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.