Artificial Intelligence and amniotic fluid multiomics analysis: The prediction of perinatal outcome in asymptomatic short cervix.

Researchers

Journal

Modalities

Models

Abstract

To evaluate the utility of Artificial Intelligence i.e. Deep Learning (DL) and other machine learning techniques for the prediction of important pregnancy outcomes in asymptomatic short cervical length (CL).
The amniotic fluid (AF) had been obtained from second trimester patients with asymptomatic women with short cervical length (<15 mm). CL, funneling and the presence of AF ‘sludge’ were assessed in all cases. Combined targeted metabolomic and proteomic analysis of amniotic fluid (AF) was performed. A combination of liquid Chromatography -Mass spectrometry (LC-MS-MS and) and proton Nuclear Mass Spectrometry (1 H-NMR) -based metabolomics and targeted proteomics analysis (Bioplex Human cytokine Group-1 assay (Bio-Rad) consisting of chemokines, cytokines and growth factors, were performed on the AF samples. To determine the robustness of the markers we used multiple machine learning techniques including deep learning (DL) to predict moderate prematurity, <34 weeks, latency period prior to delivery, and NICU stay. Logistic regression analysis was also used. Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was calculated using area under the receiver operating characteristics curve (AUC) and 95% CI, sensitivity and specificity values.
Of a total of 32 patients in the study, complete omics analysis, demographic and clinical data and outcomes information was available in 26. Of these 11 (42.3%) of patients delivered at ≥ 34 weeks while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in the CL (mean /SD CL 11.2 (4.40)mm versus 8.9 (5.30) mm, p=0.31. DL had an AUC (95%CI) of 0.89 (0.81-0.97) for delivery < 34 weeks gestation, 0.89 (0.79-0.99) for delivery < 28 days post -amniocentesis and 0.792 (0. 70-0.89) for NICU stay. These values were overall higher than for the other five machine learning methods. Each ML technique individually yielded statistically significantly prediction of the different perinatal outcomes.
This is the first report using AI combined with proteomics , metabolomics and ultrasound assessment . Good to excellent prediction of important perinatal outcomes were achieved in asymptomatic mid-trimester CL shortening.
The aim was to predict important perinatal outcomes in asymptomatic patients with shortened cervical length (CL) using Artificial intelligence analysis of amniotic fluid metabolomics and proteomics data. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.