CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. METHODS: To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. RESULTS: CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. CONCLUSIONS: CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

BACKGROUND: High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data. OBJECTIVE: This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images. METHODS: A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database. RESULTS: The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named "Deface program" for medical images as an open-source project. CONCLUSIONS: We developed deep learning-based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.

Double-Crosslinked Polyurethane Acrylate for Highly Conductive and Stable Polymer Electrolyte.

High ionic conductivity and good stability are major factors that influence the use of polymer electrolytes in electrochemical storage and conversion devices. In this study, we present polyurethane acrylate (PUA) membranes having enhanced ionic conductivity and swelling stability by double crosslinking the polyurethane (PU) and polyacrylate (PA) compartments. The crosslinking agent concentration was varied to control their mechanical properties, swelling stability, and ionic conductivity. Under optimum conditions, the electrolyte uptake of the double-crosslinked PUA membranes without notable defects was 245%. The maximum ionic conductivity of these membranes reached 9.6 mS/cm, which was higher than those with respect to most of the previously reported PUA- or PU-based polymer electrolytes.

Surface reaction of LiCoO2/Li system under high-voltage conditions by X-ray spectroscopy and two-dimensional correlation spectroscopy (2D-COS).

We studied the surface reactions of a LiCoO(2)/Li cell under high-voltage conditions using X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and two-dimensional correlation spectroscopy (2D-COS). 2D XPS correlation spectra show that Li(2)CO(3) is formed first by decomposition of the organic solvents, and then polycarbonate, which is formed by polymerization of the electrolytes, is produced on the cathode surface of the LiCoO(2)/Li system under high-voltage conditions. XAS measurements also confirm that the solid electrolyte interface (SEI) layer is formed on the LiCoO(2) electrode by decomposition of the organic solvents. The thickness of the SEI layer is less than 100 Å.