  We contrast high effectiveness of state of the art deep learning
architectures designed for general audio classification tasks, refined for
respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$)
estimation and classification through automated audio analysis. Recently,
multiple deep learning architectures have been proposed to detect RI in COVID
patients through audio analysis, achieving accuracy above 95% and F1-score
above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly
defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial
determinant of RI, a medical doctor's diagnosis typically relies on multiple
factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among
others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14)
and the Masked Autoencoder (Audio-MAE) for RI detection, where these models
achieve near perfect accuracy, surpassing previous results. Yet, for the
regression task of estimating SpO$_2$ levels, the models achieve root mean
square error values exceeding the accepted clinical range of 3.5% for finger
oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3.
As deep learning models perform better in classification than regression, we
transform SpO$_2$-regression into a SpO$_2$-threshold binary classification
problem, with a threshold of 92%. However, this task still yields an F1-score
below 0.65. Thus, audio analysis offers valuable insights into a patient's RI
status, but does not provide accurate information about actual SpO$_2$ levels,
indicating a separation of domains in which voice and speech biomarkers may and
may not be useful in medical diagnostics under current technologies.
