Artificial Intelligence assisted echocardiography to facilitate optimal image extraction for congenital heart defects diagnosis in Sub-Saharan Africa

人工智能辅助超声心动图优化成像 撒哈拉以南非洲先天性心脏病诊断中的心脏摘除 摘要 撒哈拉以南非洲(SSA)占全球5岁以下儿童死亡总数的50%以上。先天 畸形(CA)，特别是先天性心脏病(CHD)，约占所有畸形的三分之一 (CA)是导致SSA 5岁以下儿童发病率和死亡率居高不下的主要原因。迟到和错过 由于缺乏能够进行超声心动图扫描的专家，诊断仍然是 SSA对CHD诊断和护理的主要挑战。最近，有更多的 脉搏血氧饱和度筛查新生儿冠心病的研究然而，这项测试是非特异性的，仍然 需要通过超声心动图进行专家确认。为数不多的儿科心脏病专家 现有的中心往往位于距离分娩中心数百公里的地方， 这给父母带来了巨大的经济和身体负担，他们必须踏上这段旅程，以确认 他们的婴儿的诊断，并不排除特别脆弱和脆弱的新生儿 可能会在旅途中死去。培训计划已被证明 提高了异常的图像捕获和识别能力。然而，这样的计划是劳力和 时间密集，需要随着员工的更替而重复。因此，一个互补的战略是 需要改进和维持培训成果。与DSI-非洲的使命一致 通过数据科学的应用解决关键的健康差距，我们提议的项目旨在 利用数据科学和人工智能(AI)的现代进步来解决这一问题 通过为低技能的超声诊断员进行 超声心动图扫描新生儿(0-28天)并提取最佳图像 随后将其发送给远程专家进行解释。这意味着本地非专家(例如 全科医生、护士、助产士)现在可以为分娩中心提供产后服务 在脉搏血氧饱和度筛查后，超声心动图扫描疑似患有冠心病的新生儿， 从而允许他们获得可以传输到远程的最佳标记的图像/视频剪辑 诊断确认专家。这将消除数以百计的旅行的负担和风险 公里，增加早期诊断和远程启动护理，并减少 为数不多的可用的专家。未来的步骤将包括扩展到产前诊断和预测 真正的诊断。

Artificial Intelligence assisted echocardiography to facilitate optimal image extraction for congenital heart defects diagnosis in Sub-Saharan Africa Summary Sub-Saharan Africa (SSA) accounts for over 50% of all global under-5 deaths. Congenital anomalies (CAs), notably congenital heart defects (CHD) which constitutes about a third of all (CAs), are a major contributor to this high under-5 morbidity and mortality in SSA. Late and missed diagnosis, owing to the lack of experts who can perform an echocardiography scan, remains the primary challenge to CHD diagnosis and care in SSA. Recently, there have been increased uptake CHD screening in newborns by pulse oximetry. However, the test is nonspecific and still requires expert confirmation through echocardiography. The few expert paediatric cardiologist centres that exist are often located hundreds of kilometers away from the birthing centres, placing enormous financial and physical burden on parents who must undertake this journey to confirm their baby’s diagnosis, and not leaving out the particularly fragile and vulnerable neonate who may end up dying in the course of the journey. Training programs have been demonstrated to improving image capture and recognition of the anomaly. However, such programs are labor and time intensive and need to be repeated with staff turnover. A complementary strategy is therefore needed to improve and sustain the gains from training. In line with the DSI-Africa’s mission to address critical health gaps through the application of data science, our proposed project seeks to leverage modern advances in data science and artificial intelligence (AI) to address the problem of CHD diagnosis in SSA by creating the possibility for low skilled sonographers to conduct an echocardiography scan for neonates (0-28 days) and extract optimal images that can be subsequently transmitted to a remote expert for interpretation. This means local non-experts (e.g GPs, nurses, midwives) serving the birthing centres will now be able conduct postnatal echocardiography scans for neonates suspected of having a CHD after pulse oximetry screening, allowing them to obtain optimal labeled images/video clips that can be transmitted to a remote expert for diagnosis confirmation. This will remove the burden and risk of travelling hundreds of kilometers, increase early diagnosis and initiation of care remotely, and reduce the workload on the few available experts. Future steps will include extending to prenatal diagnosis and predicting actual diagnosis.