Brzezińska Olga Elżbieta, Rychlicki-Kicior Krzysztof Andrzej, Makowska Joanna Samanta
Department of Rheumatology, Medical University of Lodz, Poland.
University of Economics and Human Sciences in Warsaw, Poland.
Reumatologia. 2024;62(5):346-350. doi: 10.5114/reum/194040. Epub 2024 Nov 9.
Capillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capillary counting and image classification as normal or pathological.
The study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed.
The results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%.
The pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.
毛细血管镜检查是一种简单的甲襞毛细血管成像方法，用于诊断系统性硬化症谱系疾病。然而，对毛细血管图像的评估既耗时又主观。这使得它难以用于不同医生评估的研究之间的详细比较。这项初步研究旨在验证用于自动毛细血管计数以及将图像分类为正常或病理图像的软件。
该研究基于对200张毛细血管镜图像的评估，这些图像取自患有系统性硬化症或硬皮病谱系疾病的患者以及健康人。使用Dinolite MEDL4N Pro进行毛细血管镜检查。每张图像都进行了手动分析，并使用工作软件进行描述。使用fast.ai库（基于PyTorch）对神经网络进行训练。选择了ResNet - 34深度残差神经网络；在GPU优化（P5000 GPU）环境中，使用最先进的Darknet - YoloV3神经网络进行了10折交叉验证以及验证集和测试集的操作。为了计算1毫米的毛细血管数量，设计了一种额外的检测机制。
将神经网络训练得到的结果与手动分析得到的结果进行比较。在正确图像与病理图像分类中，自动工具相对于手动评估的训练组灵敏度为89.0%，特异性为89.4%，在验证中分别为89.0%和86.9%。对于1毫米内毛细血管的平均数量，在感兴趣区域内检测到的真实图像的精度为96.48%。
用于全自动毛细血管镜图像评估的初步软件可以成为快速分类正常和异常毛细血管镜检查模式的有用工具。此外，它还能让人快速计算毛细血管数量。未来，该工具将得到进一步开发，并有可能获得独立于检查者经验的完整成像特征。
