Augmenting AI: Increasing Throughput, Quality and Validity of Imaging Data for Biomedical AI

增强人工智能：提高生物医学人工智能成像数据的吞吐量、质量和有效性

• 批准号: MR/V023314/1
• 负责人: Richard Salmon
• 金额: $ 76.43万
• 依托单位: ffei (United Kingdom)
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV023314%2F1
• 关键词: Augmenting; AI; Increasing; Throughput; Quality

The use of artificial intelligence (AI) in biological discovery and digital healthcare is increasing at rate. Digital imaging provides large quantities of diagnostic data in formats amenable to widespread adoption and automated analysis. As a result, research and commercial opportunities are arising to enhance and adapt current technologies to improve efficiency and automation with AI. However, in order to ensure the safest and most reliable deployment of these technologies in the digital era, there is a core requirement to ensure that the data upon which automated analyses are performed are of the highest quality and validity to ensure reliably positive outcomes. Furthermore, to warrant the maximum benefits of automation are reached, analysis devices must perform at the highest throughput and efficiency - a process that can be self-fulfilling by the integration of AI-workflow and Industry 4.0 approaches.To augment biomedical AI the applicant proposes a Portfolio Fellowship that will enhance, integrate and optimise FFEI's past, present and future technologies in bio-imaging and digital analysis workflow. This ambitious project will develop four core technologies, each enhancing a stage of the AI imaging pipeline. Technologies will start from different stages of market-readiness to ensure commercial and grant deliverables are manageable and realised. The overlapping stages of development will lead to a sustainable balance of commercial and research activities, whilst incrementally priming AI imaging markets for the emergence of modular, end-to-end AI technology from FFEI that can provide solutions that are adaptable and integrative to most segments of the digital healthcare market. A long-term objective is to integrate all the core developments into an FFEI 'smart lab' product, in which a single, modular device can perform all essential activities of biomedical AI laboratories. The Fellow aims to develop FFEI's biomedical capabilities by establishing a research environment to collect baseline metrics of current technology as a starting point for enhancement. The project will prototype new and established FFEI technology with flexibility to integrate emerging concepts from a network of academic partners. Ultimately, the objective will be for the Fellow's team to be able to dynamically test biological, mechanical and computational concepts to better achieve end-to-end optimisation of image data for AI. A key objective is to prove augmentation validity in refining end-to-end medical AI efficiency and reliability. To medically validate these technologies beyond concept, the Fellow will collaborate with NHS partners in parallel to FFEI productisation, allowing for iterative optimisations and case-data for accreditation. Enhancement of workflow processes with AI will require practical assessment and expert consultation, therefore the Fellow will create and lead a consortium of academic and NHS collaborators with expertise in biomedical R&D, diagnostics and AI analysis, further raising awareness through dissemination of peer-reviewed data.An essential component of the project's success will be the creation of a new 'AI imaging' team under the Fellow's leadership. FFEI have a highly experienced and established R&D imaging team into which the Fellow will recruit new members to grow FFEI's Life Science business, to bolster this team and explore new concepts whilst learning the skills of blending innovative thinking with commercial application. In return, the new team will bring fresh talent to FFEI, with anticipated recruiting of AI software, advanced opto-mechanical and biological experts, developing a team to take FFEI into a new age of AI augmentation and commercial success. The project will benefit the Fellow with personal development opportunities in business management, team leadership and commercial collaborations, under the mentorship and support of the FFEI executive team.

人工智能 (AI) 在生物发现和数字医疗保健中的应用正在快速增加。数字成像以适合广泛采用和自动分析的格式提供大量诊断数据。因此，增强和调整现有技术以提高人工智能效率和自动化的研究和商业机会不断涌现。然而，为了确保这些技术在数字时代最安全、最可靠的部署，有一个核心要求，即确保执行自动化分析的数据具有最高的质量和有效性，以确保可靠的积极结果。此外，为了保证实现自动化的最大效益，分析设备必须以最高的吞吐量和效率运行——这一过程可以通过人工智能工作流程和工业 4.0 方法的集成来自我实现。为了增强生物医学人工智能，申请人提出了一项组合奖学金，该奖学金将增强、集成和优化 FFEI 在生物成像和数字分析工作流程中过去、现在和未来的技术。这个雄心勃勃的项目将开发四项核心技术，每一项都增强人工智能成像管道的一个阶段。技术将从市场准备的不同阶段开始，以确保商业和赠款交付成果是可管理和实现的。重叠的开发阶段将实现商业和研究活动的可持续平衡，同时逐步为人工智能成像市场做好准备，以迎接 FFEI 模块化、端到端人工智能技术的出现，该技术可以提供适用于数字医疗市场大多数细分市场的解决方案。长期目标是将所有核心开发集成到 FFEI“智能实验室”产品中，其中单个模块化设备可以执行生物医学人工智能实验室的所有基本活动。该研究员旨在通过建立一个研究环境来收集当前技术的基线指标作为增强的起点，从而发展 FFEI 的生物医学能力。该项目将对新的和成熟的 FFEI 技术进行原型设计，并灵活地集成来自学术合作伙伴网络的新兴概念。最终，该研究员团队的目标是能够动态测试生物、机械和计算概念，以更好地实现人工智能图像数据的端到端优化。一个关键目标是证明增强端到端医疗人工智能效率和可靠性的有效性。为了对这些技术进行超越概念的医学验证，该研究员将与 NHS 合作伙伴在 FFEI 产品化的同时进行合作，从而进行迭代优化和案例数据进行认证。利用人工智能增强工作流程需要实际评估和专家咨询，因此该研究员将创建并领导一个由具有生物医学研发、诊断和人工智能分析专业知识的学术和 NHS 合作者组成的联盟，通过传播同行评审的数据进一步提高认识。该项目成功的一个重要组成部分将是在该研究员的领导下创建一个新的“人工智能成像”团队。 FFEI 拥有一支经验丰富且成熟的研发成像团队，院士将招募新成员来发展 FFEI 的生命科学业务，支持该团队并探索新概念，同时学习将创新思维与商业应用相结合的技能。作为回报，新团队将为 FFEI 带来新的人才，预计将招募人工智能软件、先进的光机和生物专家，组建一支团队，带领 FFEI 进入人工智能增强和商业成功的新时代。在 FFEI 执行团队的指导和支持下，该项目将为研究员提供业务管理、团队领导和商业合作方面的个人发展机会。

项目成果

Metrology for Digital Pathology. Digital pathology cross-theme project report

数字病理学计量。

• DOI: 10.47120/npl.as102
• 作者: Adeogun M

Physical Color Calibration of Digital Pathology Scanners for Deep Learning Based Diagnosis of Prostate Cancer

用于基于深度学习的前列腺癌诊断的数字病理扫描仪的物理颜色校准

• 发表时间: 2022
• 作者: Ji, X

The need for measurement science in digital pathology.

• DOI: 10.1016/j.jpi.2022.100157
• 发表时间: 2022
• 期刊: Journal of pathology informatics
• 作者: Romanchikova, Marina;Thomas, Spencer Angus;Dexter, Alex;Shaw, Mike;Partarrieau, Ignacio;Smith, Nadia;Venton, Jenny;Adeogun, Michael;Brettle, David;Turpin, Robert James
• 通讯作者: Turpin, Robert James