medical devices can be used in diverse situations, e.g., training as well as image guidance for surgery and surgery planning. Therefore, position and orientation of a device, for instance, an ultrasound probe, need to be identified as precisely as possible. This enables correct representation of digital 3D models in medical image processing platforms such as 3D Slicer or MevisLab. In this manuscript, a comparative evaluation of the low-cost Swept Angle Laser Tracking (SALT) system SteamVR Tracking and the multi-camera-based Opti-Track System is presented. Their potential for medical device tracking is demonstrated in the use case of ultrasound probe tracking for simulation purposes. An evaluation of tracking errors is performed using a Universal Robotics UR5 industrial robot under non-laboratory conditions, involving common issues such as reflections and occlusions. A discussion on the tracking accuracy of both systems is given. The communication of tracking data is established for 3D Slicer and MeVisLab with the use of the PLUS Toolkit via the OpenIGTLink protocol.

The tracking of objects and person position, orientation, and movement is relevant for various medical use cases, e.g., practical training of medical staff or patient rehabilitation. However, these demand high tracking accuracy and occlusion robustness. Expensive professional tracking systems fulfill these demands, however, cost-efficient and potentially adequate alternatives can be found in the gaming industry, e.g., SteamVR Tracking. This work presents an evaluation of SteamVR Tracking in its latest version 2.0 in two experimental setups, involving two and four base stations. Tracking accuracy, both static and dynamic, and occlusion robustness are investigated using a VIVE Tracker (3.0). A dynamic analysis further compares three different velocities. An error evaluation is performed using a Universal Robots UR10 robotic arm as ground-truth system under nonlaboratory conditions. Results are presented using the Root Mean Square Error. For static experiments, tracking errors in the submillimeter and subdegree range are achieved by both setups. Dynamic experiments achieved errors in the submillimeter range as well, yet tracking accuracy suffers from increasing velocity. Four base stations enable generally higher accuracy and robustness, especially in the dynamic experiments. Both setups enable adequate accuracy for diverse medical use cases. However, use cases demanding very high accuracy should primarily rely on SteamVR Tracking 2.0 with four base stations.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The importance of mobile apps is growing. Independent of the technologies used, mobile apps bring more functionality into the hands of users. In the health context, mobile apps play an important role in providing information and services to patients, offering healthcare professionals ways to monitor vital parameters or consult patients remotely. The importance of confidentiality in healthcare and the opaqueness of transport security in apps make the latter an important research subject. Objective: The objectives of this study were as follows: (1) identification of relevant security concerns on the server side of mobile apps, (2) testing a subset of mobile apps regarding their vulnerability to those concerns, and (3) comparing the servers used by mHealth apps with servers used in all domains. Methods: Server-Security characteristics relevant to the security of mHealth apps were assessed, presented, and discussed. To evaluate servers, appropriate tools were selected. Apps from the Android and iOS app stores were selected, tested and the results for functional and other backend servers evaluated. Results: The 60 apps tested communicate with 823 servers. Of these, 291 were categorized as functional backend servers and 44 (15%) of these received a rating below the A range (A+, A, and A-) by Qualys SSL Labs. A chi-square test was conducted against the number of servers receiving such ratings from SSL Pulse by Qualys SSL Labs. It was found that the tested servers from mHealth apps received significantly fewer ratings below the A range (P < 0.001). The internationally available apps from the test set performed significantly better than those only available in the German stores (α=0.05, p=0.03). A total of 28 (47%) of 60 apps were found using at least one functional backend server that received a rating below the A range from Qualys SSL Labs, endangering confidentiality, authenticity and integrity of the data displayed. The number of apps that utilized at least one entirely unsecured connection was 20 (33%) when communicating with functional backend servers. It was also found that a majority of apps used ad, tracking, or external content provider servers. When looking at all non-functional backend servers, 48 (80%) of the apps used at least one server that received a rating below the A range. Conclusions: The results show, that while servers in the mHealth domain perform significantly better regarding their security, there are still problems with the configuration of some. The most severe problems observed can expose patient communication with healthcare professionals, be exploited to display false or harmful information, or used to send data to an app facilitating further damage on the device. Following the recommendations for mHealth app developers the most regularly observed security issues can be avoided or mitigated.