Open-source Research Platform Research Articles

An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo

Abstract Gain in knowledge in experimental fields of research is mostly achieved using commercial hardware platforms. The linked costs however can limit a broad access by scientists and in the specific case of mechanical ventilation the pandemic demonstrated that further and collective research is essential and necessary. This contribution presents the modular design and implementation of a research platform which is based on STM32 Nucleo boards and Simulink®. The hardware required such as the pneumatic circuit are covered as well as the software architecture in which clinician related settings are processed for control references. The core functionality of mandatory volume controlled (VCV) and pressure controlled ventilation (PCV) while ensuring a positive end-expiratory pressure for the patient ( PEEP ) $(\text{PEEP})$ is evaluated and the foundational software models are published in a repository.

ReAM250 — An open-source research platform for process monitoring and control in Powder Bed Fusion of Metals using a laser beam

Powder bed fusion of metals using a laser beam (PBF-LB/M) allows the additive manufacturing of geometrically complex parts. Current industrial applications are limited to non-safety-critical parts due to the low reproducibility of part properties and the costly quality assurance. Research in process monitoring and control aims to overcome these issues. However, due to the limited accessibility of hardware and software in commercial machines, self-made PBF-LB/M systems have to be constructed. The high implementation effort of these systems acts as an entry barrier for new researchers, and the lack of standardization limits the transferability of results. This work reviewed these setups to formulate requirements for future research systems and accordingly designed a modular, open-source, open-architecture PBF-LB/M system. It facilitates the integration of sensors, and the open-source software provides full control over the process parameters. This offers a solution to enhance the accessibility and reproducibility of research on process control in PBF-LB/M.

Bilateral Cochlear Implant Processing of Coding Strategies With CCi-MOBILE, an Open-Source Research Platform.

While speech understanding for cochlear implant (CI) users in quiet is relatively effective, listeners experience difficulty in identification of speaker and sound location. To assist for better residual hearing abilities and speech intelligibility support, bilateral and bimodal forms of assisted hearing is becoming popular among CI users. Effective bilateral processing calls for testing precise algorithm synchronization and fitting between both left and right ear channels in order to capture interaural time and level difference cues (ITD and ILDs). This work demonstrates bilateral implant algorithm processing using a custom-made CI research platform - CCi-MOBILE, which is capable of capturing precise source localization information and supports researchers in testing bilateral CI processing in real-time naturalistic environments. Simulation-based, objective, and subjective testing has been performed to validate the accuracy of the platform. The subjective test results produced an RMS error of ±8.66° for source localization, which is comparable to the performance of commercial CI processors.

Bimodal Cochlear Implant Processing based on Assisted Hearing algorithms with CCi-MOBILE: an open-source research platform.

While speech understanding for cochlear implant (CI) and Hearing aid (HA) users in quiet is relatively effective, listeners experience difficulty in identification of speakers and sound location. Previous studies have reported improved localization and better speech perception when the unilateral CI is coupled with a HA in the contralateral ear. This is referred to as bimodal presentation of speech or electric and acoustic stimulation (EAS). Various CI research interfaces developed by either academic or industry sponsored research teams support proposed signal processing and psychoacoustic investigations but have limited ability to efficiently validate bimodal algorithms. Platforms that support bimodal testing (CI and HA) are either not portable or only provide limited features due to proprietary parameters/routines. CCi-MOBILE, an open-source, portable signal processing research device, developed by UT-Dallas, enables electric and acoustic stimulations simultaneously, thus providing researchers the opportunity to explore new technology and scientific paradigms for the hearing impaired. In the present work, we provide verification and implementation of synchronized bimodal (electric-acoustic) output in an authenticated and efficient manner to support sound and acoustic localization algorithms for experimental investigations.

Abstract LB149: Luna Pathology: An integrated open source platform for computational pathology research

Abstract Pathologic review of tissue samples is a crucial step in cancer diagnosis and treatment planning. In recent years, quantitative analysis, including artificial intelligence (AI) techniques, have been applied to facilitate the evaluation of histopathologic images. Research in computational pathology comes with numerous engineering challenges: from management of whole slide images (WSIs) and their metadata, collection of expert annotations, data manipulation and feature extraction, to AI model training and evaluation of the results. These challenges span multiple departments and sets of expertise; there is a lack of an efficient system that helps manage and process histologic images and supports AI analysis. In particular, computational oncology requires significant compute resources. The need for an integrated platform with systematic processes to manage the job parameters and complex workflows that interface with multidisciplinary teams is critical for successful experiments and reproducible research. To address these challenges, we developed Luna Pathology, an open-source library and platform for computational pathology research. With flexibility, scalability, and FAIR data principles in mind, Luna Pathology was designed to provide 1) reusable workflows and modular tools for end-to-end pathology analysis 2) FAIR datasets for reproducible research and 3) a collaborative open-source research platform. Namely, Luna Pathology features support data management, annotation support, image processing, image and annotation visualization, feature extraction, and human-in-the-loop machine learning. A description of the Luna Pathology platform, its supporting computational infrastructure, and its integration within the standard computational pathology will be presented. Luna Pathology meets data processing and management needs, as well data analysis experiments and reproducible AI research on a flexible data platform. Its workflows have been deployed in colorectal, neuroblastoma and ovarian cancer projects at Memorial Sloan Kettering Cancer Center (MSK) to derive new insights from ~10,000 WSIs. We additionally present a view of the Luna Pathology platform as part of the institutional effort to bridge computational research and clinical care at MSK. The integrated open platform brings together a multidisciplinary team of pathologists, computational biologists, and bioinformatics engineers to more effectively collaborate on iterative large-scale computational pathology research. Citation Format: Doori Rose, Andrew Aukerman, Druv Patel, Arfath Pasha, Armaan Kohli, Ignacio Vázquez-García, Kevin Boehm, Rami Vanguri, Matthew Shabet, Vassiliki Mancoridis, Elizabeth Zakszewski, Benjamin Gross, Christopher Fong, Pegah Khosravi, Sohrab Shah, JianJiong Gao. Luna Pathology: An integrated open source platform for computational pathology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB149.

MACHINE LEARNING AND RADIOMICS IDENTIFY NOVEL BIOMARKERS OF BONE LOSS

Purpose: Altered bone turnover is a factor in many diseases including osteoarthritis (OA), osteoporosis, inflammation, and viral infection. The absence of obvious symptoms and insufficiently sensitive biomarkers in the early stages of bone loss limits early diagnosis and treatment. Therefore, it is urgent to identify novel, more sensitive, and easy-to-detect biomarkers which can be used in the diagnosis and prognosis of bone health. Our previous data using standard micro-computed tomography (μCT) measurements showed that SARS-CoV-2 infection in mice significantly decreased trabecular bone volume at the lumbar spine, suggesting that decreased bone mass, increased fracture risk, and OA may be underappreciated long-haul comorbidities for COVID patients. In this study, we applied integrated state-of-the-art radiomics and machine learning models to identify more sensitive image-based biomarkers of SARS-CoV-2-induced bone loss from μCT images. These radiomic biomarkers can potentially provide a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy in both research and clinical practices. Methods: All animal use was performed with approval of the Institutional Animal Care and Use Committee. To quantify SARS-CoV-2-induced bone loss, 6-week-old transgenic mice (16 male, 16 female) expressing humanized ACE2 receptors were inoculated with a 2020 strain of SARS-CoV-2 or phosphate-buffered saline (Control) [Fig. A]. Viral infection was confirmed by detection of infectious SARS-CoV-2 in throat swabs and histological identification of SARS-CoV-2 labeled cells. At 6-14 days post-infection, lumbar vertebral bodies (L5) were scanned with μCT (μCT 35, SCANCO Medical AG; 6 μm nominal voxel size). The open-source research platform 3D Slicer v2020 with a built-in Python console v3.8 was used for medical image computing and fully automated segmentation of cortical and trabecular bone. Standard μCT assessment of bone microstructure was performed. Radiomic feature extraction and data processing were performed using python based PyRadiomics v3.0.1. A total of 120 radiographic features were extracted from the segmented images [Fig. B]. Principle component analysis ( PCA ) for feature selection, a support vector machine learning (SVML) predictive model for classification, holdback method for model validation, and all statistical analyses (significance at p<0.05) were performed using JMP Pro v15 ( SAS ). Results: Using standard μCT methods, SARS-CoV-2 infection significantly reduced the bone volume fraction (BV/TV) by 10 and 10.5% (p= 0.04) and trabecular thickness (Tb.Th) by 8 and 9% (p= 0.02) in male and female mice, respectively, compared to PBS control mice [Fig. C]. Radiomics detected a 20-fold greater magnitude in change over standard methods. SARS-CoV-2 infection significantly changed radiographic parameters with the largest change being a 300% increase in the second-order parameter: cluster shade [Fig. D]. The 45 radiomic features comprising the first 3 principal components were selected for inclusion in the SVML model. The SVML Model (radial basis function kernel; cost = 4.8; gamma = 0.46) produced an area under the receiver operating characteristic curve (AUC) of 1.0 which reflects a perfectly accurate test [Fig. E]. Conclusions: SARS-CoV-2 infection of humanized ACE2 expressing mice caused significant bone changes, suggesting that decreased bone mass, increased fracture risk, OA, and other musculoskeletal complications could be long-term comorbidities for people infected with COVID-19. We developed an open-source, fully automated segmentation and radiomics system to assess systemic bone loss using μCT images. When coupled with machine learning, this system was able to identify novel radiographic biomarkers of bone loss that better discriminate differences in bone microstructure between SARS-CoV-2 infected and control mice than standard bone morphometric indices. The high accuracy of the SVML model in classifying SARS-CoV-2 infected mice opens the possibility of translating these biomarkers to the clinical setting for early detection of skeletal changes associated with long-haul COVID. The methods presented here were demonstrated using SARS-CoV-2 as a model system and can also be adapted to other diseases associated with altered bone turnover. Development of machine-learning methods for radiomic applications is a crucial step toward clinically relevant radiomic biomarkers of bone health and provides a non-invasive way of quantifying and monitoring systemic bone loss and evaluating treatment efficacy.

FaceBit

The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most effective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper fit or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by needfinding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-fit and wear time from pressure signals, all on-device with an energy-efficient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond.

Directionality characteristics of the Tympan open-source hearing aid and earpieces

Directional systems in hearing aids are critical for improving speech understanding in the presence of background noise and competing talkers. Tympan is an open-source research platform for speech processing and hearing improvement. The latest Tympan version (Rev E) includes earpieces with a front and a rear microphone, allowing researchers to explore the effects of different directional settings using a realistic hearing aid configuration. The directionality characteristics of the Tympan earpieces were evaluated using a KEMAR in a semi-reverberant sound field. To demonstrate the functionality of Tympan’s directionality features, different software settings were manipulated, including the relative delays and gains between the two microphones. Results will be discussed along with considerations for more advanced directional solutions, such as automatic directional switching, adaptive directivity patterns, binaural signal processing, and beamforming.

Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware

PurposeImage-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery.MethodsWe established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system’s ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model.ResultsExperimental validation of the system’s navigated ultrasound component demonstrated accuracy of <4.5,hbox {mm} and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of <15,hbox {min} and performed with a clinically acceptable level of accuracy.ConclusionWe present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

Towards Interoperability in Clinical Research - Enabling FHIR on the Open-Source Research Platform XNAT

This paper presents an approach to enable interoperability of the research data management system XNAT by the implementation of the HL7 standards framework Fast Healthcare Interoperability Resources (FHIR). The FHIR implementation is realized as an XNAT plugin (Source code: https://github.com/somnonetz/xnat-fhir-plugin), that allows easy adoption in arbitrary XNAT instances. The approach is demonstrated on patient data exchange between a FHIR reference implementation and XNAT.

CartAGen: an Open Source Research Platform for Map Generalization

Abstract. Automatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen.

Integration of signal processing modules of hearing aids as real-time smartphone apps

Our research group has been working on developing an open source research platform for hearing improvement studies based on smartphones noting that currently no open source, programmable, and mobile research platform exists in the public domain for carrying out hearing improvement studies. Such a platform allows smartphones, which are already widely possessed and carried by people, to be used for the development and field testing of existing or new hearing improvement signal processing algorithms. A number of smartphones apps have already been developed by our research group for specific or individual modules of the speech processing pipeline of a typical digital hearing aid. This work covers the integration of several individual signal processing modules into an integrated smartphone app. The steps taken in order to enable the individual modules to run together in real-time and with low audio latency will be presented and a demo of the integrated app running on both iOS and Android smartphones will be shown.

Smartphone as a research platform for hearing study and hearing aid applications

Smartphones are widely available and used by many people. Portability, processing power and useful features of smartphone, such as audio input/output and wire/wireless connectivity, offer unique opportunity to use smartphone as an open source research platform capable of implementing in real time many computationally intensive adaptive signal processing algorithms aimed at improving hearing study and hearing aid applications. In this paper, we present an overview of several adaptive algorithms developed for classifying the background noise, suppressing the noise, and enhancing the speech in different noisy environments. The proposed algorithms for hearing aid applications can run on existing smartphones such as the Android-based and iOS-based smartphones in real time. Objective and subjective test results are presented illustrating the performance of proposed algorithms running on smartphones in real time.

CustusX: an open-source research platform for image-guided therapy.

PurposeCustusX is an image-guided therapy (IGT) research platform dedicated to intraoperative navigation and ultrasound imaging. In this paper, we present CustusX as a robust, accurate, and extensible platform with full access to data and algorithms and show examples of application in technological and clinical IGT research.MethodsCustusX has been developed continuously for more than 15 years based on requirements from clinical and technological researchers within the framework of a well-defined software quality process. The platform was designed as a layered architecture with plugins based on the CTK/OSGi framework, a superbuild that manages dependencies and features supporting the IGT workflow. We describe the use of the system in several different clinical settings and characterize major aspects of the system such as accuracy, frame rate, and latency.ResultsThe validation experiments show a navigation system accuracy of <1.1 mm, a frame rate of 20 fps, and latency of 285 ms for a typical setup. The current platform is extensible, user-friendly and has a streamlined architecture and quality process. CustusX has successfully been used for IGT research in neurosurgery, laparoscopic surgery, vascular surgery, and bronchoscopy.ConclusionsCustusX is now a mature research platform for intraoperative navigation and ultrasound imaging and is ready for use by the IGT research community. CustusX is open-source and freely available at http://www.custusx.org.

An open source research platform for embedded visible light networking

Despite the growing interest in visible light communication, a reference networking platform based on commercial off-the-shelf components is not available yet. An open source platform would lower the barriers to entry of VLC network research and help the VLC community gain momentum. We introduce OpenVLC, an open source VLC research platform based on software-defined implementation. Built around a creditcard- sized embedded Linux platform with a simple opto-electronic transceiver front-end, OpenVLC offers a basic physical layer, a set of essential medium access primitives, as well as interoperability with Internet protocols. We investigate the performance of OpenVLC and show examples of how it can be used along with standard network diagnostics tools. Our software-defined implementation can currently reach throughput on the order of the basic rate of the IEEE 802.15.7 standard. We discuss several techniques that researchers and engineers could introduce to improve the performance of OpenVLC and envision several directions that can benefit from OpenVLC by adopting it as a reference platform.

The visualFields package: A tool for analysis and visualization of visual fields

This paper introduces the R package visualFields, a contributed, open-source software for the analysis of the visual field. The package aims to provide a framework for collaborative research, including data sharing and conventional and novel methods. Single visual field and progression analyses, such as Permutation of Pointwise Linear Regression can be performed with visualFields using simple scripts. The package can be easily customized and it allows the inclusion of custom test locations and different normative values. Here, we demonstrate how to use the visualFields package and discuss its capabilities. The analyses presented here are easy to replicate upon installation of the package, which is freely available for download from the Comprehensive R Archive Network. The relevant R code is shown and commented on. A shift from proprietary to an open-source research platform is an important step towards more direct collaborative research. The visualFields package is part of the Open Perimetry Initiative, which is expected to grow as researchers contribute new routines and datasets.