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

这个i-Corps项目的更广泛影响是开发了一种基于人工智能(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到压迫的情况。据估计，全球高达2%的成年人患有颈椎病，但由于成像和表现的微妙复杂性，往往被低估。脊髓型颈椎病具有潜伏的、不可逆转的神经症状进展，可能需要手术治疗，目前的诊断途径可能需要从症状开始到诊断/治疗长达2年，平均5次会诊。这项技术旨在使用医学成像对脊髓进行自动化和标准化分析。早期发现脊髓疾病、量化退行性病变和确定手术候选人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项目利用经验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。这项技术是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)和临床结果来识别特定退行性脊柱疾病的高危患者。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，还开发了一条自动图像分析管道，生成与外科医生和放射科医生合作设计的新的临床指标，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，以进一步进行模型训练、验证和统计分析，此外还包括加入额外的机器学习技术，以提高不同MRI采集技术上的模型性能的稳健性。未来，结果可能被用来直接转介患者进行手术咨询或其他适当的管理选择。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。

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.