In International journal of pharmaceutics: X
Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development.
Iwata Hiroaki, Hayashi Yoshihiro, Hasegawa Aki, Terayama Kei, Okuno Yasushi
2022-Dec
API, active pharmaceutical ingredient, Artificial intelligence, CNNs, convolutional neural networks, Convolutional neural networks, Excipients, FN, false negative, FP, false positive, Grad-CAM, gradient-weighted class activation mapping, Machine learning, Powder, SEM, scanning electron microscopy, Scanning electron microscope, TN, true negative, TP, true positive
