Noninvasive identification of IDH-mutational status in glioma patients using 1H-MRS is diagnostically and prognostically valuable. However, the most widely used short TE method is reported to be more subject to false diagnosis due to the severe spectral overlap of 2HG. We explored the potential applicability of deep learning in addressing this issue. A deep neural network that was trained on a large number of simulated spectra substantially improved the overall diagnostic accuracy on the patient spectra, compared to the LCModel analysis. As no spectral fitting is involved, our results are not subject to ambiguity arising from the CRLB-based data interpretation.