| Summary: | A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO<sub>2max</sub>) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland–Altman plot of measured and estimated VO<sub>2max</sub>, the VO<sub>2max</sub> values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: −0.33 mL·kg<sup>−1</sup>·min<sup>−1</sup>, bias: 0.30 mL·kg<sup>−1</sup>·min<sup>−1</sup>, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO<sub>2max</sub> values measured using a CPET than existing equations. This model may be a promising tool for estimating VO<sub>2max</sub> and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.
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