Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix. (9th July 2019)

• Record Type: Journal Article
• Authors: Bahado‐Singh, R. O.; Sonek, J.; McKenna, D.; Cool, D.; Aydas, B.; Turkoglu, O.; Bjorndahl, T.; Mandal, R.; Wishart, D.; Friedman, P.; Graham, S. F.; Yilmaz, A.
• Is Part Of: Ultrasound in obstetrics & gynecology. Volume 54:Number 1(2019)
• Page Start: 110
• Page End: 118
• Publication Date: 2019-07-09
• Subjects: artificial intelligence -- deep learning -- metabolomics -- multiomics -- perinatal -- proteomics -- ultrasound; Ultrasonics in obstetrics -- Periodicals; Generative organs, Female -- Diseases -- Diagnosis -- Periodicals; Diagnosis, Ultrasonic -- Periodicals; Genital Diseases, Female -- ultrasonography -- Periodicals; Ultrasonography, Prenatal -- Periodicals; 618.047543
• DOI: 10.1002/uog.20168
• Languages: English
• ISSNs: 0960-7692

Abstract

ABSTRACT: Objective: To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine‐learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL). Methods: AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF 'sludge' were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy‐based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine‐learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver–operating characteristics curve (AUC) with 95% CI, sensitivity and specificity. Results: Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810–0.970) for delivery < 34 weeks' gestation, 0.890 (0.790–0.990) for delivery < 28 days post‐amniocentesis and 0.792 (0.689–0.894) for NICU admission. These values were higher overall than for the other five machine‐learning methods, although each individual machine‐learning technique yielded statistically significant prediction of the different perinatal outcomes. Conclusions: This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.