Why the study?
Does a deep learning model using only SpO2 data improve prediction of impending hypoxemia compared to trained anesthesiologists in surgical patients?
Does a deep learning model using only SpO2 data improve prediction of impending hypoxemia compared to trained anesthesiologists in surgical patients?
Answers are grounded in this paper and cite their sources.
A deep learning model using only past SpO2 data can predict impending intraoperative hypoxemia more accurately than anesthesiologists with access to full operating room data.
We use a deep learning model trained only on a patient's blood oxygenation data (measurable with an inexpensive fingertip sensor) to predict impending hypoxemia (low blood oxygen) more accurately than trained anesthesiologists with access to all the data recorded in a modern operating room. We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values. This work has the potential to provide cutting-edge clinical decision support in low-resource settings, where rates of surgical complication and death are substantially greater than in high-resource areas.
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Erion et al. (2017) studied this question.
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