Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment algorithm. AI-based PRO score assessment was compared to the imaging experts’ visual score. Additionally, undulation of the DEJ, the number of protrusions detected within the image, and the maximum depth of the protrusions were computed. Our results show that AI-automated PRO grading is highly comparable to the visual score, with an agreement of 71.3% for the lesions evaluated. Furthermore, this AI-based assessment was significantly faster than the regular visual PRO score assessment. The results confirm our previous findings of the pilot study in a larger cohort that the AI-based grading of LC-OCT images is a reliable and fast tool to optimize the efficiency of visual PRO score grading. This technology has the potential to improve the accuracy and speed of AK diagnosis and may lead to better clinical outcomes for patients.
光化性角化病（AK）是一种常见的皮肤原位癌，可进展为侵袭性鳞状细胞癌。线场共聚焦光学相干断层扫描（LC-OCT）作为一种无创成像技术，有助于其诊断。近期开发的机器学习算法能够基于LC-OCT图像中真皮-表皮交界处（DEJ）的突起，自动评估AK的PRO评分。本研究采用包含80个经组织学确诊的AK病灶、共计19,898张LC-OCT图像的数据集，测试了先前已验证的基于人工智能（AI）的LC-OCT评估算法的性能。将AI评估的PRO评分与影像专家的视觉评分进行比较，同时计算了DEJ的波动程度、图像中检测到的突起数量以及突起的最大深度。结果显示，AI自动PRO分级与视觉评分具有高度可比性，在评估病灶中两者一致率达71.3%。此外，基于AI的评估速度显著快于常规视觉PRO评分。这些结果在更大规模队列中证实了我们先前试点研究的发现：基于AI的LC-OCT图像分级是一种可靠且快速的方法，可优化视觉PRO评分效率。该技术有望提高AK诊断的准确性和速度，从而为患者带来更好的临床预后。
肿瘤(癌症)患者之家