SBIR Phase I Topic 402: Artificial Intelligence-Aided Imaging for Cancer Prevention, Diagnosis, and Monitoring

SBIR 第一阶段主题 402：用于癌症预防、诊断和监测的人工智能辅助成像

• 批准号: 10269836
• 负责人: XUE FENG
• 金额: $ 40万
• 依托单位: CARINA MEDICAL, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2021-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269836
• 关键词: Address; Algorithms; Artificial Intelligence; Cervical lymph node group; Clinical; Complex; Computer software; Data; Detection; Diagnosis; Evaluation; Head and Neck Cancer; Human; Image; Interobserver Variability; Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Modality; Monitor; Patient Care; Performance; Phase; Privatization; Reporting; Site; Small Business Innovation Research Grant; Software Framework; Visualization; automated segmentation; base; cancer diagnosis; cancer imaging; cancer prevention; clinical practice; clinically relevant; cloud based; cost; imaging modality; lymph nodes; novel; segmentation algorithm; usability

Image-based evaluation of lymph nodes is an essential step in cancer diagnosis, treatment and monitoring. Current clinical practice mostly uses qualitative or semi-quantitative measures in evaluation and thus suffers from inaccuracy due to intra- and inter-observer variability and increased human efforts. This becomes a more serious issue in head and neck cancers due to the large number of clinically relevant lymph nodes. In this project an AI-based automatic segmentation software will be developed for quantitative cervical lymph node evaluation to increase the accuracy and reduce the cost. However, there are a few challenges in developing and deploying such a software due to different clinical practices such as usage of different modalities (MRI and/or CT) and complex clinical workflow. To address these challenges, a novel AI algorithm that can handle the variability in imaging modalities and support incremental learning using site-specific data to enhance its robustness will be developed; a private-cloud-based software framework with high usability will then be developed to incorporate this algorithm and provide advanced visualization and reporting for clinical usage. This software will have high impact on all stages of patient care for head and neck cancers and can be further extended to other cancers.

基于图像的淋巴结评估是癌症诊断、治疗和监测的重要步骤。目前的临床实践大多使用定性或半定量的评估方法，因此由于观察者内部和观察者之间的差异以及人为努力的增加而导致不准确。由于大量临床相关淋巴结，这在头颈癌中成为一个更严重的问题。本项目将开发基于人工智能的自动分割软件，用于定量颈部淋巴结评估，以提高准确性并降低成本。然而，由于不同的临床实践，例如使用不同的模式（MRI 和/或 CT）和复杂的临床工作流程，开发和部署此类软件面临一些挑战。为了应对这些挑战，将开发一种新颖的人工智能算法，该算法可以处理成像模式的变化，并支持使用特定站点数据进行增量学习以增强其鲁棒性；然后将开发一个具有高可用性的基于私有云的软件框架，以纳入该算法并为临床使用提供先进的可视化和报告。该软件将对头颈癌患者护理的各个阶段产生重大影响，并可以进一步扩展到其他癌症。