Ozmen Berk B, Pandey Sonia K, Schwarz Graham S
From the Department of Plastic Surgery, Cleveland Clinic, Cleveland, Ohio.
Plast Reconstr Surg Glob Open. 2024 Aug 23;12(8):e6132. doi: 10.1097/GOX.0000000000006132. eCollection 2024 Aug.
Lymphedema diagnosis relies on effective imaging of the lymphatic system. Indocyanine green (ICG) lymphography has become an essential diagnostic tool, but globally accepted protocols and objective analysis methods are lacking. In this study, we aimed to investigate artificial intelligence (AI), specifically convolutional neural networks, to categorize ICG lymphography images patterns into linear, reticular, splash, stardust, and diffuse.
A dataset composed of 68 ICG lymphography images was compiled and labeled according to five recognized pattern types: linear, reticular, splash, stardust, and diffuse. A convolutional neural network model, using MobileNetV2 and TensorFlow, was developed and coded in Python for pattern classification.
The AI model achieved 97.78% accuracy and 0.0678 loss in categorizing images into five ICG lymphography patterns, demonstrating high potential for enhancing ICG lymphography interpretation. The high level of accuracy with a low loss achieved by our model demonstrates its effectiveness in pattern recognition with a high degree of precision.
This study demonstrates that AI models can accurately classify ICG lymphography patterns. AI can assist in standardizing and automating the interpretation of ICG lymphographic imaging.
淋巴水肿的诊断依赖于淋巴系统的有效成像。吲哚菁绿（ICG）淋巴管造影已成为一种重要的诊断工具，但全球缺乏公认的方案和客观分析方法。在本研究中，我们旨在研究人工智能（AI），特别是卷积神经网络，将ICG淋巴管造影图像模式分类为线性、网状、飞溅、星尘和弥漫性。
编制了一个由68张ICG淋巴管造影图像组成的数据集，并根据线性、网状、飞溅、星尘和弥漫性这五种公认的模式类型进行标记。使用MobileNetV2和TensorFlow开发了一个卷积神经网络模型，并在Python中进行编码以进行模式分类。
该AI模型在将图像分类为五种ICG淋巴管造影模式时，准确率达到97.78%，损失率为0.0678，显示出增强ICG淋巴管造影解读的巨大潜力。我们的模型实现的高准确率和低损失表明其在高精度模式识别方面的有效性。
本研究表明AI模型可以准确地对ICG淋巴管造影模式进行分类。AI可以协助标准化和自动化ICG淋巴造影成像的解读。
