STTR Phase I: Artificial Intelligence Tool to Optimize Organ Transplantation Outcomes (Transplant-AI)

STTR 第一阶段：优化器官移植结果的人工智能工具（Transplant-AI）

• 批准号: 2014827
• 负责人: Jim Havelka
• 金额: $ 22.5万
• 依托单位: InformAI, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014827&HistoricalAwards=false
• 关键词: STTR; Phase; Artificial; Intelligence; Tool

The broader impact of this Small Business Technology Transfer (STTR) Phase I project will be to improve solid organ transplantation outcomes. Few significant clinical or technological advancements have been made within the last two decades to improve organ matching success, and the accuracies of current models predicting survival outcomes are diminishing. There is a great need for clinicians to have better decision-making support tools. Every 12 minutes, a new person is added to the organ transplant waiting list, a number growing by about five percent each year. Within a single day, 21 people die waiting for a kidney, liver, or other organ match. Although 36,500 kidney and liver transplants are performed each year, the patient demand for donor organs far outweighs supply by four to one, so the need to improve donor-recipient matching is urgent. Optimizing the donor organ-patient match is a key determining factor for improving transplant success and patient survival. This project's artificial intelligence (AI) model will guide transplant surgeons, physicians, and other healthcare professionals will deliver precise, accurate, quantitative information for real-time predictions.This Small Business Technology Transfer (STTR) Phase I project proposes artificial intelligence to predict outcomes after solid organ transplantation procedures. Clinicians currently consider several factors when determining organ allocation and candidate patient ranking on the recipient waitlist, including extent of disease pathology, functional status of the recipient, and intrinsic donor and recipient compatibility factors. Measures, indices and functional status scores have been designed to predict specific outcomes but are not easily combined into one optimized decision to guide organ allocation decisions. To date, no organ-matching predictive outcome model has comprehensively synthesized all available patient- and donor-specific variables at the time of transplantation. This project will train an artificial intelligence (AI) algorithm to comprehensively integrate all information available at the time of transplantation procedures (hundreds of variables) into a predictive model. An AI model of this nature would be a substantial improvement from linear models able to synthesize only a modest number of parameters (approximately 15) to date. It is expected that the proposed technology will predict both pre- and post-transplant survival more accurately than currently accepted models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小型企业技术转让(STTR)第一阶段项目的更广泛影响将是改善固体器官移植的结果。在过去的二十年里，在提高器官匹配成功率方面，几乎没有取得重大的临床或技术进步，目前预测生存结果的模型的准确性正在下降。临床医生非常需要有更好的决策支持工具。每隔12分钟，就会有一个新的人被添加到器官移植等待名单中，这个数字每年增长约5%。在一天内，21人在等待肾脏、肝脏或其他器官匹配时死亡。尽管每年进行3.65万例肾和肝移植，但患者对捐献器官的需求远远超过供应，因此迫切需要改善捐赠者和接受者的匹配。优化供体器官与患者的匹配是提高移植成功率和患者存活率的关键决定因素。该项目的人工智能(AI)模型将指导移植外科医生、医生和其他医疗保健专业人员为实时预测提供精确、准确、定量的信息。该小企业技术转移(STTR)第一阶段项目建议使用人工智能来预测固体器官移植手术后的结果。目前，临床医生在确定器官分配和候选患者在受者等待名单上的排名时会考虑几个因素，包括疾病病理程度、受者的功能状态以及内在的供者和受者相容因素。测量、指数和功能状态评分旨在预测特定的结果，但并不容易合并为一个优化的决定，以指导器官分配决定。到目前为止，还没有一个器官匹配的预测结果模型综合了移植时所有可用的患者和供体特定的变量。该项目将训练人工智能(AI)算法，将移植程序(数百个变量)时可用的所有信息全面整合到预测模型中。这种性质的人工智能模型将是对线性模型的实质性改进，到目前为止，线性模型只能合成少量(大约15个)参数。预计拟议的技术将比目前接受的模型更准确地预测移植前和移植后的存活率。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。