Peck Daniel, Rwebembera Joselyn, Nakagaayi Doreen, Minja Neema W, Ollberding Nicholas J, Pulle Jafesi, Klein Jennifer, Adams David, Martin Randolph, Koepsell Kilian, Sanyahumbi Amy, Beaton Andrea, Okello Emmy, Sable Craig
University of Minnesota, Minneapolis, Minnesota.
Uganda Heart Institute, Kampala, Uganda.
J Am Soc Echocardiogr. 2023 Jul;36(7):724-732. doi: 10.1016/j.echo.2023.03.001. Epub 2023 Mar 9.
A novel technology utilizing artificial intelligence (AI) to provide real-time image-acquisition guidance, enabling novices to obtain diagnostic echocardiographic images, holds promise to expand the reach of echo screening for rheumatic heart disease (RHD). We evaluated the ability of nonexperts to obtain diagnostic-quality images in patients with RHD using AI guidance with color Doppler.
Novice providers without prior ultrasound experience underwent a 1-day training curriculum to complete a 7-view screening protocol using AI guidance in Kampala, Uganda. All trainees then scanned 8 to 10 volunteer patients using AI guidance, half RHD and half normal. The same patients were scanned by 2 expert sonographers without the use of AI guidance. Images were evaluated by expert blinded cardiologists to assess (1) diagnostic quality to determine presence/absence of RHD and (2) valvular function and (3) to assign an American College of Emergency Physicians score of 1 to 5 for each view.
Thirty-six novice participants scanned a total of 50 patients, resulting in a total of 462 echocardiogram studies, 362 obtained by nonexperts using AI guidance and 100 obtained by expert sonographers without AI guidance. Novice images enabled diagnostic interpretation in >90% of studies for presence/absence of RHD, abnormal MV morphology, and mitral regurgitation (vs 99% by experts, P ≤ .001). Images were less diagnostic for aortic valve disease (79% for aortic regurgitation, 50% for aortic stenosis, vs 99% and 91% by experts, P < .001). The American College of Emergency Physicians scores of nonexpert images were highest in the parasternal long-axis images (mean, 3.45; 81% ≥ 3) compared with lower scores for apical 4-chamber (mean, 3.20; 74% ≥ 3) and apical 5-chamber images (mean, 2.43; 38% ≥ 3).
Artificial intelligence guidance with color Doppler is feasible to enable RHD screening by nonexperts, performing significantly better for assessment of the mitral than aortic valve. Further refinement is needed to optimize acquisition of color Doppler apical views.
一项利用人工智能（AI）提供实时图像采集指导的新技术，使新手能够获取诊断性超声心动图图像，有望扩大风湿性心脏病（RHD）超声筛查的范围。我们评估了非专业人员在使用彩色多普勒AI指导下获取RHD患者诊断质量图像的能力。
在乌干达坎帕拉，没有超声经验的新手提供者参加了为期1天的培训课程，以使用AI指导完成7视图筛查方案。然后，所有受训人员使用AI指导对8至10名志愿者患者进行扫描，其中一半为RHD患者，一半为正常患者。由2名专家超声心动图医师对相同患者进行扫描，不使用AI指导。由不知情的专家心脏病学家对图像进行评估，以评估（1）诊断质量以确定是否存在RHD，（2）瓣膜功能，以及（3）为每个视图分配美国急诊医师学会1至5分。
36名新手参与者共扫描了50名患者，总共进行了462项超声心动图研究，其中362项由非专业人员使用AI指导获得，100项由专家超声心动图医师在不使用AI指导的情况下获得。新手获得的图像在>90%的研究中能够对是否存在RHD、二尖瓣形态异常和二尖瓣反流进行诊断性解读（专家为99%，P≤0.001）。对于主动脉瓣疾病，图像的诊断性较差（主动脉反流为79%，主动脉狭窄为50%，专家分别为99%和91%，P<0.001）。非专业人员图像的美国急诊医师学会评分在胸骨旁长轴图像中最高（平均3.45；81%≥3），而心尖四腔心（平均3.20；74%≥3）和心尖五腔心图像评分较低（平均2.43；38%≥3）。
彩色多普勒AI指导对于非专业人员进行RHD筛查是可行的，在评估二尖瓣方面比主动脉瓣表现明显更好。需要进一步改进以优化彩色多普勒心尖视图的采集。
