Shinnosuke Sawano

Does ECG-Based AI Detect Aortic Stenosis Beyond Conventional LVH Criteria? An Analysis of the CLIDAS Database

Takenobu Shimada, Satoshi Kodera, Shinnosuke Sawano, JingChuan Guan et autres

Abstract Background Aortic stenosis (AS) is a progressive valvular disease associated with poor prognosis once symptoms develop, yet routine echocardiographic screening is impractical. While artificial intelligence (AI)-based electrocardiogram (ECG) models have shown promise for AS detection, it remains unclear whether they primarily …

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Optimizing Single-Lead Electrocardiogram Orientation Improves Detection of Left Ventricular Dysfunction

Hisaki Makimoto, Takayuki Okatani, Masafumi Sato, Shinnosuke Sawano et autres

BACKGROUND: Single-lead electrocardiograms (ECGs) contain subtle markers of left ventricular (LV) dysfunction. We hypothesized that a synthesized frontal-plane vector would improve single-lead detection of reduced LV ejection fraction (LVEF) compared with standard limb leads. METHODS AND RESULTS: This diagnostic study used paired …

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Validity of Diagnoses of Aortic Dissection and Pulmonary Embolism in Hospitalized Patients Using the Japanese DPC Database

Risa Kishikawa, Satoshi Kodera, Kohei Abe, Shinnosuke Sawano et autres

Acute aortic dissection (AD) and pulmonary thromboembolism (PE) are life-threatening conditions requiring timely diagnosis. In Japan, large-scale validation studies of administrative data for these diagnoses are limited. It is required to validate the accuracy of International Classification of Diseases, 10th Revision (ICD-10) …

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Impact of arrhythmias on the performance of a deep learning model for predicting echocardiographic abnormalities from electrocardiograms

Goro Fujiki, Satoshi Kodera, K Tanabe, Shunichi Kushida et autres

Abstract Background Heart failure requires early diagnosis, and targeted screening could improve if functional and morphological cardiac abnormalities were detectable on an electrocardiogram (ECG). While deep learning (DL) models have shown promise in predicting echocardiographic findings, their performance in patients with arrhythmias …

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Prevalence, associated factors, and prognostic value of P wave abnormality in patients with coronary artery disease

Kazutoshi Hirose, Hiroyuki Kiriyama, Shun Minatsuki, Yugo Nagae et autres

P-wave terminal force in V1 (PTFV1) on electrocardiography is an easily available and cost-effective surrogate marker reflecting myocardial electrical and structural remodeling. An abnormal PTFV1 was recently suggested to be a reliable predictor of adverse cardiovascular events, whereas its performance in the …

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Enhancing Chest Radiography Artificial Intelligence Model with Electrocardiogram for Improved Detection of Low Ejection Fraction

Masataka Satô, Satoshi Kodera, Shogo Hamano, Naoto Setoguchi et autres

Artificial intelligence models reportedly detect a low ejection fraction (EF) with chest radiography (CXR) examinations, which traditionally require a transthoracic echocardiogram (TTE) for evaluation. However, whether coupling a CXR model with an electrocardiogram (ECG) model improves the detection performance remains unclear. This …

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Preventive Effects of Prasugrel on Cerebrovascular Events Following Percutaneous Coronary Intervention

Hisaki Makimoto, Yusuke Sasabuchi, Tomoyuki Kabutoya, Takahide Kohro et autres

BACKGROUND: Dual antiplatelet drug administration is recommended after percutaneous coronary intervention (PCI) stent placement. Although prasugrel, a newer P2Y12 inhibitor, reportedly suppresses cardiovascular events more effectively than the traditional agent clopidogrel, its preventive effects on cerebrovascular disorders remain a topic of ongoing …

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Human-Centered AI in Multidisciplinary Medical Discussions: Evaluating the Feasibility of a Chat-Based Approach to Case Assessment

Shinnosuke Sawano, Satoshi Kodera

In this study, we investigate the feasibility of using a human-centered artificial intelligence (AI) chat platform where medical specialists collaboratively assess complex cases. As the target population for this platform, we focus on patients with cardiovascular diseases who are in a state …

Long-term cardiovascular outcomes after percutaneous coronary intervention in patients with systemic sclerosis

Kazutoshi Hirose, Hiroyuki Kiriyama, Shun Minatsuki, Yugo Nagae et autres

Background: Recent data have shown that systemic sclerosis (SSc) is a significant risk factor for coronary artery disease (CAD) and poorer cardiovascular outcomes in the setting of acute coronary syndrome. However, the morphological characteristics of CAD and the long-term cardiovascular outcomes in …

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An ensemble learning model for detection of pulmonary hypertension using electrocardiogram, chest X-ray, and brain natriuretic peptide

Risa Kishikawa, Satoshi Kodera, Naoto Setoguchi, Kengo Tanabe et autres

Abstract Aims Delayed diagnosis of pulmonary hypertension (PH) is a known cause of poor patient prognosis. We aimed to develop an artificial intelligence (AI) model, using ensemble learning method to detect PH using electrocardiography (ECG), chest X-ray (CXR), and brain natriuretic peptide …

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Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms

Goro Fujiki, Satoshi Kodera, Naoto Setoguchi, Kengo Tanabe et autres

Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive. Objectives: This study aimed to develop a deep learning model for comprehensive prediction …

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Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis

Mai Shimbo, Masaru Hatano, Susumu Katsushika, Satoshi Kodera et autres

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis (SSc). Deep learning models can be applied to detect PH in the chest X-ray images of these patients. The aim of the study was to investigate the performance and …

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