Related Experiment Video
Updated: Jun 2, 2025
Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System
Published on: May 5, 2018
Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial
Ray Bahado-Singh1, Nadia Ashrafi2, Amin Ibrahim2
1Department of Obstetrics and Gynecology, Corewell Health William Beaumont University Hospital, Oakland University William Beaumont School of Medicine, Royal Oak, MI, 48073, USA.
Insights
Maternal saliva metabolomics combined with AI accurately detects cyanotic congenital heart disease (CCHD) prenatally. This precision fetal cardiology approach offers improved early diagnosis for CCHD and overall congenital heart disease (CHD).
Area of Science:
- Fetal Cardiology
- Metabolomics
- Artificial Intelligence (AI)
- Congenital Heart Disease (CHD)
Background:
- Prenatal diagnosis of congenital heart disease (CHD) via ultrasound improves outcomes but has limitations.
- Failure to detect cyanotic CHD (CCHD) prenatally or in newborns can lead to severe consequences.
- Existing screening methods lack sufficient accuracy for early and reliable CCHD detection.
Purpose of the Study:
- To develop a Precision Fetal Cardiology approach for prenatal detection of isolated, non-syndromic cyanotic CHD.
- To combine maternal salivary metabolomic profiling with machine learning (AI) for improved diagnostic accuracy.
- To assess the efficacy of AI in identifying CCHD and overall CHD using salivary metabolite data.
Main Methods:
- Maternal saliva samples were analyzed using Ultra-High Performance Liquid Chromatography/Mass Spectrometry to identify metabolites.
- Six different AI platforms were employed to detect CCHD and overall CHD.
- Metabolomic data was correlated with AI-based diagnostic performance metrics (AUC, sensitivity, specificity).
Main Results:
- AI demonstrated excellent accuracy for CCHD detection: AUC 0.819, sensitivity 92.5%, specificity 87.0%.
- AI also achieved high accuracy for overall CHD detection: AUC 0.828-0.849, sensitivity 90.5%-92.5%, specificity 88.0%-91.0%.
- Pathway analysis revealed significant alterations in lipid metabolism, including Arachidonic Acid, Alpha-linoleic acid, and Tryptophan metabolism in CCHD cases.
Conclusions:
- Maternal salivary metabolomics coupled with AI enables accurate prenatal detection of non-syndromic cyanotic CHD.
- This novel approach significantly improves upon current diagnostic limitations for fetal heart conditions.
- The findings highlight significant lipid metabolism dysfunction associated with cyanotic CHD.
Abstract:
Prenatal sonographic diagnosis of congenital heart disease (CHD) can lead to improved morbidity and mortality. However, the diagnostic accuracy of ultrasound, the sole prenatal screening tool, remains limited. Failed prenatal or early newborn detection of cyanotic CHD (CCHD) can have disastrous consequences. We therefore sought to use a Precision Fetal Cardiology based approach combining metabolomic profiling of maternal saliva and machine learning, a major branch of artificial intelligence (AI), for the prenatal detection of isolated, non-syndromic cyanotic CHD. Metabolomic analyses using Ultra-High Performance Liquid Chromatography/Mass Spectrometry identified 468 metabolites in the saliva. Six different AI platforms were utilized for the detection of CCHD and CHD overall. AI achieved excellent accuracy for the CCHD detection: Area Under the ROC curve: AUC (95% CI) = 0.819 (0.635-1.00) with a sensitivity and specificity of 92.5% and 87.0%, and for CHD overall: AUC (95% CI) = 0.828 (0.635-1.00) with a sensitivity of 90.5% and specificity of 88.0%. Similarly high accuracies were achieved for the detection of CHD overall: AUC (95% CI) = 0.8488 (0.635-1.00) with a sensitivity of 92.5% and specificity of 91.0%. Pathway analysis showed significant alterations in Arachidonic Acid, Alpha-linoleic acid, and Tryptophan metabolism indicating significant lipid dysfunction in cyanotic CHD. In summary, we report for the first time, the accurate detection of non-syndromic cyanotic CHD using maternal salivary metabolomics. Further, analysis revealed significant alteration of lipid metabolism.
