STTR Phase I: A Clinical Decision Support Tool for Brain Magnetic Resonance Imaging (MRI) in Children

这一小型企业技术转让(STTR)第一阶段项目的更广泛影响/商业潜力是使医生能够最大限度地利用从昂贵的儿科磁共振成像(MRI)脑部扫描中提取的诊断信息。每年大约有350万次脑部MRI扫描，平均价格从500美元到2000美元不等。尽管获取和解读扫描图像的成本很高，但医生通常只使用图像的肉眼检查来诊断异常。与核磁共振的总成本相比，拟议中的软件将相对便宜，有望让医生对大脑中的关键结构进行准确的测量。这一点很重要，因为发育性疾病和其他疾病会导致肉眼无法轻易看到的小体积和表面积变化。通过及早发现这些变化，医生应该能够在早期阶段治疗疾病，潜在地带来更好的生活质量和经济节省。拟议中的工具还有望通过测量药物或治疗对大脑特定结构的影响，使医生能够更好地跟踪治疗结果。最终，预计这项拟议的技术将提高医疗质量并降低医疗成本。该计划利用当前计算机技术的进步，如机器学习和计算机视觉，自动测量儿童大脑关键部位的体积、表面积和形状。然后将这些测量结果与0-12岁儿童的大型数据库进行比较，以确定他们是否偏离了正常水平。尽管核磁共振成像越来越多地被用作儿童脑部疾病的首选诊断工具，因为缺乏辐射，但还没有一种工具可以让医生从核磁共振扫描中准确地测量儿童大脑的变化。因为，在儿童中，大脑随着她/他的成长而迅速变化，很难从视觉上确定这些变化是由于正常发育还是由于疾病。通过准确测量大脑，并将其与提议团队已经收集的大型数据库进行比较，预计儿科医生将能够确定儿童的大脑是否偏离了正常。它还可以让医生更好地选择治疗方法，并监测患者对特定治疗的反应。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to enable doctors to maximize the diagnostic information extracted from costly Pediatric Magnetic Resonance Imaging (MRI) scans of the brain. Annually, approximately 3.5 million brain MRI scans are performed with average prices with interpretation from $500 - $2,000. Even though the scans are expensive to acquire and interpret, doctors generally only use visual inspection of the images to diagnose abnormalities. The proposed software will be relatively cheap compared to the overall cost of an MRI and is expected to let doctors make accurate measurements of key structures in the brain. This is important because developmental and other diseases can cause small volume and surface area changes that cannot be easily seen by the human eye. By detecting these changes early, doctors should be able to treat the disease at early stages, potentially leading to better quality of life and financial savings. The proposed tool is also expected to enable doctors to better track treatment outcomes, by measuring the effect a drug or treatment has on particular structures in the brain. Ultimately, it is expected that the proposed technology will improve quality of care as well as reduce healthcare costs.The proposed project leverages current advances in computational technology such as machine learning and computer vision to automatically measure volume, surface area and shapes in critical parts of the brain in children. These measurements are then compared to a large database of children over the ages of 0-12 years to determine if they have deviated from normal. Although MRIs have been increasing adopted as the diagnostic tool of choice for childhood brain disorders due to the lack of radiation, there has yet to be a tool that allows doctors to accurately measure changes in a child's brain from MRI scans. Because, in children, the brain is rapidly changing as she/he grows, it is difficult to determine visually whether the changes are due to normal development or disease. By measuring the brain accurately and comparing it to a large database already collected by the proposing team, it is expected that a pediatric doctor will be able to determine whether a child's brain has deviated from normal. It could also allow physicians to better select treatments and monitor the patient response to a specific therapy.