While morphological examination is the most widely used for Aspergillus identification in clinical laboratories, PCR-sequencing and MALDI-TOF MS are emerging technologies in more financially-competent laboratories. However, mycological expertise, molecular biologists and/or expensive equipment are needed for these. Recently, artificial intelligence (AI), especially image recognition, is being increasingly employed in medicine for fast and automated disease diagnosis. We explored the potential utility of AI in identifying Aspergillus species. In this proof-of-concept study, using 2813, 2814 and 1240 images from four clinically important Aspergillus species for training, validation and testing, respectively; the performances and accuracies of automatic Aspergillus identification using colonial images by three different convolutional neural networks were evaluated. Results demonstrated that ResNet-18 outperformed Inception-v3 and DenseNet-121 and is the best algorithm of choice because it made the fewest misidentifications (n = 8) and possessed the highest testing accuracy (99.35%). Images showing more unique morphological features were more accurately identified. AI-based image recognition using colonial images is a promising technology for Aspergillus identification. Given its short turn-around-time, minimal demand of expertise, low reagent/equipment costs and user-friendliness, it has the potential to serve as a routine laboratory diagnostic tool after the database is further expanded.

中文翻译：

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通过基于人工智能的图像识别自动识别临床上重要的曲霉菌属物种：概念验证研究。


虽然形态学检查是临床实验室中用于曲霉菌鉴定的最广泛方法，但 PCR 测序和 MALDI-TOF MS 是经济能力更强的实验室中的新兴技术。然而，这些需要真菌学专业知识、分子生物学家和/或昂贵的设备。最近，人工智能 （AI），尤其是图像识别，越来越多地用于医学的快速和自动化疾病诊断。我们探讨了 AI 在识别曲霉菌属物种方面的潜在效用。在这项概念验证研究中，分别使用来自四种临床上重要的曲霉菌物种的 2813、2814 和 1240 张图像进行训练、验证和测试;评估了通过三种不同的卷积神经网络使用菌落图像自动识别曲霉菌的性能和准确性。结果表明，ResNet-18 的性能优于 Inception-v3 和 DenseNet-121，并且是最好的选择算法，因为它的错误识别最少 （n = 8） 并且具有最高的测试准确性 （99.35%）。显示更多独特形态特征的图像被更准确地识别出来。使用殖民地图像的基于 AI 的图像识别是一种很有前途的曲霉菌识别技术。鉴于其周转时间短、对专业知识的需求最低、试剂/设备成本低且用户友好，在数据库进一步扩展后，它有可能作为常规实验室诊断工具。