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促进撒哈拉以南非洲先天性心脏缺陷诊断的最佳图像提取

• 批准号: 10710681
• 负责人: Aminkeng Zawuo LEKE
• 金额: $ 23.61万
• 依托单位: HEALTH RESEARCH FOUNDATION
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710681
• 关键词: Address; Affect; Africa; Africa South of the Sahara; Artificial Intelligence; Awareness; Birthing Centers; Cardiac; Caring; Cessation of life; Cities; Classification; Clip; Congenital Heart Defects; Data Science; Detection; Diagnosis; Early Diagnosis; Early treatment; Echocardiography; Feedback; Future; Goals; Health; Heart; Image; Infant; Instruction; Label; Midwife; Mission; Modeling; Modernization; Morbidity - disease rate; Newborn Infant; Nurses; Parents; Patients; Performance; Prediction of Response to Therapy; Prenatal Diagnosis; Procedures; Pulse Oximetry; Resource-limited setting; Risk; Scanning; Stream; Technology; Testing; Time; Training; Training Programs; Travel; Work; Workload; clinical practice; congenital anomaly; cost; design; diagnostic value; improved; mortality; neonate; new technology; pediatric cardiologist; postnatal; prenatal; programs; remote health care; screening; skills; transmission process; uptake

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.

人工智能辅助超声心动图促进最佳图像 撒哈拉以南非洲的先天性心脏缺陷提取诊断 概括 撒哈拉以南非洲（SSA）占全球5岁以下死亡的50％以上。先天性 异常（CAS），尤其是先天性心脏缺陷（CHD），大约三分之一 （CAS）是SSA中这种高5岁以下发病率和死亡率的主要贡献者。迟到了 由于缺乏可以进行超声心动图扫描的专家，诊断仍然是 SSA中CHD诊断和护理的主要挑战。最近，有所增加 通过脉搏血氧饱和度在新生儿中摄取CHD筛查。但是，该测试是非特异性的，仍然 需要通过超声心动图的专家确认。少数专业的儿科心脏病专家 存在的中心通常位于距生日中心数百公里的地方 对必须进行这次旅行以确认的父母的巨大财务和身体焚烧 他们的婴儿的诊断，也没有遗漏特别脆弱且脆弱的新生儿 可能最终在旅途中死亡。培训计划已被证明 改善图像捕获和识别异常。但是，这样的计划是劳动和 时间密集，需要在员工营业额中重复重复。因此，完整的策略是 需要改善和维持培训的收益。符合DSI-AFRICA的使命 通过应用数据科学解决关键的健康差距，我们的拟议项目寻求 利用数据科学和人工智能（AI）的现代进步来解决问题 SSA中CHD诊断的诊断方法是通过为低技能超声师进行进行的可能性 超声心动图扫描新生儿（0-28天），并提取最佳图像 随后将其传输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）为生日中心服务现在可以进行产后进行 超声心动图扫描对怀疑在脉搏血氧仪筛查后遇到CHD的新生儿， 允许他们获得可以传输到遥控器的最佳标记图像/视频剪辑 诊断确认的专家。这将消除燃烧和旅行数百次的风险 远程，增加诊断和远程护理的主动性，并减少工作量 少数可用的专家。未来的步骤将包括扩展到产前诊断并预测 实际诊断。