Evidence map›Paper›PMID 41422036›Full record

ArticleBioData mining2025

AI-Driven SaO

JiWon Woo, Orian Stapleton, Jay Luo, Chao Cheng Chuang, Yolanda Su, Sreenidhi Sankararaman, Esanika Mukherjee, Nikita Sivakumar, Katherine Calligy, Summer Duffy and 4 more

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Article in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

JiWon Woo *Division of Pediatric Cardiac Surgery, Johns Hopkins Children's Center, Baltimore, MD, USA.
Orian Stapleton *Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Jay LuoDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Chao Cheng ChuangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Yolanda SuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Sreenidhi SankararamanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Esanika MukherjeeDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nikita SivakumarInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
Katherine CalligyDivision of Pediatric Cardiac Surgery, Johns Hopkins Children's Center, Baltimore, MD, USA.
Summer DuffyDivision of Pediatric Cardiac Surgery, Johns Hopkins Children's Center, Baltimore, MD, USA.
Rebecca MosierDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Joseph GreensteinInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
Casey Overby TaylorInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
Danielle Gottlieb SenDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. dsen@uthsc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo address limitations in pulse oximetry accuracy associated with low saturation and melanin content using machine learning (ML) and to compare our results to standard pulse oximetry and gold standard arterial blood gas readings.

backgroundOxygen saturation is traditionally measured through the gold standard arterial blood gas (SaO2) or pulse oximetry (SpO2), which approximates SaO2 using light absorption patterns. However, SpO2 has been shown to overestimate oxygen saturation, particularly in individuals with darker skin tone, leading to hidden hypoxemia and delayed medical interventions.

methodWe developed a machine learning (ML) model trained on the BOLD dataset, integrating patient data from eICU, MIMIC-III, and MIMIC-IV (n = 49,093). With 64 clinical features and 2 outcomes (SaO2 and hidden hypoxemia events), we trained regression ML models (linear regression, random forest, and XGBoost) to predict SaO2, minimizing the mean squared error between predicted SaO2 and ground truth SaO2. We used our test data to compare model performance to standard SaO2 measurement with accuracy root mean square error (Arms), R2, and change in hidden hypoxemia events. We used SHapley Additive exPlanations (SHAP) to rank important features for SaO2 prediction.

resultsThe XGBoost-Vanilla model improved Arms to 3.3% from a baseline of 4.1%. In low SaO2 patients (SaO2 < 90%), the accuracy of pulse oximetry was heavily compromised with Arms of 10.3%; the linear regression model with weighted loss was able to reduce Arms to 8.6%. We found that SpO2, creatinine level, mean corpuscular hemoglobin level, respiratory rate, and age at admission were the leading features driving the SaO2 prediction.

conclusionThese findings suggest that ML-based models can enhance the accuracy of the standard SpO2. Further investigation is warranted to address SpO₂ inaccuracies and bias.

Indexed as

Health equityHidden hypoxemiaMachine learningPulse oximetry

Identifiers

PMID41422036
PMCPMC12752230

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.