## 1 Introduction
Therefore, by fitting an experimental Balmer-\(\alpha\) line emitted by hydrogen isotopes, it is possible to infer various parameters like the temperatures and fractions of the different neutral populations in the emissive zone, and more importantly the fraction of each hydrogen isotope and, hence, the isotopic ratio. Therefore, by applying this spectroscopic technique to spectra measured along various lines of sights, one can draw a cartography of the content of a given hydrogen isotope of the considered device. For instance, [PERSON] et al. [11,12] have applied this technique to JET data. In the previous references [7, 8, 9], even if the main objective was to determine the different neutral populations and characterize the dominant recycling mechanisms, the fractions of hydrogen isotopes were also determined. Usually, it is necessary to consider the co-existence of at least two neutral populations, each with its velocity distribution for each hydrogen isotope to fit the observed spectra. The presence of the magnetic field may lead to even more complications if its intensity is not sufficiently enough that the strong field approximation can be used. It is obvious that this technique relies on the fit of the whole line profiles of the considered line, i.e., the Balmer-\(\alpha\) emitted by hydrogen isotopes. Besides, for its cost in terms of CPU time, this technique is convenient for post-operation interpretation of the measured spectra and cannot be used for real-time plasma controls. In a nuclear fusion reactor working with D-T mixtures, a real-time control of the tritium content must be necessary to ensure good performances and meet safety conditions. Inthis context, a fast method allowing the determination of the isotopic ratio is necessary. In this paper, we propose to combine artificial intelligence with plasma spectroscopy to determine the isotopic ratio of hydrogen in tokamaks and stellarators for two objectives: promote plasma control through the monitoring of the hydrogen isotope content and predict future operations from available experimental data. Here, we will only present the proof of principle of the proposed method. In this method, instead of searching to fit the whole spectrum of the Balmer-\(\alpha\) line, we used only some of its spectroscopic characteristics as input features in a deep-learning algorithm to determine the isotopic ratio for H-D mixtures. It is not the first time that machine-learning is introduced in plasma physics and plasma spectroscopy. Indeed, in 2021, [PERSON] et al. [13] used a multiple regression algorithm from the open source Sickit-Learn package machine-learning library [14] to analyze helium emission lines to determine the plasma electron density and temperature in the linear divertor simulator Magnum-PSI [15]. In [15], [PERSON] et al. used the standard neutral helium line intensity ratio technique for electron density and temperature determination. This technique requires the combination of spectroscopic intensity measurements of specific helium lines and a collisional-radiative modeling of the excited upper levels involved in the considered radiative transitions. The same regression technique was also used by [PERSON], [PERSON] and [PERSON] [16] to predict electron density and temperature from helium lines, this technique being validated using independent measurements using Langmuir probes. In [13], the assessment was realized thanks to independent measurements made with a Thomson Scattering system. In other physics fields, machine learning has been used for several years. For instance, in astrophysics, [PERSON] et al. [17] have published a work about an open-source code using deep learning for fitting profiles of the hydrogen Lyman-alpha line emitted in galaxies. One can also refer to the work of [PERSON] et al. [18] on the use of machine learning for the analysis of atomic spectral lines. It may be useful to mention some other references dealing with machine-learning in plasma spectroscopy such as [PERSON] et al. [19], [PERSON] et al. [20] or [PERSON] et al.