{"insert":{"type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://www.ncbi.nlm.nih.gov/pubmed/42388419","label":"url"},{"@id":"https://www.scopus.com/pages/publications/105043533627","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=465417","label":"url"}],"paper_title":{"en":"Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients","ja":"Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients"},"authors":{"en":[{"name":"Saijyo Yoshihito"},{"name":"Zheng Robert"},{"name":"Ookushi Yuichiro"},{"name":"Nomura Yuka"},{"name":"Hirata Yukina"},{"name":"Inoue Hiroaki"},{"name":"Yamada Hirotsugu"},{"name":"Kusunose Kenya"},{"name":"Sata Masataka"}],"ja":[{"name":"西條 良仁"},{"name":"Robert Zheng"},{"name":"大櫛 祐一郎"},{"name":"野村 侑香"},{"name":"平田 有紀奈"},{"name":"Inoue Hiroaki"},{"name":"山田 博胤"},{"name":"楠瀬 賢也"},{"name":"佐田 政隆"}]},"description":{"en":"Global longitudinal strain (GLS) is essential for the early detection of cancer therapy-related cardiac dysfunction (CTRCD). A fully automated echocardiographic analysis system using artificial intelligence (AI) may improve workflow efficiency in cardio-oncology. We sought to evaluate the feasibility and diagnostic performance of a fully automated AI-based echocardiographic system in breast cancer patients receiving cardiotoxic chemotherapy. In this prospective observational study, patients with breast cancer undergoing anthracyclines and/or HER2-targeted therapy between January 2022 and June 2025 were enrolled. Transthoracic echocardiography was performed at baseline and every 12 weeks. GLS was measured manually by two experts and automatically by a fully automated AI-based analysis system. A total of 92 patients (456 echocardiographic studies) were analysed. AI-derived GLS values were significantly lower than expert measurements (17.7 ± 2.9% vs. 18.4 ± 2.8%, P = 0.007). Correlation and agreement between the two methods were moderate (R = 0.64, intraclass correlation coefficient = 0.63). On linear mixed-effects modelling, longitudinal changes in GLS were not significantly different between methods (P = 0.72). GLS-based CTRCD was detected in 31.5% of patients by experts and 34.8% by AI (P = 0.58), with similar detection timing (P = 0.47). Diagnostic agreement was substantial (κ = 0.68, P < 0.001). The fully automated AI-based echocardiographic system demonstrated acceptable agreement and diagnostic performance for GLS assessment and showed a similar ability to track temporal relative GLS changes and identify CTRCD. However, systemic underestimation of absolute GLS values may contribute to threshold-based classification discordance in borderline cases.","ja":"Global longitudinal strain (GLS) is essential for the early detection of cancer therapy-related cardiac dysfunction (CTRCD). A fully automated echocardiographic analysis system using artificial intelligence (AI) may improve workflow efficiency in cardio-oncology. We sought to evaluate the feasibility and diagnostic performance of a fully automated AI-based echocardiographic system in breast cancer patients receiving cardiotoxic chemotherapy. In this prospective observational study, patients with breast cancer undergoing anthracyclines and/or HER2-targeted therapy between January 2022 and June 2025 were enrolled. Transthoracic echocardiography was performed at baseline and every 12 weeks. GLS was measured manually by two experts and automatically by a fully automated AI-based analysis system. A total of 92 patients (456 echocardiographic studies) were analysed. AI-derived GLS values were significantly lower than expert measurements (17.7 ± 2.9% vs. 18.4 ± 2.8%, P = 0.007). Correlation and agreement between the two methods were moderate (R = 0.64, intraclass correlation coefficient = 0.63). On linear mixed-effects modelling, longitudinal changes in GLS were not significantly different between methods (P = 0.72). GLS-based CTRCD was detected in 31.5% of patients by experts and 34.8% by AI (P = 0.58), with similar detection timing (P = 0.47). Diagnostic agreement was substantial (κ = 0.68, P < 0.001). The fully automated AI-based echocardiographic system demonstrated acceptable agreement and diagnostic performance for GLS assessment and showed a similar ability to track temporal relative GLS changes and identify CTRCD. However, systemic underestimation of absolute GLS values may contribute to threshold-based classification discordance in borderline cases."},"publication_date":"2026-07-01","publication_name":{"en":"European Heart Journal. Digital Health","ja":"European Heart Journal. Digital Health"},"volume":"7","number":"6","starting_page":"ztag097","ending_page":"ztag097","languages":["eng"],"referee":true,"identifiers":{"doi":["10.1093/ehjdh/ztag097"],"issn":["2634-3916"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}