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Featured Research.
Featured Research.
#1. Opportunistic Screening of Chronic Liver Disease with Deep-Learning–Enhanced Echocardiography
Chronic liver disease affects 1.5 billion people worldwide, often leading to severe health outcomes. Despite increasing prevalence, most patients remain undiagnosed. Various screening methods (like CT, MRI and liver biopsy) exist, but barriers like cost and availability limit their use.
Echocardiography, widely used in the clinic and tertialy center, can provide valuable information about liver tissue through subcostal views as well as cardiac structures. Deep learning applied to these echocardiographic images have been developed to detect cardiovascular diseases and predict disease progression. Echo-Net-Liver, a deep-learning algorithm pipeline, is developed to identify chronic liver disease (particularly steatotic liver disease (SLD) and cirrhosis), using subcostal echocardiographic images. Opportunistic liver disease screening using AI-guided echocardiography may contribute to early detection and patient care by utilizing existing procedures. Thank you for all contributors and mentors!
#2. EchoNet-Measurement -Artificial intelligence automation of echocardiographic measurements-
This project focuses on building a deep-learning model that can automatically annotate specific measurements from echocardiographic videos or images. Annotation of measurements on images is typically a time-consuming task for cardiologists and sonographer, especially with the growing volume of echocardiographic exams being performed. Automating this process has the potential to significantly improve efficiency in clinical workflows, and potentially reduce human-error, and allow high-throughput cardiovascular research using echocardiography. Our model (a first open-model for echocardiography measurements) aims to streamline this annotation process and guide parameter measurements with faster and more accurate manner. This project repository contains the deep learning model used for automatic annotation for 2D echo videos and Doppler images, along with the necessary code and weights to load the model and perform inference on your echo videos. Users can easily run predictions using the provided codes and weights.
#3. What is the association between Fruit and Vegetable Intake and All-Cause Mortality in Japanese cohort?
Sahashi Y, Goto A, Takachi R, Ishihara J, Kito K, Kanehara R, Yamaji T, Iwasaki M, Inoue M, Shoichiro T, Sawada N. Inverse Association between Fruit and Vegetable Intake and All-Cause Mortality: Japan Public Health Center-Based Prospective Study. J Nutr. 2022 Oct 6;152(10):2245-2254. doi: 10.1093/jn/nxac136. PMID: 35762672.
This study addresses a gap in the existing literature regarding the association between fruit and vegetable intake and mortality in Asian populations. While numerous studies in Western countries have shown an inverse, often nonlinear, relationship, evidence from Asia has been limited by shorter follow-up periods or less validated dietary assessment methods, leaving the nature of this association, particularly its nonlinearity, less clear in Asian contexts. Analyzing data from over 94,000 Japanese participants over a median of 20.9 years, the study found that higher intake of both fruits and vegetables was significantly associated with lower all-cause mortality. Importantly, this association was observed to be nonlinear, with the most substantial benefits seen at higher levels of intake (specifically, the fourth and fifth quintiles) compared to the lowest quintile. Fruit intake was also inversely associated with cardiovascular mortality. These findings provide robust evidence from a large Asian cohort with extensive follow-up, demonstrating that higher fruit and vegetable consumption is linked to reduced all-cause mortality in the Japanese population. This contributes valuable data for developing evidence-based dietary guidelines tailored for Asia, where current recommendations are largely based on Western data. This paper was featured by several media including NHK etc.
Thanks for all co-authors and mentor (Prof. Atsushi Goto)!
2025
2025
I-Min Chiu, Sahashi Y* et al. Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence, European Heart Journal Digital Health 2025 (*Equal contributor)
I-Min Chiu, Sahashi Y* et al. Factors Associated with Physician Modifications to Automated ECG Interpretations European Heart Journal Digital Health 2025 (*Equal contributor)
Yamagishi J, Watanabe T, Matsumoto-Miyazaki J, Sahashi Y, Watanabe D, Asada R, Okura H. Impact of cardiac rehabilitation exercise management on improving exercise tolerance among patients with chronic heart disease using a telemetry-based biosignal measurement device: protocol for a multicentre randomised controlled trial in Japan (iCARE-MATE study). BMJ Open. 2025 Aug 28;15(8):e098436.
Rawlani M, Ieki H, Binder C, Yuan V, Chiu IM, Bhatt A, Ebinger JE, Sahashi Y, Ambrosy AP, Cheng P, Kwan AC, Cheng S, Ouyang D. Artificial Intelligence Prediction of Age from Echocardiography as a Marker for Cardiovascular Disease. npj Digital medicine, in press.
Kuroda S, Kawamura I, Sahashi Y, Tonegawa-Kuji R, Kuno T. Effectiveness of rate-adaptive pacing on patients with chronotropic incompetence: Systematic review and meta-analysis of randomized controlled trials. Int J Cardiol. 2025 Mar 15;423:133022. doi: 10.1016/j.ijcard.2025.133022
2024
2024
Sahashi Y, Takeshita R, Watanabe T, Ishihara T, Sekine A, Watanabe D, Ishihara T, Ichiryu H, Endo S, Fukuoka D, Hara T, Okura H. Development of artificial intelligence-based slow-motion echocardiography and clinical usefulness for evaluating regional wall motion abnormalities. Int J Cardiovasc Imaging. 2024 Feb;40(2):385-395. doi: 10.1007/s10554-023-02997-6. Epub 2023 Nov 8. PMID: 37940734
Harano Y*, Sahashi Y*, Kawase Y, Matsuo H. Comparative Evaluation of Alcohol Septal Ablation: Left Anterior Descending Versus Non-left Anterior Descending Artery Approaches. Am J Cardiol. 2024 Apr 1;216:54-62. doi: 10.1016/j.amjcard.2024.01.034. Epub 2024 Feb 23. PMID: 38402924. * Equal Contributor.
Mizuno A, Yoneoka D, Kishi T, Kusunose K, Matsumoto C, Sahashi Y, Ishida M, Sanada S, Fukuda M, Sugimoto T, Hirano M, Sata M, Anzai T, Node K. From Optional to Default - Enhancing Article Viewership Through X (Formerly Twitter) Posting. Circ Rep. 2024 Aug 1;6(9):389-394. doi: 10.1253/circrep.CR-24-0074. PMID: 39262644;
Okazaki M, Sahashi Y, Nagase T, Inoue K, Sekiguchi Y, Nitta J, Shinoda S, Shimizu S, Kuroki M, Isobe M, Mihara T. Inappropriate shock incidence in patients with subcutaneous implantable cardioverter-defibrillators with concomitant cardiac implantable electronic devices: A single-center cohort study. Pacing Clin Electrophysiol. 2024 Jan;47(1):131-138. doi: 10.1111/pace.14887. Epub 2023 Nov 27. PMID: 38010718.
Kuroda S, Kawamura I, Sahashi Y, Kuno T. The significance of non-sustained ventricular tachycardia on life-threatening ventricular arrhythmia events in patients with non-ischemic cardiomyopathy in the contemporary era: A systematic review and meta-analysis. J Cardiol. 2024 Sep;84(3):177-179. doi: 10.1016/j.jjcc.2024.05.004. Epub 2024 May 14. PMID: 38754764.
Miwa T, Hanai T, Hirata S, Nishimura K, Sahashi Y, Unome S, Imai K, Shirakami Y, Suetsugu A, Takai K, Shimizu M. Vitamin D deficiency stratifies the risk of covert and overt hepatic encephalopathy in patients with cirrhosis: A retrospective cohort study. Clin Nutr ESPEN. 2024 Oct;63:267-273. doi: 10.1016/j.clnesp.2024.06.055. Epub 2024 Jul 2. PMID: 38972037.
2023
2023
Suzuki T, Mizuno A, Kishi T, Rewley J, Matsumoto C, Sahashi Y, Ishida M, Sanada S, Fukuda M, Sugimoto T, Hirano M, Node K. Impact of Tweet Content on the Number of Retweets - "Tweet the Meeting 2022". Circ Rep. 2023 Jun 6;5(7):306-310. doi: 10.1253/circrep.CR-23-0043. PMID: 37431517;
2022
2022
Sahashi Y, Goto A, Takachi R, Ishihara J, Kito K, Kanehara R, Yamaji T, Iwasaki M, Inoue M, Shoichiro T, Sawada N. Inverse Association between Fruit and Vegetable Intake and All-Cause Mortality: Japan Public Health Center-Based Prospective Study. J Nutr. 2022 Oct 6;152(10):2245-2254. doi: 10.1093/jn/nxac136. PMID: 35762672.
Sahashi Y, Kuno T, Tanaka Y, Passman R, Briasoulis A, Malik AH. The 30-day readmission rate of same-day discharge protocol following catheter ablation for atrial fibrillation: a propensity score-matched analysis from National Readmission Database. Europace. 2022 May 3;24(5):755-761. doi: 10.1093/europace/euab296. PMID: 34904164
Sahashi Y, Kawasaki M, Okubo M, Kawamura I, Kawase Y, Yoshida A, Tanaka T, Hattori A, Matsuo H, Ozaki Y. Development of 60 MHz integrated backscatter intravascular ultrasound and tissue characterization of attenuated signal coronary plaques that cause myocardial injury after percutaneous coronary intervention. Heart Vessels. 2022 Oct;37(10):1689-1700. doi: 10.1007/s00380-022-02080-5. Epub 2022 May 7. PMID: 35524780.
Sahashi Y, Kawamura I, Aikawa T, Takagi H, Briasoulis A, Kuno T. Safety and feasibility of same-day discharge in patients receiving pulmonary vein isolation-systematic review and a meta-analysis. J Interv Card Electrophysiol. 2022 Mar;63(2):251-258. doi: 10.1007/s10840-021-00967-3. Epub 2021 Feb 25. PMID: 33630213.
Kuno T, Sahashi Y, Kawahito S, Takahashi M, Iwagami M, Egorova NN. Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir. J Med Virol. 2022 Mar;94(3):958-964. doi: 10.1002/jmv.27393. Epub 2021 Oct 22. PMID: 34647622
Kuno T, Fujisaki T, Shoji S, Sahashi Y, Tsugawa Y, Iwagami M, Takagi H, Briasoulis A, Deharo P, Cuisset T, Latib A, Kohsaka S, Bhatt DL. Comparison of Unguided De-Escalation Versus Guided Selection of Dual Antiplatelet Therapy After Acute Coronary Syndrome: A Systematic Review and Network Meta-Analysis. Circ Cardiovasc Interv. 2022 Aug;15(8):e011990. doi: 10.1161/CIRCINTERVENTIONS.122.011990. Epub 2022 Jul 28. PMID: 35899618.
Kuno T, Mikami T, Sahashi Y, Numasawa Y, Suzuki M, Noma S, Fukuda K, Kohsaka S. Machine learning prediction model of acute kidney injury after percutaneous coronary intervention. Sci Rep. 2022 Jan 14;12(1):749. doi: 10.1038/s41598-021-04372-8. PMID: 35031637; PMCID: PMC8760264.
Kawamura I, Kuno T, Sahashi Y, Tanaka Y, Passman R, Briasoulis A, Malik AH. Thirty-day readmission rate of same-day discharge protocol after left atrial appendage occlusion: A propensity score-matched analysis from the National Readmission Database. Heart Rhythm. 2022 Nov;19(11):1819-1825. doi: 10.1016/j.hrthm.2022.07.006. Epub 2022 Jul 11. PMID: 35835364.
Mizuno A, Kusunose K, Kishi T, Rewley J, Matsumoto C, Sahashi Y, Ishida M, Sanada S, Fukuda M, Sugimoto T, Hirano M, Yoneoka D, Sata M, Anzai T, Node K. Impact of Tweeting Summaries by the Japanese Circulation Society Official Account on Article Viewership - Pilot Trial. Circ J. 2022 Mar 25;86(4):715-720. doi: 10.1253/circj.CJ-21-0944. Epub 2022 Mar 12. PMID: 35283367.
2021
2021
Sahashi, Y., Endo, H., Sugimoto, T., Nabeta, T., Nishizaki, K., Kikuchi, A., ... & Matsue, Y. (2021). Worries and concerns among healthcare workers during the coronavirus 2019 pandemic: a web-based cross-sectional survey. Humanities and Social Sciences Communications, 8(1), 1-8.
Preprint, as of today.
Preprint, as of today.
Rawlani M, Ieki H, Binder C, Yuan V, Chiu IM, Bhatt A, Ebinger JE, Sahashi Y, Ambrosy AP, Cheng P, Kwan AC, Cheng S, Ouyang D. Artificial Intelligence Prediction of Age from Echocardiography as a Marker for Cardiovascular Disease. medRxiv [Preprint]. 2025 Mar 26:2025.03.25.25324627. doi: 10.1101/2025.03.25.25324627. PMID: 40196275;
Binder C, Sahashi Y, Ieki H, Vukadinovic M, Yuan V, Rawlani M, Cheng P, Ouyang D, Siegel RJ. Automated Aortic Regurgitation Detection and Quantification: A Deep Learning Approach Using Multi-View Echocardiography. medRxiv [Preprint]. 2025 Mar 19:2025.03.18.25323918. doi: 10.1101/2025.03.18.25323918. PMID: 40166551
Yuan V, Sahashi Y, Ieki H, Vukadinovic M, Binder C, Pieszko K, Ambrosy AP, Cheng PP, Cheng S, Ouyang D. Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function. medRxiv [Preprint]. 2025 Apr 30:2025.04.29.25326683. doi: 10.1101/2025.04.29.25326683. PMID: 40343044;
Chiu IM, Vukadinovic M, Sahashi Y, Cheng PP, Cheng CY, Cheng S, Ouyang D. Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence. medRxiv [Preprint]. 2024 Dec 1:2024.11.27.24318110. doi: 10.1101/2024.11.27.24318110. PMID: 39649606;
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