Caenorhabditis elegans ( C. elegans ) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening and genetic screening studies. The effects of various antiaging interventions on the rate of aging in the early stage of the aging process can be determined based on the quantification of physiological age. However, in general, the age of C. elegans is evaluated via human visual inspection of morphological changes based on personal experience and subjective judgment. For example, the rate of motor activity decay has been used to predict lifespan in early- to mid-stage aging. Using image processing, the physiological age of C. elegans can be measured and then classified into periods or classes from childhood to elderhood (e.g., 3 periods comprising days 0–2, 4–6 and 10–12) by using texture entropy (Shamir, L. et al., 2009). Our dataset consists of 913 microscopic images of C. elegans , with approximately 60 images per day from day 1 to day 14 of adulthood. We present quantitative methods to measure the physiological age of C. elegans with convolution neural networks (CNNs), which can measure age with a granularity of days rather than periods. The methods achieved a mean absolute error (MAE) of less than 1 day for the measured age of C. elegans . In our experiments, we found that after training and testing our dataset, 5 popular CNN models, 50-layer residual network (ResNet50), InceptionV3, InceptionResNetV2, 16-layer Visual Geometry Group network (VGG16) and MobileNet, measured the physiological age of C. elegans with an average testing MAE of 1.58 days. Furthermore, based on the results, we propose two models, one model for linear regression analysis and the other model for logistic regression, that combine a CNN model and a new attribute: curved_or_straight. The linear regression analysis model achieved a test MAE of 0.94 days; the logistic regression model achieved an accuracy of 84.78 percent with an error tolerance of 1 day.

中文翻译：

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使用卷积神经网络测量秀丽隐杆线虫的生理年龄

秀丽隐杆线虫 ( C. elegans ）由于其寿命短，是研究衰老的流行且优秀的模型。精确测量生理年龄的方法C. elegans 是非常需要的，特别是对于抗衰老药物筛选和基因筛选研究。可以根据生理年龄的量化来确定各种抗衰老干预措施对衰老过程早期衰老速度的影响。不过一般来说年龄秀丽隐杆线虫是通过基于个人经验和主观判断的形态变化的人类视觉检查来评估的。例如，运动活动衰减率已被用于预测早期至中期衰老的寿命。使用图像处理，生理年龄通过使用纹理熵 (Shamir, L. 等人， 2009）。我们的数据集由 913 张显微图像组成C. elegans ，从成年的第 1 天到第 14 天，每天大约有 60 张图像。我们提出了测量生理年龄的定量方法带有卷积神经网络 (CNN) 的秀丽隐杆线虫，它可以用天而不是周期来测量年龄。对于测量的年龄，这些方法实现了小于 1 天的平均绝对误差 (MAE)秀丽隐杆线虫。在我们的实验中，我们发现在训练和测试我们的数据集后，5 个流行的 CNN 模型、50 层残差网络（ResNet50）、InceptionV3、InceptionResNetV2、16 层视觉几何组网络（VGG16）和 MobileNet，测量了线虫的平均测试 MAE 为 1.58 天。此外，根据结果，我们提出了两个模型，一个模型用于线性回归分析，另一个模型用于逻辑回归，它们结合了 CNN 模型和一个新属性：curved_or_straight。线性回归分析模型实现了0.94天的测试MAE；逻辑回归模型达到了 84.78% 的准确率，误差容限为 1 天。