SERS spectra were used to distinguish tetracycline hydrochloride from the expired ones with an optical fiber nano-probe.

Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate‐specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross‐validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.

In this work, we presented a deep learning approach based on the LeNet-5 network for analysing and classifying recognisable shapes in urine sample images. The approach is based on shape analysis to recognise and classify red blood cells, white blood cells, epithelial cells and crystals observed under microscopes in urine samples. We modified the LeNet-5 neural network by changing the numbers of output nodes and convolutional layers. We compared the results of our method with those obtained by traditional feature extraction followed by back-propagation neural networks. Our testing showed that our method achieved a higher accuracy, sensitivity and specificity. The performance of our method demonstrated its broad applicability in urine sample analysis.