The Approach to Sensing the True Fetal Heart Rate for CTG Monitoring: An Evaluation of Effectiveness of Deep Learning with Doppler Ultrasound Signals.

Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.

Extracting fetal heart signals from Doppler using semi-supervised convolutional neural networks.

Cardiotocography (CTG) measurements are critical for assessing fetal wellbeing during monitoring, and accurate assessment requires well-traceable CTG signals. The current FHR calculation algorithm, based on autocorrelation to Doppler ultrasound (DUS) signals, often results in periods of loss owing to its inability to differentiate signals. We hypothesized that classifying DUS signals by type could be a solution and proposed that an artificial intelligence (AI)-based approach could be used for classification. However, limited studies have incorporated the use of AI for DUS signals because of the limited data availability. Therefore, this study focused on evaluating the effectiveness of semi-supervised learning in enhancing classification accuracy, even in limited datasets, for DUS signals. Data comprising fetal heartbeat, artifacts, and two other categories were created from non-stress tests and labor DUS signals. With labeled and unlabeled data totaling 9,600 and 48,000 data points, respectively, the semi-supervised learning model consistently outperformed the supervised learning model, achieving an average classification accuracy of 80.9%. The preliminary findings indicate that applying semi-supervised learning to the development of AI models using DUS signals can achieve high generalization accuracy and reduce the effort. This approach may enhance the quality of fetal monitoring.

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

The Usefulness of the Breast Density Assessment Application Used by Breast Radiologists.

Breast density determined by breast radiologists and also automatically estimated by applications has been widely investigated. However, no study has yet clarified whether the use of these applications by breast radiologists improves reading efficacy. Therefore, this study aimed to assess the usefulness of applications when used by breast radiologists. A Breast Density Assessment application (App) developed by Konica Minolta, Inc. (Tokyo, Japan) was used. Independent and sequential tests were conducted to assess the usefulness of the concurrent- and second-look modes. Fifty and 100 cases were evaluated using sequential and independent tests, respectively. Each dataset was configured based on the evaluation by an expert breast radiologist who developed the Japanese guidelines for breast density. Nine breast radiologists evaluated the mammary gland content ratio and breast density; the inter-observer and expert-to-observer variability were calculated. The time required to complete the experiments was also recorded. The inter-observer variability was significant with the App, as revealed by the independent test. The use of the App significantly improved the agreement between the responses of the observers for the mammary gland content ratio and those of the expert by 6.6% and led to a reduction of 186.9 seconds in the average time required by the observers to evaluate 100 cases. However, the results of the sequential test did not suggest the effectiveness of the App. These findings suggest that the concurrent use of the App improves reading efficiency.

Development of a Subtraction Processing Technology for Assistance in the Comparative Interpretation of Mammograms.

A comparative interpretation of mammograms has become increasingly important, and it is crucial to develop subtraction processing and registration methods for mammograms. However, nonrigid image registration has seldom been applied to subjects constructed with soft tissue only, such as mammograms. We examined whether subtraction processing for the comparative interpretation of mammograms can be performed using nonrigid image registration. As a preliminary study, we evaluated the results of subtraction processing by applying nonrigid image registration to normal mammograms, assuming a comparative interpretation between the left and right breasts. Mediolateral-oblique-view mammograms were taken from noncancer patients and divided into 1000 cases for training, 100 cases for validation, and 500 cases for testing. Nonrigid image registration was applied to align the horizontally flipped left-breast mammogram with the right one. We compared the sum of absolute differences (SAD) of the difference of bilateral images (Difference Image) with and without the application of nonrigid image registration. Statistically, the average SAD was significantly lower with the application of nonrigid image registration than without it (without: 0.0692; with: 0.0549 (p < 0.001)). In four subgroups using the breast area, breast density, compressed breast thickness, and Difference Image without nonrigid image registration, the average SAD of the Difference Image was also significantly lower with nonrigid image registration than without it (p < 0.001). Nonrigid image registration was found to be sufficiently useful in aligning bilateral mammograms, and it is expected to be an important tool in the development of a support system for the comparative interpretation of mammograms.

Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence.

Background: Mammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT). Methods: We used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles. Results: The average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40-49, 50-59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40-49, 50-59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40-59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36. Conclusion: The study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40-59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.

Domain generalization across tumor types, laboratories, and species - Insights from the 2022 edition of the Mitosis Domain Generalization Challenge.

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories. This observation motivated the inception of the 2022 challenge on MItosis Domain Generalization (MIDOG 2022). The challenge provided annotated histologic tumor images from six different domains and evaluated the algorithmic approaches for mitotic figure detection provided by nine challenge participants on ten independent domains. Ground truth for mitotic figure detection was established in two ways: a three-expert majority vote and an independent, immunohistochemistry-assisted set of labels. This work represents an overview of the challenge tasks, the algorithmic strategies employed by the participants, and potential factors contributing to their success. With an F1 score of 0.764 for the top-performing team, we summarize that domain generalization across various tumor domains is possible with today's deep learning-based recognition pipelines. However, we also found that domain characteristics not present in the training set (feline as new species, spindle cell shape as new morphology and a new scanner) led to small but significant decreases in performance. When assessed against the immunohistochemistry-assisted reference standard, all methods resulted in reduced recall scores, with only minor changes in the order of participants in the ranking.

Spirometry test values can be estimated from a single chest radiograph.

Introduction: Physical measurements of expiratory flow volume and speed can be obtained using spirometry. These measurements have been used for the diagnosis and risk assessment of chronic obstructive pulmonary disease and play a crucial role in delivering early care. However, spirometry is not performed frequently in routine clinical practice, thereby hindering the early detection of pulmonary function impairment. Chest radiographs (CXRs), though acquired frequently, are not used to measure pulmonary functional information. This study aimed to evaluate whether spirometry parameters can be estimated accurately from single frontal CXR without image findings using deep learning. Methods: Forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC as spirometry measurements as well as the corresponding chest radiographs of 11,837 participants were used in this study. The data were randomly allocated to the training, validation, and evaluation datasets at an 8:1:1 ratio. A deep learning network was pretrained using ImageNet. The input and output information were CXRs and spirometry test values, respectively. The training and evaluation of the deep learning network were performed separately for each parameter. The mean absolute error rate (MAPE) and Pearson's correlation coefficient (r) were used as the evaluation indices. Results: The MAPEs between the spirometry measurements and AI estimates for FVC, FEV1 and FEV1/FVC were 7.59% (r = 0.910), 9.06% (r = 0.879) and 5.21% (r = 0.522), respectively. A strong positive correlation was observed between the measured and predicted indices of FVC and FEV1. The average accuracy of >90% was obtained in each estimation of spirometry indices. Bland-Altman analysis revealed good agreement between the estimated and measured values for FVC and FEV1. Discussion: Frontal CXRs contain information related to pulmonary function, and AI estimation performed using frontal CXRs without image findings could accurately estimate spirometry values. The network proposed for estimating pulmonary function in this study could serve as a recommendation for performing spirometry or as an alternative method, suggesting its utility.

Impact of renal function on adverse bleeding events associated with dual antiplatelet therapy in patients with acute coronary syndrome.

It is believed, but not well established, that renal dysfunction increases the risk of adverse bleeding events associated with dual antiplatelet therapy (DAPT), especially in patients with acute coronary syndrome (ACS). The aim of this study is to estimate the impact of renal function on adverse bleeding events associated with DAPT in patients with ACS. A total of 1,264 ACS patients who received DAPT, clopidogrel (n = 530) or prasugrel (n = 734) in addition to aspirin, were assessed in a multicenter observational study. The relationship between renal function and bleeding event, defined as BARC 3 or 5, was determined using a marginal effect from the logit model and Royston-Parmar model. During an average 313.1 days of the observation period, defined as the duration of DAPT after admission until the implementation of a change in the regimen, bleeding events were observed in 7.4% of patients (n = 94). The estimated curves demonstrated that the probability of bleeding was positive correlated with renal dysfunction (6.0 to 8.6), regardless of the DAPT regimen used. This probability was consistently higher in clopidogrel (7.4 to 10.5) than in prasugrel (4.8 to 0.7). This trend was also shown in maintenance hemodialysis patients (6.7 vs. 10.3). Estimated cumulative incidences among individual stages of renal function were drawn. In conclusion, bleeding events increased with worsening renal function, and prasugrel is safer than clopidogrel as a component of DAPT throughout all levels of renal function, including hemodialysis patients after ACS.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

Identification of areas of the brain activated by active stimulation in hairless skin.

In the past few decades, neuroscientists have studied the physiological basis of pleasant touch. Unmyelinated low-threshold mechanoreceptors are central to the study of the physiological basis of pleasant touch. Research on pleasant stimuli has mostly focused on passive stimuli, and the brain activation sites for active pleasant stimuli are not clear. Therefore, the purpose of this study was to identify brain activation sites during active pleasant stimulation of hairless skin using functional magnetic resonance imaging. Forty-two healthy subjects aged 19 years or older were asked to actively grasp in five stimulus tasks. The comfort and sensations that occurred during the tasks were investigated using a questionnaire. Significant activation was found in the middle frontal gyrus when the hair ball and slime ball were grasped, while there was significant activation in the amygdala when grasping a squeeze ball compared to the tennis ball. In a questionnaire survey of the subjects, there was a significant difference in the comfort score between the tennis ball and the squeeze ball, but no significant correlation was found between the comfort scores and the brain sites of activation. Therefore, although active stimulation with the squeeze ball significantly activated the amygdala, it was not clear that the amygdala was significantly activated by active pleasant stimulation. In the future, it will be necessary to investigate the texture of the squeeze ball in more detail, and to increase the number of subjects for further study.

The relaxation effect of autonomous sensory meridian response depends on personal preference.

Background: Autonomous sensory meridian response (ASMR) is a sensory response such as tingling and pleasantness from audiovisual stimuli. ASMR videos come in a wide variety of types, and personal preferences are biased. There are many reports of the effects os ASMR on sleep onset, anxiety relief, and other relaxation effects. However, prior task-oriented studies have used ASMR videos provided by the experimenter. We hypothesized that ASMR movies of a personal preference would show significantly increased activity in the nucleus accumbens, frontal cortex, and insular cortex, which are brain areas associated with relaxation. Therefore, the purpose of this study was to elucidate the neuroscientific basis for the relaxation effects of ASMR videos that match someone's personal preferences. Methods: This study included 30 healthy individuals aged ≥18 years. ASMR enthusiasts were included as the target population due to the need to have a clear preference for ASMR videos. A control video (1 type) and ASMR videos (20 types) were used as the stimulus tasks. Among the ASMR videos, those with high and low evaluation scores were considered liked and dislikedASMR videos, respectively. Functional magnetic resonance imaging was performed while the participants viewed a block design with a resting task in between. The data were analyzed using Statistical Parametric Mapping 12 to identify the areas activated by control, disliked, and liked ASMR videos. Results: Emotion-related areas (the amygdala, frontal cortex, and insular cortex) not activated by control and unliked ASMR videos were activated only by liked ASMR videos. Conclusion: The amygdala, frontal cortex, and insular cortex may be involved in the limbic dopamine circuits of the amygdala and middle frontal gyrus and the autonomic balance of the left and right insular cortices. This suggests the potential of positive mood and its use as a treatment for patients with anxiety and depression. These results suggest that the use of ASMR videos to match individual preferences may induce relaxation and have beneficial effects on depression and other disorders, and also support the introduction of ASMR videos in mental health care.

Temporal variations in ischemic and bleeding event risks after acute coronary syndrome during dual antiplatelet therapy.

BACKGROUND: This study estimates the temporal risk variations of ischemic and bleeding events during dual antiplatelet therapy (DAPT) among patients stratified according to the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria, suggesting the optimal period for DAPT after acute coronary syndrome (ACS). METHODS: A total of 1264 ACS patients receiving either clopidogrel or prasugrel with aspirin were classified by ARC-HBR; HBR (n = 574) and non-HBR groups (n = 690). This study was designed as a multicenter observation to evaluate the primary endpoints of ischemic, including cardiovascular death, myocardial infarction, or ischemic stroke, and bleeding events, defined as Bleeding Academic Research Consortium type 3/5. The temporal risk variations were estimated using the Cox hazard and Royston-Parmar models. RESULTS: Ischemic and bleeding events were observed in 9.4% and 7.4%, respectively, during an average observation period of 313 days. The HBR group had a higher incidence of both events than the non-HBR group (15.3% vs. 4.5%, P < 0.01 for ischemic; 11.9% vs. 3.8%, P < 0.01 for bleeding). The estimated risk curves for both events revealed peaks and steep declines in the first few days, followed by constant declines. The peak of risk was higher for bleeding than for ischemic events, but this relationship reversed early, with ischemic events displaying a higher risk in both the HBR and non-HBR groups until at least 60 days. CONCLUSIONS: A 60-day period of DAPT is appropriate to balance the risks of adverse events after ACS, regardless of ARC-HBR criteria.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection.

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence.

Recently, breast types were categorized into four types based on the Breast Imaging Reporting and Data System (BI-RADS) atlas, and evaluating them is vital in clinical practice. A Japanese guideline, called breast composition, was developed for the breast types based on BI-RADS. The guideline is characterized using a continuous value called the mammary gland content ratio calculated to determine the breast composition, therefore allowing a more objective and visual evaluation. Although a discriminative deep convolutional neural network (DCNN) has been developed conventionally to classify the breast composition, it could encounter two-step errors or more. Hence, we propose an alternative regression DCNN based on mammary gland content ratio. We used 1476 images, evaluated by an expert physician. Our regression DCNN contained four convolution layers and three fully connected layers. Consequently, we obtained a high correlation of 0.93 (p < 0.01). Furthermore, to scrutinize the effectiveness of the regression DCNN, we categorized breast composition using the estimated ratio obtained by the regression DCNN. The agreement rates are high at 84.8%, suggesting that the breast composition can be calculated using regression DCNN with high accuracy. Moreover, the occurrence of two-step errors or more is unlikely, and the proposed method can intuitively understand the estimated results.

Brain function effects of autonomous sensory meridian response (ASMR) video viewing.

Background: Autonomous sensory meridian response (ASMR) is the sensation of tingling from audiovisual stimuli that leads to positive emotions. ASMR is used among young people to relax, induce sleep, reduce stress, and alleviate anxiety. However, even without experiencing tingling, ASMR is used by many young people to seek relaxation. Auditory stimulation in ASMR is thought to play the most important role among its triggers, and previous studies have used a mixture of auditory and visual stimulation and auditory stimulation. This is the first study to approach the differences between the effects of direct audiovisual and auditory stimulation from the perspective of brain function using functional magnetic resonance imaging (fMRI) and to clarify the effects of ASMR, which attracts many young people. Methods: The subjects were 30 healthy subjects over 19 years old or older who had not experienced tingling. Brain function was imaged by fMRI while watching ASMR videos or listening to the sound files only. We administered a questionnaire based on a Likert scale to determine if the participants felt a "relaxed mood" and "tingling mood" during the task. Results: Significant activation was found in the visual cortex for audiovisual stimulation and in the visual and auditory cortex for auditory stimulation. In addition, activation of characteristic sites was observed. The specific sites of activation for audiovisual stimulation were the middle frontal gyrus and the left nucleus accumbens, while the specific sites of activation for auditory stimulation were the bilateral insular cortices. The questionnaire showed no significant differences in either "relaxed mood" or "tingling mood" in response to auditory and visual stimulation or auditory stimulation alone. Conclusion: The results of this study showed that there was a clear difference between auditory and audiovisual stimulation in terms of the areas of activation in the brain, but the questionnaire did not reveal any difference in the subjects' mood. Audiovisual stimulation showed activation of the middle frontal gyrus and the nucleus accumbens, whereas auditory stimulation showed activation of the insular cortex. This difference in brain activation sites suggests a difference in mental health effects between auditory and audiovisual stimulation. However, future research on comparisons between those who experience tingling and those who do not, as well as investigations of physiological indices, and examination of the relationship with activated areas in the brain may show that ASMR is useful for mental health.

Novel dual guiding catheter technique for kissing balloon inflation in percutaneous coronary intervention for bifurcated chronic total occlusive lesion.

Traditionally, two bigger-sized guiding catheters (GCs) via the femoral artery have been used in percutaneous coronary intervention (PCI) for chronic total occlusion (CTO). However, those GCs are associated with an increased incidence of bleeding. Therefore, the use of smaller GCs (e.g., 5-Fr) may lessen the invasiveness of this procedure. However, the use of 5-Fr GCs in PCI is complicated by device limitations. A novel technique was developed to overcome the challenges with regard to kissing balloon inflation. This approach involves simultaneous balloon inflation through a dual 5-Fr GC technique for CTO-PCI. In our hospital, this technique was successfully performed in 9 cases of CTO with 10 bifurcated lesions (including four left main-related bifurcations). During the study period, there was no requirement for an additional invasive procedure or occurrence of complications related to the PCI procedure. This technique may be a good option for performing KBI during CTO-PCI through the use of two 5-Fr GCs.

Estimation of adverse events associated with P2Y12 receptor inhibitors stratified by academic research consortium for high bleeding risk criteria in acute coronary syndrome.

BACKGROUND: The usefulness of the Academic Research Consortium for High Bleeding Risk (ARC-HBR) criteria in the selection of P2Y12 receptor inhibitors for acute coronary syndrome is unknown. This study investigated whether the selection of antiplatelet agents according to the ARC-HBR criteria could improve clinical outcomes. METHODS: This multicenter retrospective study included 1261 patients with acute coronary syndrome who received dual antiplatelet therapy, namely clopidogrel (75 mg, n = 529) or prasugrel (3.75 mg, n = 732) in addition to aspirin. The primary endpoint was net adverse clinical events (NACE) after hospital admission, including ischemic (death, myocardial infarction, ischemic stroke) and bleeding events (Bleeding Academic Research Consortium 3 or 5). Secondary outcomes were ischemic and bleeding events. For each patient, the observation period was defined as the duration of dual antiplatelet therapy after admission. RESULTS: During the observation period (average: 313 days), the rate of NACE was lower in the prasugrel group than the clopidogrel group (20.6% vs. 12.6%, respectively, P < 0.01). In patients who satisfied or did not satisfy the ARC-HBR criteria, prasugrel was associated with a 3.7% and 2.1% lower incidence of NACE, respectively, versus clopidogrel. Ischemic and bleeding events were less frequent in the prasugrel group than the clopidogrel group (11.5% vs. 7.9%, respectively, P = 0.03; 10.6% vs. 5.2%, respectively, P < 0.01). The estimated incidence models for NACE suggested that the difference between clopidogrel and prasugrel was greater in patients who satisfied the ARC-HBR criteria than in those who did not. CONCLUSIONS: Prasugrel is preferable to clopidogrel regardless of the ARC-HBR.

A Case of ST Segment Elevation Myocardial Infarction Within 24 h of a Third Dose of COVID-19 mRNA Vaccine.

Although coronavirus disease 2019 (COVID-19) vaccination is known to carry a slight risk of myocarditis and pericarditis, it remains unclear whether it has any impact on coronary artery disease. Here we present a case without particular thrombotic diathesis with a diagnosis of ST segment elevation acute myocardial infarction (STEMI) 19 h after a third dose of a COVID-19 mRNA vaccine. A primary percutaneous coronary intervention procedure for occluded right coronary artery with thrombus aspiration alone was successful in this patient. However, the relationship between STEMI and COVID-19 mRNA vaccination is uncertain, and additional studies to validate thrombogenetic effects of COVID-19 mRNA vaccines are needed. This case was helpful in distinguishing STEMI from myocarditis and pericarditis, which are recognized rare cardiac side effects of COVID-19 vaccination. It is important not to hesitate to perform coronary angiography procedures to rule out the possibility of STEMI occurrence, as in this case.

Prediction of COVID-19 patients in danger of death using radiomic features of portable chest radiographs.

INTRODUCTION: Computer-aided diagnostic systems have been developed for the detection and differential diagnosis of coronavirus disease 2019 (COVID-19) pneumonia using imaging studies to characterise a patient's current condition. In this radiomic study, we propose a system for predicting COVID-19 patients in danger of death using portable chest X-ray images. METHODS: In this retrospective study, we selected 100 patients, including ten that died and 90 that recovered from the COVID-19-AR database of the Cancer Imaging Archive. Since it can be difficult to analyse portable chest X-ray images of patients with COVID-19 because bone components overlap with the abnormal patterns of this disease, we employed a bone-suppression technique during pre-processing. A total of 620 radiomic features were measured in the left and right lung regions, and four radiomic features were selected using the least absolute shrinkage and selection operator technique. We distinguished death from recovery cases using a linear discriminant analysis (LDA) and a support vector machine (SVM). The leave-one-out method was used to train and test the classifiers, and the area under the receiver-operating characteristic curve (AUC) was used to evaluate discriminative performance. RESULTS: The AUCs for LDA and SVM were 0.756 and 0.959, respectively. The discriminative performance was improved when the bone-suppression technique was employed. When the SVM was used, the sensitivity for predicting disease severity was 90.9% (9/10), and the specificity was 95.6% (86/90). CONCLUSIONS: We believe that the radiomic features of portable chest X-ray images can predict COVID-19 patients in danger of death.