Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms.

Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.

Raman spectroscopy may allow rapid noninvasive screening of keratitis and conjunctivitis.

Keratitis and conjunctivitis are the most common ocular diseases, their symptoms are similar and easy to confuse, however infectious conjunctivitis is highly contagious. If misdiagnosed, it may worsen the disease and pose a threat to public health.This is a preclinical study to propose a method for rapid and accurate screening of keratitis and conjunctivitis by combining tear Raman spectroscopy with deep learning models that may be applied to clinical applications in the future.The tears of 16 cases of keratitis patients, 13 cases of conjunctivitis patients and 46 cases of healthy subjects were collected, and their Raman spectra were compared and analyzed. By adding different decibels of Gaussian white noise to expand the data, the performance of the tear Raman spectra with a large sample size in the deep learning model was discussed. Principal component analysis (PCA), partial least squares (PLS) and maximum correlation minimum redundancy (mRMR) were used for feature extraction. The processed data were imported into convolutional neural network (CNN) and recurrent neural network (RNN) depth models for classification. After the data were enhanced and processed by PLS, the highest classification accuracy of healthy subjects and keratitis patients, healthy subjects and conjunctivitis patients, and keratitis and conjunctivitis patients reached 94.8%, 95.4%, and 92.7%, respectively. The results of this study show that the use of large sample tear Raman spectra data combined with PLS feature extraction and depth learning algorithms may have great potential in clinical screening of keratitis and conjunctivitis.

Detection of breast cancer of various clinical stages based on serum FT-IR spectroscopy combined with multiple algorithms.

BACKGROUND: Breast cancer screening is time consuming, requires expensive equipment, and has demanding requirements for doctors. Hence, a large number of breast cancer patients may miss screening and early treatment, which greatly threatens their health around the world. Infrared spectroscopy may be able to be used as a screening tool for breast cancer detection. Fourier transform infrared (FT-IR) spectroscopy of serum was combined with traditional machine learning algorithms to achieve an auxiliary diagnosis that could quickly and accurately distinguish patients with different stages of breast cancer, including stage 1 disease, from control subjects without breast cancer. MATERIALS AND METHODS: FT-IR spectroscopy were performed on the serum of 114 non-cancer control subjects, 35 patients with stage I, 43 patients with stage II, and 29 patients with stage III & IV breast cancer. Due to the experimental sample imbalance, we used the oversampling to process the four classes of sample. The oversampling selected Synthetic Minority Oversampling Technique (SMOTE). Subsequently, we used the random discarding method in undersampling to do experiments as well. The average FT-IR spectroscopy results for the four groups showed differences in phospholipids, nucleic acids, lipids, and proteins between non-cancer control subjects and breast cancer patients at different stages. Based on these differences, four classification models were used to classify stage I, II, III & IV breast cancer patients and non-cancer control subjects. First, standard normal variate transformation (SNV) was used to preprocess the original data, and then partial least squares (PLS) was used for feature extraction. Finally, the five models were established including extreme learning machine (ELM), k-nearest neighbor (KNN), genetic algorithms based on support vector machine (GA-SVM), particle swarm optimization-support vector machine (PSO-SVM) and grid search-support vector machine (GS-SVM). CONCLUSION: In oversampling experiment, the GS-SVM classifier obtained the highest average classification accuracy of 95.45 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 90 %; breast cancer stage II was 84.62 %; and breast cancer stage III & IV was 100 %. In undersampling experiment, the GA-SVM model obtained the highest average classification accuracy of 100 %; the diagnostic accuracy of non-cancer control subjects was 100 %; breast cancer stage I was 100 %; breast cancer stage II was 100 %; and breast cancer stage III & IV was 100 %. The results show that FT-IR spectroscopy combined with powerful classification algorithms has great potential in distinguishing patients with different stages of breast cancer from non-cancer control subjects. In addition, this research provides a reference for future multiclassification studies of cervical cancer, ovarian cancer and other female high-incidence cancers through serum FT-IR spectroscopy.

Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function.

BACKGROUND: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function. MATERIALS AND METHODS: Serum samples of 199 patients with abnormal thyroid function and 183 healthy patients were collected by infrared spectroscopy data and combined with different decibel noise for data expansion. The data were directly imported into three deep models: multilayer perceptron (MLP), a long-short-term memory network (LSTM), and a convolutional neural network (CNN), and 10-fold cross-validation was used to evaluate the performance of the model. RESULTS: The accuracy rates of the three models using the original data were 91.3 %, 88.6 % and 89.3 %, and the accuracy rates of the three models after data enhancement were 92.7 %, 93.6 % and 95.1 %. CONCLUSION: The results of this study indicated that the use of large sample serum infrared spectroscopy data combined with deep learning algorithms is a promising method for the diagnosis of abnormal thyroid function.

Discrimination of alcohol dependence based on the convolutional neural network.

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time.