Automated mitochondrial oxygen consumption (mitoVO) analysis via a bi-directional long short-term memory neural network.

Monitoring in vivo mitochondrial oxygen tension (mitoPO) enables the measurement of mitochondrial oxygen consumption (mitoVO), providing deeper insights into the skin's mitochondrial environment. However, current mitoVO 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 mitoVO. A Bi-directional Long Short-Term Memory neural network was trained on 125 mitoPO measurement sequences and optimized through Bayesian optimization. It identifies start points and measurement periods, then applies a modified Michaelis-Menten fit to calculate mitoVO. 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 mitoVO start points within a ± 5-sample range of the manual consensus. Mean mitoVO values for the consensus and software were 6.56 and 6.63 mmHg s, respectively, corresponding to a bias of -0.057 mmHg s. 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 mitoVO measurements, substantially reducing reliance on subjective judgments. By enabling a standardized approach to mitoVO 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.