Fetal growth and gestational age prediction by machine learning

Ananth, Cande V.; Brandt, Justin S.

doi:10.1016/s2589-7500(20)30143-6

Cited by 16 publications

(10 citation statements)

References 10 publications

(9 reference statements)

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“…Machine learning is being widely adopted as a new approach to health data analysis ( 40 – 42 ). Such approaches have been used in other studies to estimate GA or predict preterm birth.…”

Section: Discussionmentioning

confidence: 99%

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth

et al. 2023

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IntroductionA new medical device was previously developed to estimate gestational age (GA) at birth by processing a machine learning algorithm on the light scatter signal acquired on the newborn's skin. The study aims to validate GA calculated by the new device (test), comparing the result with the best available GA in newborns with low birth weight (LBW).MethodsWe conducted a multicenter, non-randomized, and single-blinded clinical trial in three urban referral centers for perinatal care in Brazil and Mozambique. LBW newborns with a GA over 24 weeks and weighing between 500 and 2,500 g were recruited in the first 24 h of life. All pregnancies had a GA calculated by obstetric ultrasound before 24 weeks or by reliable last menstrual period (LMP). The primary endpoint was the agreement between the GA calculated by the new device (test) and the best available clinical GA, with 95% confidence limits. In addition, we assessed the accuracy of using the test in the classification of preterm and SGA. Prematurity was childbirth before 37 gestational weeks. The growth standard curve was Intergrowth-21st, with the 10th percentile being the limit for classifying SGA.ResultsAmong 305 evaluated newborns, 234 (76.7%) were premature, and 139 (45.6%) were SGA. The intraclass correlation coefficient between GA by the test and reference GA was 0.829 (95% CI: 0.785–0.863). However, the new device (test) underestimated the reference GA by an average of 2.8 days (95% limits of agreement: −40.6 to 31.2 days). Its use in classifying preterm or term newborns revealed an accuracy of 78.4% (95% CI: 73.3–81.6), with high sensitivity (96.2%; 95% CI: 92.8–98.2). The accuracy of classifying SGA newborns using GA calculated by the test was 62.3% (95% CI: 56.6–67.8).DiscussionThe new device (test) was able to assess GA at birth in LBW newborns, with a high agreement with the best available GA as a reference. The GA estimated by the device (test), when used to classify newborns on the first day of life, was useful in identifying premature infants but not when applied to identify SGA infants, considering current algohrithm. Nonetheless, the new device (test) has the potential to provide important information in places where the GA is unknown or inaccurate.

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“…Machine learning is being widely adopted as a new approach to health data analysis ( 40 – 42 ). Such approaches have been used in other studies to estimate GA or predict preterm birth.…”

Section: Discussionmentioning

confidence: 99%

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth

et al. 2023

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“…Besides, the CIs accompanying AUROC accuracy contributed to revealing the forecast’s limits for discriminating terms from preterm newborns at different cutoff points with clinical relevance. Such strengths are critical for ensuring the potential value of the device in facing the challenges of postnatal identification of preterm newborns [ 35 ]. Postnatal approaches for GA assessment had characteristically shown higher errors than antenatal approaches [ 36 ]; however, studies using first-trimester ultrasound as the standard for postnatal GA comparisons were uncommon until recently.…”

Section: Discussionmentioning

confidence: 99%

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

et al. 2022

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Background Early access to antenatal care and high-cost technologies for pregnancy dating challenge early neonatal risk assessment at birth in resource-constrained settings. To overcome the absence or inaccuracy of postnatal gestational age (GA), we developed a new medical device to assess GA based on the photobiological properties of newborns’ skin and predictive models. Objective This study aims to validate a device that uses the photobiological model of skin maturity adjusted to the clinical data to detect GA and establish its accuracy in discriminating preterm newborns. Methods A multicenter, single-blinded, and single-arm intention-to-diagnosis clinical trial evaluated the accuracy of a novel device for the detection of GA and preterm newborns. The first-trimester ultrasound, a second comparator ultrasound, and data regarding the last menstrual period (LMP) from antenatal reports were used as references for GA at birth. The new test for validation was performed using a portable multiband reflectance photometer device that assessed the skin maturity of newborns and used machine learning models to predict GA, adjusted for birth weight and antenatal corticosteroid therapy exposure. Results The study group comprised 702 pregnant women who gave birth to 781 newborns, of which 366 (46.9%) were preterm newborns. As the primary outcome, the GA as predicted by the new test was in line with the reference GA that was calculated by using the intraclass correlation coefficient (0.969, 95% CI 0.964-0.973). The paired difference between predicted and reference GAs was −1.34 days, with Bland-Altman limits of −21.2 to 18.4 days. As a secondary outcome, the new test achieved 66.6% (95% CI 62.9%-70.1%) agreement with the reference GA within an error of 1 week. This agreement was similar to that of comparator-LMP-GAs (64.1%, 95% CI 60.7%-67.5%). The discrimination between preterm and term newborns via the device had a similar area under the receiver operating characteristic curve (0.970, 95% CI 0.959-0.981) compared with that for comparator-LMP-GAs (0.957, 95% CI 0.941-0.974). In newborns with absent or unreliable LMPs (n=451), the intent-to-discriminate analysis showed correct preterm versus term classifications with the new test, which achieved an accuracy of 89.6% (95% CI 86.4%-92.2%), while the accuracy for comparator-LMP-GA was 69.6% (95% CI 65.3%-73.7%). Conclusions The assessment of newborn’s skin maturity (adjusted by learning models) promises accurate pregnancy dating at birth, even without the antenatal ultrasound reference. Thus, the novel device could add value to the set of clinical parameters that direct the delivery of neonatal care in birth scenarios where GA is unknown or unreliable. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2018-027442

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“…Such strengths are critical to assure the potential value of the new test in facing the challenges of postnatal prematurity identi cation. 26 A new type of data science algorithm has thus emerged with the aim of qualifying pregnancy dating. High-performance reports using learning models based on antenatal ultrasound predictors 27 contradistinguished meager outcomes from those using other morphometric postnatal predictors.…”

Section: Discussionmentioning

confidence: 99%

Newborn skin maturity models for gestational age prediction

Reis

Guimarães

Romanelli

et al. 2022

Preprint

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A multicenter clinical trial evaluated the accuracy of a novel device to detect preterm newborns. A portable multiband reflectance photometric device assessed 781 newborns’ skin maturity and used machine learning models to predict reference gestational age, adjusting it to birth weight and antenatal corticosteroid therapy exposure. The day difference between the reference and the test had a median of -1.4 (IQR: -2.1). Using established methods such as comparator ultrasound and last menstrual period (LMP), the medians were 0 (IQR: 4) and 0.01 (IQR: 4), respectively. For prematurity discrimination, the area under the receiver operating characteristic curve (AUROC) was 0.986 (95% CI: 0.977 to 0.994). In newborns with absent or unreliable LMP, the intent-to-discriminate analysis showed that the test generated correct classifications 95.8% of the time. The assessment of the newborn's skin maturity adjusted by learning models promises accurate pregnancy dating at birth without the use of antenatal ultrasound reference.

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Fetal growth and gestational age prediction by machine learning

Cited by 16 publications

References 10 publications

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial

Newborn skin maturity models for gestational age prediction

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