Development of Artificial Intelligence OCT Biomarkers for Accelerated Skin Disease Research and Diagnosis

我们的愿景是将机器学习和计算机视觉（也称为“人工智能”或AI）的力量带到光学相干断层扫描（OCT）皮肤成像的应用中，以显着提高这些OCT成像设备对皮肤科医生和临床科学家的速度，准确性和实用性。目前，终端用户临床医生和科学家使用OCT成像设备捕获皮肤的亚表面图像，然后他们手动分析图像以提取数据，然后用于评估药物治疗对皮肤病的影响。OCT成像比皮肤活检更快，侵入性更小，成本更低，但图像分析步骤仍然耗时，有些主观，并且需要对观察者进行培训。这阻碍了使用OCT成像来加速皮肤癌、特应性皮炎和牛皮癣等皮肤病的药物开发，而这些疾病的市场价值高达数十亿美元。我们相信，强大的机器学习算法将改变临床科学家和临床用户使用OCT皮肤成像来研究和开发新药的方式。为了实现这一愿景，我们建议从人工智能专家曼彻斯特成像有限公司（MIL）派驻主办组织迈克尔逊诊断有限公司（世界领先的VivoSight光学相干断层扫描（OCT）皮肤成像和测量系统的英国中小企业制造商），在2年内开发和测试用于OCT的新型机器学习算法。将OCT广泛应用于皮肤科研究的主要障碍是OCT图像需要训练有素的专家来解释它们。而且图像分析本质上是手工的。皮肤科医生通常没有时间，可能没有时间学习如何做到这一点，而手工分析的本质可能会产生不必要的偏见。因此，挑战在于通过以下方式减少采用的障碍：自动识别常见皮肤病的图像标记自动量化图像标记迈克尔逊用户群要求的OCT图像标记示例如下：表皮增厚（特应性皮炎）真皮-表皮交界处定义不清（皮肤癌）真皮肿瘤“巢”的检测及其侵袭深度/范围（皮肤癌）血管密度增加（所有炎症性疾病）血管形状/扭曲改变（黑色素瘤）这一挑战只能通过将人工智能算法（图像处理/机器学习）和OCT成像技术（激光物理）的专业知识结合起来来应对。光学和仪器)与最终用户临床科学用户群密切联系，形成一个高度专注和积极的多学科团队，他们将在真实的临床数据上开发和测试候选算法。

Our vision is to bring the power of machine learning and computer vision (also known as 'Artificial Intelligence' or AI) to the application of Optical Coherence Tomography (OCT) imaging of skin, in order to dramatically improve the speed, accuracy and utility of these OCT imaging devices to dermatologists and clinical scientists.At present, end-user clinicians and scientists use OCT imaging devices to capture sub-surface images of skin and then they manually analyse the images to extract data, which is then used to assess the effects of pharmaceutical treatments on skin diseases. OCT imaging is faster, less invasive and less costly than taking skin biopsies, but the image analysis step is still time-consuming, somewhat subjective, and requires observer training. This is a hindrance to the use of OCT imaging to accelerate drug development for skin diseases like skin cancer, atopic dermatitis and psoriasis, which are multi-billion-$ markets.We believe that powerful machine learning algorithms will transform how OCT skin imaging is used by clinical scientists and clinical users to research and develop new drugs. To achieve this vision, we propose seconding a leading expert from AI specialists Manchester Imaging Ltd (MIL) to the host organisation Michelson Diagnostics Ltd, UK SME manufacturer of the world-leading VivoSight Optical Coherence Tomography (OCT) skin imaging and measurement system, over 2 years, to develop and test novel machine learning algorithms for OCT.Key barriers to the wider adoption of OCT for dermatology research is that the OCT images require trained experts to interpret them, and also that the image analysis is somewhat manual in nature. Dermatologists are often time-poor and may not have time to learn how to do this, and the manual nature of the analysis creates potential for unwanted bias.Therefore the challenge is to reduce the barriers to adoption by: Automatically identifying image-markers for common skin diseases Automatically quantifying the image-markers Examples of OCT image-markers requested by Michelson's user base are: Thickened epidermis (Atopic Dermatitis) Loss of definition of dermis-epidermis junction (skin cancer) Detection of tumour 'nests' in the dermis and their invasion-depth/extent (skin cancer) Increase in blood vessel density (all inflammatory diseases) Alterations in blood vessel shape/tortuosity (melanoma)The challenge can only be met by bringing together expertise in AI-algorithms (image processing/machine learning) and OCT imaging technology (laser physics, optics and instrumentation) with close links to the end-user clinical science user base, to form a highly focused and motivated multi-disciplinary team and who will develop and test candidate algorithms on real clinical data.