Application of machine learning in the diagnosis of vestibular disease.

Machine learning is considered a potential aid to support human decision making in disease prediction. In this study, we determined the utility of various machine learning algorithms in classifying peripheral vestibular (PV) and non-PV diseases based on the results of equilibrium function tests. A total of 1009 patients who had undergone our standardized neuro-otological examinations were recruited. We applied five supervised machine learning algorithms (random forest, adaboost, gradient boosting, support vector machine, and logistic regression). After preprocessing the data, optimizing the hyperparameters using GridSearchCV, and performing a final evaluation on the test set using scikit-learn, we evaluated the predictive capability using various performance metrics, namely, accuracy, F1-score, area under the receiver operating characteristic curve, precision, recall, and Matthews correlation coefficient (MCC). All five machine learning algorithms yielded satisfactory results; the accuracy of the algorithms ranged from 76 to 79%, with the support vector machine classifier having the highest accuracy. In cases where the predictions of the five models were consistent, the accuracy of the PV diagnostic results was improved to 83%, whereas it increased to 85% for the non-PV diagnostic results. Future research should increase the number of patients and optimize the classification methods to obtain the highest diagnostic accuracy.

Validation of a Highly Sensitive qPCR Assay for the Detection of Plasma Cell-Free Epstein-Barr Virus DNA in Nasopharyngeal Carcinoma Diagnosis.

Quantification of plasma cell-free Epstein Barr virus DNA (cf EBV DNA) has been suggested as a promising liquid biopsy assay for screening and early detection of nasopharyngeal carcinoma (NPC). However, the diagnostic value of this assay is currently not known in the population of Vietnam, one of the countries which contributed the most to the NPC cases. Herein, we have reported a highly sensitive quantitative polymerase chain reaction (qPCR)-based assay targeting cf EBV DNA for the detection of NPC. A standard curve with linear regression, R 2 = 0.9961 (range: 25-150 000 copies/mL) and a detection limit of 25 copies/mL were obtained using an EBV standard panel provided by the Chinese University of Hong Kong. The clinical performance of this assay was assessed using plasma samples obtained from 261 Vietnamese individuals. The optimized qPCR assay detected cf EBV DNA in plasma with a sensitivity of 97.4% and a specificity of 98.2%. The absolute quantitative results of pretreatment cf EBV DNA and patient overall clinical stages were statistically correlated (P < .05). In summary, the remarkably high sensitivity and specificity of our optimized qPCR assay strongly supports the wide use of cf EBV DNA quantification as a routine noninvasive method in early diagnosis and management of patients with NPC.