Key result
AI-based camera estimation of SpO2 from video shows promising results versus traditional methods.
Why the study?
Technical advances and artificial intelligence have enabled camera-based SpO2 estimation, prompting a review of imaging photoplethysmography-based techniques for predicting SpO2 from video.
Does camera-based imaging photoplethysmography using AI accurately estimate blood oxygen saturation from video compared to traditional methods?
Does camera-based imaging photoplethysmography using AI accurately estimate blood oxygen saturation from video compared to traditional methods?
AI-based imaging photoplethysmography from video is a promising non-contact method for estimating blood oxygen saturation, though further validation is needed for regulatory approval.
The combination of technical advances and the emergence of artificial intelligence in the medical field has given rise to new oxygen saturation (SpO 2 ) measurement processes. In recent years, camera-based estimation of SpO 2 has attracted increasing interest. This technique offers an optical way to acquire SpO 2 in a very practical way. This review provides an overview of the imaging photoplethysmography-based techniques developed to achieve SpO 2 prediction from video. A total of 81 articles have been assessed and are included in this review. The results obtained using artificial intelligence-based methods in particular are very promising in view of traditional methods. This suggests that AI-based methods will have an important future role to play in medical devices. The prospects are now focused on efforts to improve the robustness and performance of the models, and in fine, to stamp these medical devices in accordance with current standards (CE or FDA) to meet the demands of the medical profession and patient safety.
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Wuyart et al. (2025) conducted a review in Blood oxygen saturation assessment. Camera-based estimation of SpO2 from video vs. Traditional methods was evaluated on SpO2 prediction. Camera-based estimation of blood oxygen saturation from video using artificial intelligence-based methods shows promising results compared to traditional methods.
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