I-Corps: Translation potential of an Artificial Intelligence (AI) approach to quantify and diagnose spinal cord diseases

I-Corps：人工智能 (AI) 方法量化和诊断脊髓疾病的转化潜力

• 批准号: 2416075
• 负责人: Turner Baker
• 金额: $ 5万
• 依托单位: Icahn School of Medicine at Mount Sinai
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2416075&HistoricalAwards=false
• 关键词: Corps; Translation; potential; Artificial; Intelligence

The broader impact of this I-Corps project is the development of an artificial intelligence (AI)-based tool to quantify and diagnose spinal cord diseases. One such disease is cervical myelopathy (CM), a condition in which the spinal cord is compressed within the neck. Cervical myelopathy is estimated to affect up to 2% of adults globally, but is often underdiagnosed due to subtle complexities in imaging and presentation. Cervical myelopathy has an insidious and irreversible progression of neurological symptoms that may require surgical intervention, and the current path to diagnosis may take up to 2 years from symptom onset to diagnosis/treatment with an average of 5 consults. This technology is designed to automate and standardize analysis of the spinal cord using medical imaging. Early detection of spinal cord disease, quantification of degenerative pathology, and identification of surgical candidates are all unmet needs that may improve patient outcomes, decrease cost, and reduce medical burden for CM patients.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The technology is based on the prior development of a machine learning-based software solution that uses spinal magnetic resonance imaging (MRI) and clinical findings to identify patients at high risk for particular degenerative spinal diseases. The technology uses deep learning and image registration models to annotate anatomical structures and enable extraction of clinical metrics. In addition, an automated image analysis pipeline has been developed that generates novel clinical metrics designed in collaboration with surgeons and radiologists that aid in the characterization of spinal disease pathology. Next steps include developing models on larger-scale clinical imaging datasets for further model training, validation, and statistical analysis, in addition to incorporating additional machine learning techniques to improve the robustness of model performance on different MRI acquisition techniques. In the future, the results may be used to directly refer patients for surgical consultations or to other appropriate management options.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.

I-Corps 项目更广泛的影响是开发一种基于人工智能 (AI) 的工具来量化和诊断脊髓疾病。其中一种疾病是脊髓型颈椎病（CM），这是一种脊髓在颈部受压的疾病。据估计，脊髓型颈椎病影响着全球高达 2% 的成年人，但由于影像学和表现的微妙复杂性，常常导致诊断不足。脊髓型颈椎病具有隐匿且不可逆的神经系统症状进展，可能需要手术干预，目前的诊断路径从症状出现到诊断/治疗可能需要长达 2 年的时间，平均需要 5 次咨询。该技术旨在利用医学成像对脊髓进行自动化和标准化分析。脊髓疾病的早期检测、退行性病理学的量化以及手术候选者的识别都是未满足的需求，这些需求可能会改善患者的治疗结果、降低成本并减轻 CM 患者的医疗负担。这个 I-Corps 项目利用体验式学习与第一- 对行业生态系统进行手工调查，以评估该技术的转化潜力。该技术基于先前开发的基于机器学习的软件解决方案，该解决方案使用脊柱磁共振成像（MRI）和临床发现来识别患有特定退行性脊柱疾病高风险的患者。该技术使用深度学习和图像配准模型来注释解剖结构并能够提取临床指标。此外，还开发了一种自动图像分析流程，可生成与外科医生和放射科医生合作设计的新颖的临床指标，有助于表征脊柱疾病病理学。下一步包括在更大规模的临床成像数据集上开发模型，以进行进一步的模型训练、验证和统计分析，此外还结合其他机器学习技术来提高不同 MRI 采集技术上模型性能的稳健性。将来，这些结果可能会被用来直接将患者转诊至外科会诊或其他适当的管理方案。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。