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

I-Corps项目的更广泛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。其中一种疾病是颈脊髓病（CM），这是一种脊髓被压缩在颈部的疾病。据估计，脊髓型颈椎病影响全球高达2%的成年人，但由于影像学和表现的微妙复杂性，往往未被充分诊断。脊髓型颈椎病的神经系统症状具有潜伏性和不可逆转的进展，可能需要手术干预，目前的诊断途径可能需要长达2年的时间，从症状出现到诊断/治疗，平均5次会诊。该技术旨在使用医学成像技术对脊髓进行自动化和标准化分析。脊髓疾病的早期发现、退行性病理的量化和外科候选手术的确定都是尚未满足的需求，这些需求可能会改善患者的预后、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和对行业生态系统的第一手调查来评估该技术的翻译潜力。该技术基于先前开发的基于机器学习的软件解决方案，该解决方案使用脊髓磁共振成像（MRI）和临床结果来识别患有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配准模型来注释解剖结构，并能够提取临床指标。此外，与外科医生和放射科医生合作开发了一种自动图像分析管道，可以生成新的临床指标，帮助描述脊柱疾病的病理特征。下一步包括在更大规模的临床成像数据集上开发模型，用于进一步的模型训练、验证和统计分析，以及结合额外的机器学习技术来提高模型在不同MRI采集技术上的鲁棒性。在未来，结果可用于直接转介患者进行手术咨询或其他适当的管理方案。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.