Hadjidimitriou Stelios, Pagourelias Efstathios, Apostolidis Georgios, Dimaridis Ioannis, Charisis Vasileios, Bakogiannis Constantinos, Hadjileontiadis Leontios, Vassilikos Vassilios
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Third Cardiology Department, Hippokrateion Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.
JMIR Res Protoc. 2023 Mar 13;12:e44650. doi: 10.2196/44650.
Echocardiography (ECHO) is a type of ultrasonographic procedure for examining the cardiac function and morphology, with functional parameters of the left ventricle (LV), such as the ejection fraction (EF) and global longitudinal strain (GLS), being important indicators. Estimation of LV-EF and LV-GLS is performed either manually or semiautomatically by cardiologists and requires a nonnegligible amount of time, while estimation accuracy depends on scan quality and the clinician's experience in ECHO, leading to considerable measurement variability.
The aim of this study is to externally validate the clinical performance of a trained artificial intelligence (AI)-based tool that automatically estimates LV-EF and LV-GLS from transthoracic ECHO scans and to produce preliminary evidence regarding its utility.
This is a prospective cohort study conducted in 2 phases. ECHO scans will be collected from 120 participants referred for ECHO examination based on routine clinical practice in the Hippokration General Hospital, Thessaloniki, Greece. During the first phase, 60 scans will be processed by 15 cardiologists of different experience levels and the AI-based tool to determine whether the latter is noninferior in LV-EF and LV-GLS estimation accuracy (primary outcomes) compared to cardiologists. Secondary outcomes include the time required for estimation and Bland-Altman plots and intraclass correlation coefficients to assess measurement reliability for both the AI and cardiologists. In the second phase, the rest of the scans will be examined by the same cardiologists with and without the AI-based tool to primarily evaluate whether the combination of the cardiologist and the tool is superior in terms of correctness of LV function diagnosis (normal or abnormal) to the cardiologist's routine examination practice, accounting for the cardiologist's level of ECHO experience. Secondary outcomes include time to diagnosis and the system usability scale score. Reference LV-EF and LV-GLS measurements and LV function diagnoses will be provided by a panel of 3 expert cardiologists.
Recruitment started in September 2022, and data collection is ongoing. The results of the first phase are expected to be available by summer 2023, while the study will conclude in May 2024, with the end of the second phase.
This study will provide external evidence regarding the clinical performance and utility of the AI-based tool based on prospectively collected ECHO scans in the routine clinical setting, thus reflecting real-world clinical scenarios. The study protocol may be useful to investigators conducting similar research.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44650.
超声心动图（ECHO）是一种用于检查心脏功能和形态的超声检查方法，左心室（LV）的功能参数，如射血分数（EF）和整体纵向应变（GLS），是重要指标。LV-EF和LV-GLS的估计由心脏病专家手动或半自动进行，需要花费不可忽视的时间，而估计准确性取决于扫描质量和临床医生的ECHO经验，导致测量变异性相当大。
本研究的目的是对一种经过训练的基于人工智能（AI）的工具的临床性能进行外部验证，该工具可从经胸ECHO扫描中自动估计LV-EF和LV-GLS，并提供有关其效用的初步证据。
这是一项分两个阶段进行的前瞻性队列研究。将根据希腊塞萨洛尼基希波克拉底综合医院的常规临床实践，从120名接受ECHO检查的参与者中收集ECHO扫描。在第一阶段，15名不同经验水平的心脏病专家和基于AI的工具将处理60次扫描，以确定后者在LV-EF和LV-GLS估计准确性（主要结果）方面是否不劣于心脏病专家。次要结果包括估计所需时间、Bland-Altman图以及组内相关系数，以评估AI和心脏病专家的测量可靠性。在第二阶段，其余扫描将由同一名心脏病专家在有和没有基于AI的工具的情况下进行检查，主要评估心脏病专家和该工具的组合在LV功能诊断（正常或异常）的正确性方面是否优于心脏病专家的常规检查实践，并考虑心脏病专家的ECHO经验水平。次要结果包括诊断时间和系统可用性量表评分。3名心脏病专家组成的专家小组将提供参考LV-EF和LV-GLS测量值以及LV功能诊断。
招募工作于2022年9月开始，数据收集正在进行中。第一阶段的结果预计在2023年夏季可得，而该研究将于2024年5月随着第二阶段的结束而结束。
本研究将基于在常规临床环境中前瞻性收集的ECHO扫描，提供有关基于AI的工具的临床性能和效用的外部证据，从而反映现实世界的临床情况。该研究方案可能对进行类似研究的调查人员有用。
国际注册报告识别号（IRRID）：DERR1-10.2196/44650。
