Abstract：
Nail psoriasis is a chronic condition characterized by nail dystrophy affecting the nail matrix and bed. The severity of nail psoriasis is commonly assessed using the Nail Psoriasis Severity Index (NAPSI), which evaluates the characteristics and extent of nail involvement. Although the NAPSI is numeric, reproducible, and simple, the assessment process is time-consuming and often challenging to use in real-world clinical settings. To overcome the time-consuming nature of NAPSI assessment, we aimed to develop a deep learning algorithm that can rapidly and reliably evaluate NAPSI, thereby providing numerous clinical and research advantages. We developed a dataset consisting of 7054 single fingernail images cropped from images of the dorsum of the hands of 634 patients with psoriasis. We annotated the eight features of the NAPSI in a single nail using bounding boxes and trained the YOLOv7-based deep learning algorithm using this annotation. The performance of the deep learning algorithm (DLA) was evaluated by comparing the NAPSI estimated using the DLA with the ground truth of the test dataset. The NAPSI evaluated using the DLA differed by 2 points from the ground truth in 98.6% of the images. The accuracy and mean absolute error of the model were 67.6% and 0.449, respectively. The intraclass correlation coefficient was 0.876, indicating good agreement. Our results showed that the DLA can rapidly and accurately evaluate the NAPSI. The rapid and accurate NAPSI assessment by the DLA is not only applicable in clinical settings, but also provides research advantages by enabling rapid NAPSI evaluations of previously collected nail images.
摘要：
指甲牛皮癣是一种慢性病症,其特征在于影响指甲基质和床的指甲营养不良。通常使用指甲牛皮癣严重程度指数（NAPSI）评估指甲牛皮癣的严重程度，评估指甲受累的特征和程度。虽然NAPSI是数字的，可重复,简单，评估过程耗时,而且在实际临床环境中使用时通常具有挑战性.为了克服NAPSI评估的耗时性质，我们的目标是开发一种能够快速可靠地评估NAPSI的深度学习算法，从而提供了许多临床和研究优势。我们开发了一个数据集，该数据集包括从634名牛皮癣患者的手背部图像中裁剪的7054个单指甲图像。我们使用边界框在单个指甲中注释了NAPSI的八个特征，并使用此注释训练了基于YOLOv7的深度学习算法。通过将使用DLA估计的NAPSI与测试数据集的地面实况进行比较来评估深度学习算法(DLA)的性能。在98.6％的图像中，使用DLA评估的NAPSI与地面实况相差2分。模型的准确度和平均绝对误差分别为67.6%和0.449。组内相关系数为0.876，表明一致性良好。我们的结果表明，DLA可以快速准确地评估NAPSI。DLA快速准确的NAPSI评估不仅适用于临床环境，但也提供了研究的优势，使快速NAPSI评估以前收集的指甲图像。
