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Artificial intelligence-assisted evaluation of cardiac function by oncology staff in chemotherapy patients.
Papadopoulou, Stella-Lida; Dionysopoulos, Dimitrios; Mentesidou, Vaia; Loga, Konstantia; Michalopoulou, Stella; Koukoutzeli, Chrysanthi; Efthimiadis, Konstantinos; Kantartzi, Vasiliki; Timotheadou, Eleni; Styliadis, Ioannis; Nihoyannopoulos, Petros; Sachpekidis, Vasileios.
Afiliación
  • Papadopoulou SL; Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Dionysopoulos D; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Mentesidou V; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Loga K; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Michalopoulou S; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Koukoutzeli C; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Efthimiadis K; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Kantartzi V; Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Timotheadou E; Department of Medical Oncology, Papageorgiou Hospital, Aristotle University of Thessaloniki, School of Health Sciences, Faculty of Medicine, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Styliadis I; Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
  • Nihoyannopoulos P; Imperial College London, NHLI Hammersmith Hospital, Du Cane Road, London W120NN, UK.
  • Sachpekidis V; Department of Cardiology, Papageorgiou General Hospital, Ring Road, Nea Efkarpia, Thessaloniki 56403, Greece.
Eur Heart J Digit Health ; 5(3): 278-287, 2024 May.
Article en En | MEDLINE | ID: mdl-38774364
ABSTRACT

Aims:

Left ventricular ejection fraction (LVEF) calculation by echocardiography is pivotal in evaluating cancer patients' cardiac function. Artificial intelligence (AI) can facilitate the acquisition of optimal images and automated LVEF (autoEF) calculation. We sought to evaluate the feasibility and accuracy of LVEF calculation by oncology staff using an AI-enabled handheld ultrasound device (HUD). Methods and

results:

We studied 115 patients referred for echocardiographic LVEF estimation. All patients were scanned by a cardiologist using standard echocardiography (SE), and biplane Simpson's LVEF was the reference standard. Hands-on training using the Kosmos HUD was provided to the oncology staff before the study. Each patient was scanned by a cardiologist, a senior oncologist, an oncology resident, and a nurse using the TRIO AI and KOSMOS EF deep learning algorithms to obtain autoEF. The correlation between autoEF and SE-ejection fraction (EF) was excellent for the cardiologist (r = 0.90), the junior oncologist (r = 0.82), and the nurse (r = 0.84), and good for the senior oncologist (r = 0.79). The Bland-Altman analysis showed a small underestimation by autoEF compared with SE-EF. Detection of impaired LVEF < 50% was feasible with a sensitivity of 95% and specificity of 94% for the cardiologist; sensitivity of 86% and specificity of 93% for the senior oncologist; sensitivity of 95% and specificity of 91% for the junior oncologist; and sensitivity of 94% and specificity of 87% for the nurse.

Conclusion:

Automated LVEF calculation by oncology staff was feasible using AI-enabled HUD in a selected patient population. Detection of LVEF < 50% was possible with good accuracy. These findings show the potential to expedite the clinical workflow of cancer patients and speed up a referral when necessary.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Heart J Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Grecia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Eur Heart J Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Grecia Pais de publicación: Reino Unido