Key result
A novel Deep Neural Network model accurately predicted SpO2 levels using smartphone-recorded fingertip videos, achieving a Mean Absolute Error of 1.97% compared to 7.03% for the classical RoR method.
Why the study?
The study aimed to increase the reliability of SpO2 readings performed by smartphones for telemedicine and home monitoring.
Does a Deep Neural Network model accurately estimate SpO2 levels using smartphone-recorded fingertip videos compared to classical methods in volunteers?
Does a Deep Neural Network model accurately estimate SpO2 levels using smartphone-recorded fingertip videos compared to classical methods in volunteers?
A novel deep learning model can accurately estimate SpO2 levels using smartphone fingertip videos, outperforming classical methods like Ratio-of-Rations.
May enable smartphone SpO2 monitoring; leaves open validation in clinical populations before practice change.
The COVID-19 pandemic has highlighted the significance of both telemedicine and measuring physiological signals as peripheral Oxygen Saturation (SpO 2 ) at home. Aiming to increase the confiability of SpO 2 readings performed by smartphones, this study presents a novel Deep Neural Network (DNN) model that predicts SpO 2 levels using fingertip videos captured by smartphones. The model was trained and evaluated using the leave-one-out method, and compared against the SpO 2 values measured by a set of oximeters. The dataset used in this work is a differential of the study, unlike most of similar studies which use a breath-holding protocol that presents only a few samples of low SpO 2 values, our tests were evaluated using a publicly available dataset in which volunteers’ SpO 2 levels were measured in a range from almost 100% to approximately 70% by manipulating the percentage of oxygen inhaled by the volunteers, and the SpO 2 levels were kept on lower values for a considerable period of time. The proposed DNN model was compared against multiple variations of the Ratio-of-Rations (RoR) method and the model proposed by the dataset creators. The results show that the DNN outputs have a strong relationship with the true SpO 2 measurements, as indicated by a Pearson coefficient (PC) of 0.95 and a Concordance Correlation Coefficient (CCC) of 0.95, and also a small error, measured by a Root Mean Squared Error (RMSE) of 2.92% and a Mean Absolute Error (MAE) of 1.97%. Compared to the RoR method, which achieved a PC of 0.29, CCC of 0.21, RMSE of 8.83% and MAE of 7.03%, the proposed DNN model exhibits superior performance. Moreover, the proposed DNN model outperforms the model proposed by the dataset authors, which achieved a MAE of 5.00%. These findings demonstrate that the proposed DNN model can accurately predict SpO 2 levels using smartphone-recorded fingertip videos with lower error and higher precision than a widely used classical method.
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Lampier et al. (2023) studied SpO2 estimation. Deep Neural Network (DNN) model for SpO2 estimation vs. Ratio-of-Rations (RoR) method and dataset creators' model was evaluated on SpO2 estimation accuracy (RMSE and MAE). A novel Deep Neural Network model accurately predicted SpO2 levels using smartphone-recorded fingertip videos, achieving a Mean Absolute Error of 1.97% compared to 7.03% for the classical RoR method.
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