ECG-QA: A Comprehensive Question Answering Dataset Combined With Electrocardiogram

Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datasets primarily focus on medical images, clinical notes, or structured electronic health record tables. This leaves the vast potential of combining electrocardiogram (ECG) data with these systems largely untapped. To address this gap, we present ECG-QA, the first QA dataset specifically designed for ECG analysis. The dataset comprises a total of 70 question templates that cover a wide range of clinically relevant ECG topics, each validated by an ECG expert to ensure their clinical utility. As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs. In addition, we have conducted numerous experiments to provide valuable insights for future research directions. We believe that ECG-QA will serve as a valuable resource for the development of intelligent QA systems capable of assisting clinicians in ECG interpretations.Comment: 39 pages (9 pages for main text, 2 pages for references, 28 pages for supplementary materials

Effect of Three-Dimensional Printed Personalized Moisture Chamber Spectacles on the Periocular Humidity

Purpose. To assess the effect of three-dimensional (3D) printed personalized moisture chamber spectacles (PMCS) on the periocular humidity. Methods. Facial computed tomography (CT) scanning was conducted on 10 normal subjects. PMCS was designed based on volume rendered CT images and produced using a 3D printer. Periocular humidity of PMCS and commercially available uniformed moisture chamber spectacles (UMCS) were measured for 30 minutes via microhydrometer. Results. The mean ambient humidity was 15.76±1.18%. The mean periocular humidity was 52.14±3.00% in PMCS and 37.67±8.97% in UMCS. The difference was significant (P<0.001). Additionally, PMCS always demonstrated lower humidity than dew points. Conclusion. PMCS made by 3D printer provides appropriate fitness for the semiclosed humid chamber. PMCS showed higher performance than UMCS. The wearing of PMCS would be an effective method to provide high enough periocular humidity in low humidity environment

Dual Fistulas of Ascending Aorta and Coronary Artery to Pulmonary Artery

Coronary artery fistula to pulmonary artery is common. However, to the best of our knowledge, a case of coronary artery fistula to pulmonary artery associated with aortopulmonary fistula remains unreported. We herein report a 64-year-old female with a left anterior descending coronary artery and ascending aorta to pulmonary artery fistulas, and conduct a brief review of the literature

Clinical characteristics of myelin oligodendrocyte glycoprotein antibody-associated disease according to their epitopes

BackgroundThe detection of myelin oligodendrocyte glycoprotein autoantibodies (MOG-Ab) is essential for the diagnosis of MOG-Ab-associated disease (MOGAD). The clinical implications of different epitopes recognized by MOG-Ab are largely unknown. In this study, we established an in-house cell-based immunoassay for detecting MOG-Ab epitopes and examined the clinical characteristics of patients with MOG-Ab according to their epitopes.MethodsWe conducted a retrospective review of patients with MOG-Ab-associated disease (MOGAD) in our single center registry, and collected serum samples from enrolled patients. Human MOG variants were generated to detect epitopes recognized by MOG-Ab. The differences in clinical characteristics according to the presence of reactivity to MOG Proline42 (P42) were evaluated.ResultsFifty five patients with MOGAD were enrolled. Optic neuritis was the most common presenting syndrome. The P42 position of MOG was a major epitope of MOG-Ab. The patients with a monophasic clinical course and childhood-onset patients were only observed in the group that showed reactivity to the P42 epitope.ConclusionWe developed an in-house cell-based immunoassay to analyze the epitopes of MOG-Ab. The P42 position of MOG is the primary target of MOG-Ab in Korean patients with MOGAD. Further studies are needed to determine the predictive value of MOG-Ab and its epitopes

New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)

GEMS will monitor air quality over Asia at unprecedented spatial and temporal resolution from GEO for the first time, providing column measurements of aerosol, ozone and their precursors (nitrogen dioxide, sulfur dioxide and formaldehyde). Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in late 2019 - early 2020 to monitor Air Quality (AQ) at an unprecedented spatial and temporal resolution from a Geostationary Earth Orbit (GEO) for the first time. With the development of UV-visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO and aerosols) can be obtained. To date, all the UV-visible satellite missions monitoring air quality have been in Low Earth orbit (LEO), allowing one to two observations per day. With UV-visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be onboard the GEO-KOMPSAT-2 satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager (GOCI)-2. These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA&apos;s TEMPO and ESA&apos;s Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS)

Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG

Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF). Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged &ge;18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG. Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913&ndash;0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively. Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance

Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning

With the wider adoption of electronic health records, the rapid response team initially believed that mortalities could be significantly reduced but due to low accuracy and false alarms, the healthcare system is currently fraught with many challenges. Rule-based methods (e.g., Modified Early Warning Score) and machine learning (e.g., random forest) were proposed as a solution but not effective. In this article, we introduce the DeepEWS (Deep learning based Early Warning Score), which is based on a novel deep learning algorithm. Relative to the standard of care and current solutions in the marketplace, there is high accuracy, and in the clinical setting even when we consider the number of alarms, the accuracy levels are superior

Validation of deep-learning-based triage and acuity score using a large national dataset.

AIM:Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset. METHODS:We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables). RESULTS:A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179). CONCLUSION:Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset

Antibiotic Resistance and Species Profile of Enterococcus Species in Dogs with Chronic Otitis Externa

Simple Summary Otitis externa (OE) is a common disease in dogs and can be induced by various causes. After the primary causes that induced the ear canal issue, microbial infections occur secondly. As the main treatment strategies are primary cause correction and antibiotic administration, prolonged treatment is likely to induce the emergence of antibiotic resistance bacteria. Here, we describe the Enterococcus bacteria, one of the main infection agents of OE. The bacterial genus showed several species distributions and antibiotic resistance. This fact clarifies the importance of appropriate antibiotic selection and prudent antibiotic administration. As companion animals share lots of space with humans, pathogen transmissions between humans and companion animals are likely to occur. This study contributes not only to treatment strategies for Enterococcus infections but can also be used as a comparable index of antibiotic resistance of Enterococcus in the future. Otitis externa, a common disease in dogs, has different etiologies. Enterococcus is a Gram-positive bacterium that frequently causes opportunistic ear infections. Here, we determined the distribution of Enterococcus in canine otitis externa via time-of-flight mass spectrometry and biochemical tests and evaluated their resistance patterns to 10 commonly used antibiotics. Among the 197 Enterococcus isolates, E. faecalis (48.7%; 96/197) was the most common, followed by E. faecium (21.3%; 42/197), E. casseliflavus (11.7%; 23/197), E. hirae (10.7%; 21/197), E. avium (3.6%; 7/197), E. gallinarum (2.5%; 5/197), E. canintestini (1.0%; 2/197), and E. durans (0.5%; 1/197). All isolates were tested for antibiotic resistance using the Kirby-Bauer disc diffusion method. Enterococcus faecalis strains were highly resistant to erythromycin (45.8%) and rifampin (34.3%) but were generally susceptible to penicillin class antibiotics. In contrast, E. faecium isolates were highly resistant to penicillin class antibiotics (ampicillin, 61.9%; penicillin, 71.4%). Most importantly, E. faecium demonstrated high resistance to most of the antibiotics used in this study. Multidrug resistance was found in 28.4% of the isolates (56/197). This study shows prevalence and antibiotics resistance profiles of Enterococcus species in canine chronic otitis externa. The results can contribute to establish therapeutic strategies of Enterococcus infections and be used as a comparable index of antibiotic resistance of Enterococcus in the future.Y