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以促进最佳图像 撒哈拉以南非洲地区先天性心脏病诊断的提取 总结 撒哈拉以南非洲占全球5岁以下儿童死亡总数的50%以上。先天性 先天性心脏病（CHD）占所有先天性心脏病的三分之一。 (CAs)是撒哈拉以南非洲5岁以下儿童发病率和死亡率高的主要原因。迟到和错过 由于缺乏能够进行超声心动图扫描的专家， 对SSA中CHD诊断和护理的主要挑战。最近， 通过脉搏血氧仪对新生儿进行CHD筛查。然而，该测试是非特异性的， 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数百公里以外的分娩中心， 这对父母来说是一个巨大的经济和身体负担，他们必须踏上这段旅程， 他们的婴儿的诊断，而不是遗漏了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得到证明， 改进异常的图像捕获和识别。然而，这些计划是劳动和 需要大量时间，并且需要随着工作人员的更替而重复进行。因此，一项补充战略是 需要改进和维持培训成果。根据非洲残疾人国际的使命， 通过数据科学的应用解决关键的健康差距，我们拟议的项目寻求 利用数据科学和人工智能（AI）的现代进步来解决这个问题 通过为低技能的超声医师创造可能性， 超声心动图扫描新生儿（0-28天），并提取最佳图像， 随后传送给远程专家进行口译。这意味着当地的非专家（例如 为分娩中心服务的全科医生、护士、助产士现在将能够进行产后护理。 在脉搏血氧仪筛查后对疑似患有CHD的新生儿进行超声心动图扫描， 从而允许他们获得最佳的标记图像/视频剪辑 专家诊断确认。这将消除数百人旅行的负担和风险， 公里，增加早期诊断和远程护理启动，并减少工作量 为数不多的专家。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.