Hirotaka Ieki

All Publications

• Artificial Intelligence Prediction of Age from Echocardiography as a Marker for Cardiovascular Disease. medRxiv : the preprint server for health sciences Rawlani, M., Ieki, H., Binder, C., Yuan, V., Chiu, I. M., Bhatt, A., Ebinger, J. E., Sahashi, Y., Ambrosy, A. P., Cheng, P., Kwan, A. C., Cheng, S., Ouyang, D. 2025

  Abstract

  Accurate understanding of biological aging and the impact of environmental stressors is crucial for understanding cardiovascular health and identifying patients at risk for adverse outcomes. Chronological age stands as perhaps the most universal risk predictor across virtually all populations and diseases. While chronological age is readily discernible, efforts to distinguish between biologically older versus younger individuals can, in turn, potentially identify individuals with accelerated versus delayed cardiovascular aging. This study presents a deep learning artificial intelligence (AI) approach to predict age from echocardiogram videos, leveraging 2,610,266 videos from 166,508 studies from 90,738 unique patients and using the trained models to identify features of accelerated and delayed aging. Leveraging multi-view echocardiography, our AI age prediction model achieved a mean absolute error (MAE) of 6.76 (6.65 - 6.87) years and a coefficient of determination (R2) of 0.732 (0.72 - 0.74). Stratification by age prediction revealed associations with increased risk of coronary artery disease, heart failure, and stroke. The age prediction can also identify heart transplant recipients as a discontinuous prediction of age is seen before and after a heart transplant. Guided back propagation visualizations highlighted the model's focus on the mitral valve, mitral apparatus, and basal inferior wall as crucial for the assessment of age. These findings underscore the potential of computer vision-based assessment of echocardiography in enhancing cardiovascular risk assessment and understanding biological aging in the heart.

  View details for DOI 10.1101/2025.03.25.25324627

  View details for PubMedID 40196275

  View details for PubMedCentralID PMC11974980

• Artificial intelligence automation of echocardiographic measurements. medRxiv : the preprint server for health sciences Sahashi, Y., Ieki, H., Yuan, V., Christensen, M., Vukadinovic, M., Binder-Rodriguez, C., Rhee, J., Zou, J. Y., He, B., Cheng, P., Ouyang, D. 2025

  Abstract

  Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time, however manual assessment is time-consuming and can be imprecise. Artificial intelligence (AI) has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.We developed and validated open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography. The outputs of segmentation models were compared to sonographer measurements from two institutions to access accuracy and precision.We utilized 877,983 echocardiographic measurements from 155,215 studies from Cedars-Sinai Medical Center (CSMC) to develop EchoNet-Measurements, an open-source deep learning model for echocardiographic annotation. The models demonstrated a good correlation when compared with sonographer measurements from held-out data from CSMC and an independent external validation dataset from Stanford Healthcare (SHC). Measurements across all nine B-mode and nine Doppler measurements had high accuracy (an overall R2 of 0.967 (0.965 - 0.970) in the held-out CSMC dataset and 0.987 (0.984 - 0.989) in the SHC dataset). When evaluated end-to-end on a temporally distinct 2,103 studies at CSMC, EchoNet-Measurements performed well an overall R2 of 0.981 (0.976 - 0.984). Performance was consistent across patient characteristics including sex and atrial fibrillation status.EchoNet-Measurement achieves high accuracy in automated echocardiographic measurement that is comparable to expert sonographers. This open-source model provides the foundation for future developments in AI applied to echocardiography.

  View details for DOI 10.1101/2025.03.18.25324215

  View details for PubMedID 40166567

  View details for PubMedCentralID PMC11957091

• Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction NATURE GENETICS Miyazawa, K., Ito, K., Ito, M., Zou, Z., Kubota, M., Nomura, S., Matsunaga, H., Koyama, S., Ieki, H., Akiyama, M., Koike, Y., Kurosawa, R., Yoshida, H., Ozaki, K., Onouchi, Y., BioBank Japan Project, A., Takahashi, A., Matsuda, K., Murakami, Y., Aburatani, H., Kubo, M., Momozawa, Y., Terao, C., Oki, S., Akazawa, H., Kamatani, Y., Komuro, I. 2023: 187-197

  Abstract

  Atrial fibrillation (AF) is a common cardiac arrhythmia resulting in increased risk of stroke. Despite highly heritable etiology, our understanding of the genetic architecture of AF remains incomplete. Here we performed a genome-wide association study in the Japanese population comprising 9,826 cases among 150,272 individuals and identified East Asian-specific rare variants associated with AF. A cross-ancestry meta-analysis of >1 million individuals, including 77,690 cases, identified 35 new susceptibility loci. Transcriptome-wide association analysis identified IL6R as a putative causal gene, suggesting the involvement of immune responses. Integrative analysis with ChIP-seq data and functional assessment using human induced pluripotent stem cell-derived cardiomyocytes demonstrated ERRg as having a key role in the transcriptional regulation of AF-associated genes. A polygenic risk score derived from the cross-ancestry meta-analysis predicted increased risks of cardiovascular and stroke mortalities and segregated individuals with cardioembolic stroke in undiagnosed AF patients. Our results provide new biological and clinical insights into AF genetics and suggest their potential for clinical applications.

  View details for DOI 10.1038/s41588-022-01284-9

  View details for Web of Science ID 000914893700001

  View details for PubMedID 36653681

  View details for PubMedCentralID PMC9925380

• Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis. Communications medicine Ieki, H., Ito, K., Saji, M., Kawakami, R., Nagatomo, Y., Takada, K., Kariyasu, T., Machida, H., Koyama, S., Yoshida, H., Kurosawa, R., Matsunaga, H., Miyazawa, K., Ozaki, K., Onouchi, Y., Katsushika, S., Matsuoka, R., Shinohara, H., Yamaguchi, T., Kodera, S., Higashikuni, Y., Fujiu, K., Akazawa, H., Iguchi, N., Isobe, M., Yoshikawa, T., Komuro, I. 2022; 2 (1): 159

  Abstract

  In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.

  View details for DOI 10.1038/s43856-022-00220-6

  View details for PubMedID 36494479

  View details for PubMedCentralID PMC9734197

• Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease NATURE GENETICS Koyama, S., Ito, K., Terao, C., Akiyama, M., Horikoshi, M., Momozawa, Y., Matsunaga, H., Ieki, H., Ozaki, K., Onouchi, Y., Takahashi, A., Nomura, S., Morita, H., Akazawa, H., Kim, C., Seo, J., Higasa, K., Iwasaki, M., Yamaji, T., Sawada, N., Tsugane, S., Koyama, T., Ikezaki, H., Takashima, N., Tanaka, K., Arisawa, K., Kuriki, K., Naito, M., Wakai, K., Suna, S., Sakata, Y., Sato, H., Hori, M., Sakata, Y., Matsuda, K., Murakami, Y., Aburatani, H., Kubo, M., Matsuda, F., Kamatani, Y., Komuro, I. 2020; 52 (11): 1169-+

  Abstract

  To elucidate the genetics of coronary artery disease (CAD) in the Japanese population, we conducted a large-scale genome-wide association study of 168,228 individuals of Japanese ancestry (25,892 cases and 142,336 controls) with genotype imputation using a newly developed reference panel of Japanese haplotypes including 1,781 CAD cases and 2,636 controls. We detected eight new susceptibility loci and Japanese-specific rare variants contributing to disease severity and increased cardiovascular mortality. We then conducted a trans-ancestry meta-analysis and discovered 35 additional new loci. Using the meta-analysis results, we derived a polygenic risk score (PRS) for CAD, which outperformed those derived from either Japanese or European genome-wide association studies. The PRS prioritized risk factors among various clinical parameters and segregated individuals with increased risk of long-term cardiovascular mortality. Our data improve the clinical characterization of CAD genetics and suggest the utility of trans-ancestry meta-analysis for PRS derivation in non-European populations.

  View details for DOI 10.1038/s41588-020-0705-3

  View details for Web of Science ID 000575347300002

  View details for PubMedID 33020668

• Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning INTERNATIONAL HEART JOURNAL Matsumoto, T., Kodera, S., Shinohara, H., Ieki, H., Yamaguchi, T., Higashikuni, Y., Kiyosue, A., Ito, K., Ando, J., Takimoto, E., Akazawa, H., Morita, H., Komuro, I. 2020; 61 (4): 781-786

  Abstract

  The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

  View details for DOI 10.1536/ihj.19-714

  View details for Web of Science ID 000562958000022

  View details for PubMedID 32684597

• Transethnic Meta-Analysis of Genome-Wide Association Studies Identifies Three New Loci and Characterizes Population-Specific Differences for Coronary Artery Disease CIRCULATION-GENOMIC AND PRECISION MEDICINE Matsunaga, H., Ito, K., Akiyama, M., Takahashi, A., Koyama, S., Nomura, S., Ieki, H., Ozaki, K., Onouchi, Y., Sakaue, S., Suna, S., Ogishima, S., Yamamoto, M., Hozawa, A., Satoh, M., Sasaki, M., Yamaji, T., Sawada, N., Iwasaki, M., Tsugane, S., Tanaka, K., Arisawa, K., Ikezaki, H., Takashima, N., Naito, M., Wakai, K., Tanaka, H., Sakata, Y., Morita, H., Sakata, Y., Matsuda, K., Murakami, Y., Akazawa, H., Kubo, M., Kamatani, Y., Komuro, I. 2020; 13 (3): 128-138

  Abstract

  Genome-wide association studies provided many biological insights into coronary artery disease (CAD), but these studies were mainly performed in Europeans. Genome-wide association studies in diverse populations have the potential to advance our understanding of CAD.We conducted 2 genome-wide association studies for CAD in the Japanese population, which included 12 494 cases and 28 879 controls and 2808 cases and 7261 controls, respectively. Then, we performed transethnic meta-analysis using the results of the coronary artery disease genome-wide replication and meta-analysis plus the coronary artery disease 1000 Genomes meta-analysis with UK Biobank. We then explored the pathophysiological significance of these novel loci and examined the differences in CAD-susceptibility loci between Japanese and Europeans.We identified 3 new loci on chromosome 1q21 (CTSS), 10q26 (WDR11-FGFR2), and 11q22 (RDX-FDX1). Quantitative trait locus analyses suggested the association of CTSS and RDX-FDX1 with atherosclerotic immune cells. Tissue/cell type enrichment analysis showed the involvement of arteries, adrenal glands, and fat tissues in the development of CAD. We next compared the odds ratios of lead variants for myocardial infarction at 76 genome-wide significant loci in the transethnic meta-analysis and a moderate correlation between Japanese and Europeans, where 8 loci showed a difference. Finally, we performed tissue/cell type enrichment analysis using East Asian-frequent and European-frequent variants according to the risk allele frequencies and identified significant enrichment of adrenal glands in the East Asian-frequent group while the enrichment of arteries and fat tissues was found in the European-frequent group. These findings indicate biological differences in CAD susceptibility between Japanese and Europeans.We identified 3 new loci for CAD and highlighted the genetic differences between the Japanese and European populations. Moreover, our transethnic analyses showed both shared and unique genetic architectures between the Japanese and Europeans. While most of the underlying genetic bases for CAD are shared, further analyses in diverse populations will be needed to elucidate variations fully.

  View details for DOI 10.1161/CIRCGEN.119.002670

  View details for Web of Science ID 000545932800004

  View details for PubMedID 32469254

• Impact of Pulmonary Artery-to-Aorta Ratio by CT on the Clinical Outcome in Heart Failure JOURNAL OF CARDIAC FAILURE Ieki, H., Nagatomo, Y., Tsugu, M., Mahara, K., Iguchi, N., Isobe, M., Yoshikawa, T. 2019; 25 (11): 886-893

  Abstract

  Previous studies have indicated that the ratio of pulmonary artery (PA) to ascending aorta (Ao) diameter as measured by computed tomography (PA/Ao) is strongly associated with pulmonary artery pressure. However, the clinical significance of PA/Ao in heart failure (HF) has not been fully characterized. We sought to investigate the prognostic impact of PA/Ao in HF.Based on the prospective registry of patients admitted to our institution due to acute decompensated HF (ADHF), the records of the consecutive 761 patients admitted between 2011 and 2016 were reviewed. Thoracic computed tomography data during the hospital stays were obtained from 447 patients (median 78 (70-84) years of age; male, 62.2%). The diameters of PA and Ao were measured at the level of PA bifurcation. The subjects were divided into the H group (PA/Ao ≥ 1.0) and the L group (PA/Ao < 1.0) according to the PA/Ao values. The cutoff value was derived from receiver operating curve analysis.There were no significant differences in age, sex or body mass index between the H and L groups. The H group was associated with significantly larger left atrial dimension (LAD), higher tricuspid regurgitation peak gradient (TRPG) and E/e' (LAD, H, 48 (42-55) mm vs L, 45 (39-50) mm, P < 0.001; TRPG, H, 34 (26-48) mm Hg vs L, 28 (22-38) mm Hg, P < 0.001; E/e', H, 23.3 (42-55) vs L, 18.4 (13.9-25), P < 0.001). Length of hospital stay was significantly longer in the H group than in the L group (H, 19 (14-32) days vs L, 16 (12-23) days, P < 0.001). In-hospital mortality was significantly higher in the H group compared with the L group (H, 5.4% vs L, 1.2%, P = 0.02). Age, sex, LAD and TRPG were independently associated with PA/Ao. The primary endpoint, defined as the composite of all-cause death and ADHF rehospitalization during a median of 479 days after discharge, was significantly more common in the H group (P < 0.001, log-rank test). PA/Ao was independently associated with the primary endpoint, even after adjusting for the other confounding factors (P = 0.002).PA/Ao is a reliable marker for the prediction of the outcome of patients with ADHF.

  View details for DOI 10.1016/j.cardfail.2019.05.005

  View details for Web of Science ID 000501939800006

  View details for PubMedID 31100468

• Complete Resolution of Left Ventricular Outflow Tract Obstruction After Spontaneous Mitral Valve Chordal Rupture in a Patient With Hypertrophic Cardiomyopathy. CASE (Philadelphia, Pa.) Ieki, H., Mahara, K., Nagatomo, Y., Iguchi, N., Takayama, M., Isobe, M. 2019; 3 (3): 103-106

  Abstract

  • Spontaneous mitral chordal rupture is a complication in hypertrophic cardiomyopathy (HCM). • Mitral chordal rupture in HCM causes deterioration in heart failure. • Symptoms improved when left ventricular outflow tract (LVOT) obstruction disappeared. • Mitral valve has a role in LVOT obstruction and systolic anterior motion.

  View details for DOI 10.1016/j.case.2019.03.001

  View details for PubMedID 31286088

  View details for PubMedCentralID PMC6588837