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)的力量应用于皮肤的光学相干层析成像(OCT)成像，以显著提高这些OCT成像设备的速度、准确性和实用性，供皮肤科医生和临床科学家使用。目前，最终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，然后手动分析图像提取数据，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很耗时，有点主观，并且需要观察者培训。这阻碍了使用OCT成像来加速治疗皮肤癌、特应性皮炎和牛皮癣等皮肤病的药物开发，这些疾病是数十亿美元的市场。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，我们提议从人工智能专家曼彻斯特成像有限公司(MIL)借调一位领先的专家到主办组织Michelson Diagnostics Ltd，该公司是世界领先的VivoSight光学相干层析(OCT)皮肤成像和测量系统的英国中小企业制造商，在两年多的时间里开发和测试用于OCT的新型机器学习算法。将OCT广泛应用于皮肤科研究的关键障碍是OCT图像需要训练有素的专家来解读它们，而且图像分析本质上是手动的。皮肤科医生往往时间紧迫，可能没有时间学习如何做到这一点，因此，挑战是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物自动量化迈克尔逊用户群要求的OCT图像标记物的例子有：表皮增厚(特应性皮炎)真皮-表皮交界处失去清晰度(皮肤癌)检测真皮中的肿瘤巢穴及其侵袭深度/范围(皮肤癌)血管密度增加(所有炎症性疾病)血管形状/扭曲的变化(黑色素瘤)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、与最终用户临床科学用户群建立密切联系，形成一个高度专注和积极进取的多学科团队，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.