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

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。目前的临床实践主要使用定性或半定量的措施，在评价，因此遭受不准确性，由于内部和观察者之间的变化和增加的人力。由于大量的临床相关淋巴结，这在头颈癌中成为更严重的问题。本项目将开发一种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，如使用不同的模态（MRI和/或CT）和复杂的临床工作流程，在开发和部署这样的软件方面存在一些挑战。为了应对这些挑战，将开发一种新型的AI算法，该算法可以处理成像模式的可变性，并支持使用特定于站点的数据进行增量学习，以增强其鲁棒性;然后将开发一个基于私有云的软件框架，该框架具有高可用性，以整合该算法，并为临床使用提供高级可视化和报告。该软件将对头颈癌患者护理的所有阶段产生重大影响，并可进一步扩展到其他癌症。

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.