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.

该I-Corps项目的更广泛影响是开发基于人工智能（AI）的工具，以量化和诊断脊髓疾病。一种这样的疾病是宫颈脊髓病（CM），脊髓在颈部被压缩。据估计，宫颈骨髓病在全球最多影响2％的成年人，但由于成像和表现的微妙复杂性，通常被诊断不足。宫颈骨髓病具有神经系统症状的阴险和不可逆转的进展，可能需要手术干预，并且当前的诊断途径可能需要从症状发作到诊断/治疗，平均进行5次咨询，最多需要2年。该技术旨在使用医学成像自动化和标准化脊髓分析。早期发现脊髓疾病，退化性病理学的定量以及手术候选者的鉴定都是未满足的需求，可以改善患者的预后，降低成本并减轻CM患者的医疗负担。该I-Corps项目利用体验性学习与行业生态系统的第一手研究相结合，以评估该技术的翻译潜力。该技术基于先前开发基于机器学习的软件解决方案，该软件解决方案使用脊柱磁共振成像（MRI）和临床发现来鉴定特定退行性脊柱疾病的高风险患者。该技术使用深度学习和图像登记模型来注释解剖结构并可以提取临床指标。此外，已经开发了一条自动图像分析管道，该管道生成了与外科医生和放射科医生合作设计的新型临床指标，这些指标有助于脊柱疾病病理的表征。下一步包括开发大规模临床成像数据集上的模型，除了合并其他机器学习技术以提高模型性能在不同的MRI获取技术上的鲁棒性外，还包括进行进一步的模型培训，验证和统计分析。将来，结果可用于直接将患者转介出手术咨询或其他适当的管理选择。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来支持的。