Andersson Emil, Hult Jenny, Troein Carl, Stridh Magne, Sjögren Benjamin, Pekar-Lukacs Agnes, Hernandez-Palacios Julio, Edén Patrik, Persson Bertil, Olariu Victor, Malmsjö Malin, Merdasa Aboma
Centre for Environmental and Climate Science, Lund University, Lund, Sweden.
Department of Clinical Sciences Lund, Ophthalmology, Lund University, Lund, Sweden.
iScience. 2024 Apr 1;27(5):109653. doi: 10.1016/j.isci.2024.109653. eCollection 2024 May 17.
In the dawning era of artificial intelligence (AI), health care stands to undergo a significant transformation with the increasing digitalization of patient data. Digital imaging, in particular, will serve as an important platform for AI to aid decision making and diagnostics. A growing number of studies demonstrate the potential of automatic pre-surgical skin tumor delineation, which could have tremendous impact on clinical practice. However, current methods rely on having ground truth images in which tumor borders are already identified, which is not clinically possible. We report a novel approach where hyperspectral images provide spectra from small regions representing healthy tissue and tumor, which are used to generate prediction maps using artificial neural networks (ANNs), after which a segmentation algorithm automatically identifies the tumor borders. This circumvents the need for ground truth images, since an ANN model is trained with data from each individual patient, representing a more clinically relevant approach.
在人工智能（AI）初露曙光的时代，随着患者数据数字化程度的不断提高，医疗保健领域即将经历重大变革。尤其是数字成像，将成为人工智能辅助决策和诊断的重要平台。越来越多的研究表明自动术前皮肤肿瘤轮廓描绘具有潜力，这可能会对临床实践产生巨大影响。然而，目前的方法依赖于已经识别出肿瘤边界的真实图像，而这在临床上是不可能实现的。我们报告了一种新颖的方法，其中高光谱图像提供来自代表健康组织和肿瘤的小区域的光谱，这些光谱用于使用人工神经网络（ANN）生成预测图，然后分割算法自动识别肿瘤边界。这避免了对真实图像的需求，因为ANN模型是使用来自每个患者的数据进行训练的，代表了一种更具临床相关性的方法。
