Finding undiagnosed ATTR-CM patients using AI technology in clinical settings

项目总结：甲状腺素运载蛋白淀粉样变性伴心肌病，ATTR-CM，代表一种严重的 保健问题。ATTR-CM涉及13%的心力衰竭，16%的经导管心脏瓣膜置换术， 和5%的假定患有肥厚型心肌病的个体。主要的挑战是， 患者未被诊断或其诊断被延迟多年。由于ATTR-CM造成的损害 心脏损害是进行性的，诊断延误严重影响预后并增加死亡率。诊断是 问题有两个原因：ATTR-CM具有可变的表现，并且患病率不高。因此，在本发明中， ATTR-CM在诊断过程中通常不被考虑，具有类似症状的更常见诊断是 错误地给予。高达98%的患者由于患病率低和可变而未被诊断 演示文稿.一项研究发现，32%的ATTR-CM患者以前曾被误诊为患有 更常见的心血管疾病。一个容易获得的基因测试可以用来检测hATTR， 99 mTc-DPD脑显像可用于诊断ATTR-CM（包括遗传型和野生型）。幸运的是， 一旦确诊，ATTR是可以治疗的。因此，ATTR-CM的主要挑战是诊断，而不是治疗。一个 需要有效和经济的精确筛查系统来发现最有ATTR风险的个体， 厘米那些通过精确筛选确定的人可以进行测试，并采用有效的治疗方法进行治疗， 挽救了生命，降低了医疗成本。Atomo在SBIR快速通道提案中的目标是创建、优化 并实施基于人工智能的临床决策支持系统（CDSS），以识别可能但未确诊的 ATTR患者在发展CM之前。在这项工作中，我们与Dan Rader博士和PENN合作 药雷德博士是西摩格雷教授的分子医学和系主任， 宾夕法尼亚大学佩雷尔曼医学院的遗传学。雷德博士还指导 宾夕法尼亚大学医学生物库。我们将利用生物银行来识别真阳性患者，以进行培训和评估。 一个人工智能模型来寻找可能的但未确诊的ATTR个体。该模型将用于试点，大多数 作为一项质量改进计划。为了完成这项工作，Atomo将利用其经过验证的ML 已在临床上使用和验证的技术，并在同行评审的期刊上发表。的ATTR AI模型将作为洞察即服务（IaaS）CDSS商业化。

Project Summary: Transthyretin Amyloidosis with cardiac myopathy, ATTR-CM, represent a serious healthcare issue. ATTR-CM is involved in 13% of heart failure, 16% of transcatheter aortic-valve replacement, and 5% of individuals with presumed hypertrophic cardiomyopathy. The primary challenge is that most patients are undiagnosed or their diagnosis is delayed for multiple years. Since the damage ATTR-CM causes to the heart is progressive, diagnosis delays strongly impact prognosis and increase mortality. Diagnosis is problematic for two reasons: ATTR-CM has a variable presentation and the prevalence is not high. Thus, ATTR-CM is often not considered during diagnosis and a more common diagnosis with similar symptoms is given erroneously. Up to 98% of patients are not diagnosed due to the low prevalence and variable presentation. One study found that 32% of ATTR-CM patients had previously been misdiagnosed as having more common cardiovascular diseases. A readily-available genetic test can be used to detect hATTR and 99mTc-DPD-scintigraphy can be used to diagnose ATTR-CM (both hereditary and wild type). Fortunately, once diagnosed, ATTR is treatable. Thus, the main challenge for ATTR-CM is diagnosis, not therapy. An effective and economical precision screening system is needed to find the individuals most at risk of ATTR- CM. Those identified via precision screening could be tested and, treated with effective therapy resulting in saved lives and reduced healthcare costs. Atomo’s goal in this SBIR Fast-Track proposal is to create, optimize and implement an AI-based Clinical Decisions Support System (CDSS) to identify probable yet undiagnosed ATTR patients before they develop CM. For this work, we are partnering with Dr. Dan Rader and PENN Medicine. Dr. Rader is the Seymour Gray Professor of Molecular Medicine and Chair of the Department of Genetics at the Perelman School of Medicine of the University of Pennsylvania. Dr. Rader also directs the Penn Medicine Biobank. We would utilize the BioBank to identify True Positive patients to train and evaluate an AI model to find probable yet undiagnosed ATTR individuals. The model would be used in a pilot, most likely as a quality improvement initiative. To complete this work, Atomo will leverage its proven ML technologies that have been used and verified clinically, with published in peer-reviewed journals. The ATTR AI model would be commercialized as an Insights As A Service (IaaS) CDSS.