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、治疗和监测的重要环节。目前的临床实践大多使用定性或半定量的方法进行评估，因此由于观察者内和观察者间的变异性以及增加的人力努力而受到不准确的影响。由于有大量临床相关的淋巴结，这在头颈部癌症中变得更加严重。在本项目中，将开发一个基于人工智能的自动分割软件，用于定量评估颈部淋巴结，以提高准确率，降低成本。然而，由于不同的临床实践，如使用不同的方式(MRI和/或CT)和复杂的临床工作流程，开发和部署这样的软件存在一些挑战。为了应对这些挑战，将开发一种新的人工智能算法，该算法可以处理成像模式的变异性，并支持使用现场特定数据的增量学习以增强其稳健性；然后将开发基于私有云的高可用性软件框架，以整合该算法并为临床使用提供高级可视化和报告。该软件将对头颈部癌症患者护理的各个阶段产生很大影响，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.