Monitoring in vivo mitochondrial oxygen tension (mitoPO₂) enables the measurement of mitochondrial oxygen consumption (mitoVO₂), providing deeper insights into the skin’s mitochondrial environment. However, current mitoVO₂ analysis often relies on manual identification of start and end points, which introduces substantial inter-user variability. Addressing this limitation is crucial for broader adoption, comparability, and reproducibility across research groups. Therefore, the aim of this study was to develop a neural network–based software that automatically analyzes mitoVO₂. A Bi-directional Long Short-Term Memory neural network was trained on 125 mitoPO₂ measurement sequences and optimized through Bayesian optimization. It identifies start points and measurement periods, then applies a modified Michaelis-Menten fit to calculate mitoVO₂. This framework, embedded in automated software, was validated against the consensus of 3 raters. Bayesian optimization yielded an overall network performance of 94.2% on the test set. The neural network identified 91% of mitoVO₂ start points within a ± 5-sample range of the manual consensus. Mean mitoVO₂ values for the consensus and software were 6.56 and 6.63 mmHg s^{− 1}, respectively, corresponding to a bias of -0.057 mmHg s^{− 1}. Multiple runs of the network on the same dataset produced identical results, confirming consistency and eliminating inter-user variability. The developed neural network–based software automatically and consistently analyzes mitoVO₂ measurements, substantially reducing reliance on subjective judgments. By enabling a standardized approach to mitoVO₂ analysis, this tool improves data comparability and reproducibility across research settings. Future work will focus on further refining precision and extending functionality through multi-center collaborations.