Newborn Cry Acoustics in the Assessment of Neonatal Opioid Withdrawal Syndrome Using Machine Learning

Abstract

ImportanceThe assessment of opioid withdrawal in the neonate, or neonatal opioid withdrawal syndrome (NOWS), is problematic because current assessment methods are based on subjective observer ratings. Crying is a distinctive component of NOWS assessment tools and can be measured objectively using acoustic analysis.ObjectiveTo evaluate the feasibility of using newborn cry acoustics (acoustics referring to the physical properties of sound) as an objective biobehavioral marker of NOWS.Design, Setting, and ParticipantsThis prospective controlled cohort study assessed whether acoustic analysis of neonate cries could predict which infants would receive pharmacological treatment for NOWS. A total of 177 full-term neonates exposed and not exposed to opioids were recruited from Women & Infants Hospital of Rhode Island between August 8, 2016, and March 18, 2020. Cry recordings were processed for 118 neonates, and 65 neonates were included in the final analyses. Neonates exposed to opioids were monitored for signs of NOWS using the Finnegan Neonatal Abstinence Scoring Tool administered every 3 hours as part of a 5-day observation period during which audio was recorded continuously to capture crying. Crying of healthy neonates was recorded before hospital discharge during routine handling (eg, diaper changes).ExposuresThe primary exposure was prenatal opioid exposure as determined by maternal receipt of medication-assisted treatment with methadone or buprenorphine.Main Outcomes and MeasuresNeonates were stratified by prenatal opioid exposure and receipt of pharmacological treatment for NOWS before discharge from the hospital. In total, 775 hours of audio were collected and trimmed into 2.5 hours of usable cries, then acoustically analyzed (using 2 separate acoustic analyzers). Cross-validated supervised machine learning methods (combining the Boruta algorithm and a random forest classifier) were used to identify relevant acoustic parameters and predict pharmacological treatment for NOWS.ResultsFinal analyses included 65 neonates (mean [SD] gestational age at birth, 36.6 [1.1] weeks; 36 [55.4%] female; 50 [76.9%] White) with usable cry recordings. Of those, 19 neonates received pharmacological treatment for NOWS, 7 neonates were exposed to opioids but did not receive pharmacological treatment for NOWS, and 39 healthy neonates were not exposed to opioids. The mean of the predictions of random forest classifiers predicted receipt of pharmacological treatment for NOWS with high diagnostic accuracy (area under the curve, 0.90 [95% CI, 0.83-0.98]; accuracy, 0.85 [95% CI, 0.74-0.92]; sensitivity, 0.89 [95% CI, 0.67-0.99]; specificity, 0.83 [95% CI, 0.69-0.92]).Conclusions and RelevanceIn this study, newborn acoustic cry analysis had potential as an objective measure of opioid withdrawal. These findings suggest that acoustic cry analysis using machine learning could improve the assessment, diagnosis, and management of NOWS and facilitate standardized care for these infants.

Acoustic Cry Characteristics in Preterm Infants and Developmental and Behavioral Outcomes at 2 Years of Age

“…Cry episodes suitable for acoustic analysis were identified based on the absence of background noises that would interfere with the analysis (ie, adult talk, medical equipment noises, and other environmental noises). The identification of usable cries was based on reliability training from a previous study 14 in which 89% agreement was established in identifying cries appropriate for acoustic analysis. The first suitable cry episode from each examination was excerpted into an uncompressed .wav file for subsequent acoustic analysis.…”

Voice as a Biomarker of Pediatric Health: A Scoping Review

“…Miodonska 2016 [60] Szklanny 2019 [70] Woloshuk 2018 [61] Singapore [2] Balamurali 2021 [52] Hee 2019 [46] South Korea [2] Lee 2020 [19] Lee 2022 [20] Sri Lanka [2] Kariyawasam 2019 [32] Wijesinghe 2019 [27] Sweden [1] Pokorny 2017 [28] Turkey [1] Satar 2022 [38] United Kingdom [1] Alharbi 2018 [51] USA [12] Asgari 2021 [22] Chi 2022 [26] Cho 2019 [17] Ji 2021 [35] Ji 2019 [36] MacFarlane 2022 [23] Manigault 2022 [67] McGinnis 2019 [63] Onu 2019 [37] Sadeghian 2015 [49] Suthar 2022 [50] VanDam 2015 [58] Appendix C…”

“…A table listing the funding source, funding country, study, and reference number for each study that stated a funding source within their respective publication. Chi 2022 [26] Khalilzad 2022 [65] Salehian Matikolaie 2020 [68] BioTechMed-Graz Pokorny 2017 [28] Bio-X Center Chi 2022 [26] Brown University Manigault 2022 [67] Coulter Foundation Chi 2022 [26] Hartwell Foundation Chi 2022 [26] Lucile Packard Foundation Chi 2022 [26] National Institute on Deafness and Other Communication Disorders VanDam 2015 [58] National Institutes of Health Asgari 2021 [22] Chi 2022 [26] National Science Foundation Chi 2022 [26] Old Dominion University-Virginia Modeling Aggarwal 2020 [15] Plough Foundation VanDam 2015 [58] Stanford University Chi 2022 [26] Weston Havens Foundation Chi 2022 [26] Appendix D…”

“…Aggarwal 2018 [14] Aggarwal 2020 [15] Chen 2023 [16] Chi 2022 [26] Manigault 2022 [67] McGinnis 2019 [63] Suthar 2022 [50] Tartarisco 2021 [56] ResNet Kotarba 2020 [59] Statistically Trained Language Model Alharbi 2018 [51] Support Vector Machine…”

Neonatal Abstinence Signs during Treatment: Trajectory, Resurgence and Heterogeneity

“…The proposed alternatives to the FNAST [17] have included the shortened FNAST [12][13][14][15], Eat, Sleep, Console (ESC) [18], skin conductance [19], infant pupillary diameter [20], and acoustic characteristics of the infant cry [21]. The shortened tools, as summarized by Miller et al [22], were meant to either "optimize" covariance with the FNAST or predict the need for pharmacological treatment.…”