Escudero Sanchez Lorena, Buddenkotte Thomas, Al Sa'd Mohammad, McCague Cathal, Darcy James, Rundo Leonardo, Samoshkin Alex, Graves Martin J, Hollamby Victoria, Browne Paul, Crispin-Ortuzar Mireia, Woitek Ramona, Sala Evis, Schönlieb Carola-Bibiane, Doran Simon J, Öktem Ozan
Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, Li Ka Shing Centre, Cambridge CB2 0RE, UK.
Diagnostics (Basel). 2023 Aug 30;13(17):2813. doi: 10.3390/diagnostics13172813.
Artificial intelligence (AI) methods applied to healthcare problems have shown enormous potential to alleviate the burden of health services worldwide and to improve the accuracy and reproducibility of predictions. In particular, developments in computer vision are creating a paradigm shift in the analysis of radiological images, where AI tools are already capable of automatically detecting and precisely delineating tumours. However, such tools are generally developed in technical departments that continue to be siloed from where the real benefit would be achieved with their usage. Significant effort still needs to be made to make these advancements available, first in academic clinical research and ultimately in the clinical setting. In this paper, we demonstrate a prototype pipeline based entirely on open-source software and free of cost to bridge this gap, simplifying the integration of tools and models developed within the AI community into the clinical research setting, ensuring an accessible platform with visualisation applications that allow end-users such as radiologists to view and interact with the outcome of these AI tools.
应用于医疗保健问题的人工智能（AI）方法已显示出巨大潜力，可减轻全球卫生服务负担，并提高预测的准确性和可重复性。特别是，计算机视觉的发展正在给放射图像分析带来范式转变，AI工具已经能够自动检测并精确勾勒肿瘤。然而，此类工具通常是在技术部门开发的，这些部门与工具实际使用能产生真正效益的地方仍然相互隔离。仍需付出巨大努力，首先在学术临床研究中，最终在临床环境中，使这些进展得以应用。在本文中，我们展示了一个完全基于开源软件且免费的原型流程，以弥合这一差距，简化将AI社区内开发的工具和模型集成到临床研究环境中的过程，确保有一个可访问的平台，其可视化应用程序能让放射科医生等终端用户查看这些AI工具的结果并与之交互。
