Language function following preterm birth: prediction using machine learning

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Corpus Callosum Abnormalities at Term-Equivalent Age Are Associated with Language Development at 2 Years' Corrected Age in Infants Born Very Preterm

We studied the effect of microstructural abnormalities in the corpus callosum on language development in 348 infants born very prematurely. We discovered that the fractional anisotropy of the corpus callosum anterior midbody was a significant predictor of standardized language scores at 2 years, independent of clinical and social risk factors.

Predicting mental and psychomotor delay in very pre-term infants using machine learning

BACKGROUND

Very preterm infants are at elevated risk for neurodevelopmental delays. Earlier prediction of delays allows timelier intervention and improved outcomes. Machine learning (ML) was used to predict mental and psychomotor delay at 25 months.

METHODS

We applied RandomForest classifier to data from 1109 very preterm infants recruited over 20 years. ML selected key predictors from 52 perinatal and 16 longitudinal variables (1-22 mo assessments). SHapley Additive exPlanations provided model interpretability.

RESULTS

Balanced accuracy with perinatal variables was 62%/61% (mental/psychomotor). Top predictors of mental and psychomotor delay overlapped and included: birth year, days in hospital, antenatal MgSO4, days intubated, birth weight, abnormal cranial ultrasound, gestational age, mom's age and education, and intrauterine growth restriction. Highest balanced accuracy was achieved with 19-month follow-up scores and perinatal variables (72%/73%).

CONCLUSIONS

Combining perinatal and longitudinal data, ML modeling predicted 24 month mental/psychomotor delay in very preterm infants ½ year early, allowing intervention to start that much sooner. Modeling using only perinatal features fell short of clinical application. Birth year's importance reflected a linear decline in predicting delay as birth year became more recent.

IMPACT

Combining perinatal and longitudinal data, ML modeling was able to predict 24 month mental/psychomotor delay in very preterm infants ½ year early (25% of their lives) potentially advancing implementation of intervention services. Although cognitive/verbal and fine/gross motor delays require separate interventions, in very preterm infants there is substantial overlap in the risk factors that can be used to predict these delays. Birth year has an important effect on ML prediction of delay in very preterm infants, with those born more recently (1989-2009) being increasing less likely to be delayed, perhaps reflecting advances in medical practice.

Corpus callosum abnormalities at term-equivalent age are associated with language development at two years corrected age in infants born very preterm

We studied the impact of microstructural abnormalities in the corpus callosum on language development in 348 infants born very prematurely. We discovered that the fractional anisotropy of the corpus callosum anterior midbody was a significant predictor of standardized language scores at two years, independent of clinical and social risk factors.

An Animal Model of Neonatal Intensive Care Unit Exposure to Light and Sound in the Preterm Infant

According to the World Health Organization, ∼15 million children are born prematurely each year. Many of these infants end up spending days to weeks in a neonatal intensive care unit (NICU). Infants who are born prematurely are often exposed to noise and light levels that affect their auditory and visual development. Children often have long-term impairments in cognition, visuospatial processing, hearing, and language. We have developed a rodent model of NICU exposure to light and sound using the Mongolian gerbil (Meriones unguiculatus), which has a low-frequency human-like audiogram and is altricial. To simulate preterm infancy, the eyes and ears were opened prematurely, and animals were exposed to the NICU-like sensory environment throughout the gerbil's cortical critical period of auditory development. After the animals matured into adults, auditory perceptual testing was carried out followed by auditory brainstem response recordings and then histology to assess the white matter morphology of various brain regions. Compared to normal hearing control animals, NICU sensory-exposed animals had significant impairments in learning at later stages of training, increased auditory thresholds reflecting hearing loss, and smaller cerebellar white matter volumes. These have all been reported in longitudinal studies of preterm infants. These preliminary results suggest that this animal model could provide researchers with an ethical way to explore the effects of the sensory environment in the NICU on the preterm infant's brain development.

Applications of Artificial Intelligence in Neonatology

The development of artificial intelligence methods has impacted therapeutics, personalized diagnostics, drug discovery, and medical imaging. Although, in many situations, AI clinical decision-support tools may seem superior to rule-based tools, their use may result in additional challenges. Examples include the paucity of large datasets and the presence of unbalanced data (i.e., due to the low occurrence of adverse outcomes), as often seen in neonatal medicine. The most recent and impactful applications of AI in neonatal medicine are discussed in this review, highlighting future research directions relating to the neonatal population. Current AI applications tested in neonatology include tools for vital signs monitoring, disease prediction (respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity) and risk stratification (retinopathy of prematurity, intestinal perforation, jaundice), neurological diagnostic and prognostic support (electroencephalograms, sleep stage classification, neuroimaging), and novel image recognition technologies, which are particularly useful for prompt recognition of infections. To have these kinds of tools helping neonatologists in daily clinical practice could be something extremely revolutionary in the next future. On the other hand, it is important to recognize the limitations of AI to ensure the proper use of this technology.

Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review

BACKGROUND

Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date.

METHODS

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors.

RESULTS

The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly.

CONCLUSIONS

Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes.

IMPACT

This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.

Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology

Preterm brains commonly exhibit elevated signal intensity in the white matter on T2-weighted MRI at term-equivalent age. This signal, known as diffuse excessive high signal intensity (DEHSI) or diffuse white matter abnormality (DWMA) when quantitatively assessed, is associated with abnormal microstructure on diffusion tensor imaging. However, postmortem data are largely lacking and difficult to obtain, and the pathological significance of DEHSI remains in question. In a cohort of 202 infants born preterm at ≤32 weeks gestational age, we leveraged two newer diffusion MRI models - Constrained Spherical Deconvolution (CSD) and neurite orientation dispersion and density index (NODDI) - to better characterize the macro and microstructural properties of DWMA and inform the ongoing debate around the clinical significance of DWMA. With increasing DWMA volume, fiber density broadly decreased throughout the white matter and fiber cross-section decreased in the major sensorimotor tracts. Neurite orientation dispersion decreased in the centrum semiovale, corona radiata, and temporal lobe. These findings provide insight into DWMA's biological underpinnings and demonstrate that it is a serious pathology.

Application and potential of artificial intelligence in neonatal medicine

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

[Early detection of primary developmental language disorders-increasing relevance due to changes in diagnostic criteria?]

Language development disorders (in German: Sprachentwicklungsstörungen, SES) are the most common developmental disorders in childhood. In contrast to "secondary SES," "primary SES" (prevalence about 7%) are not (co-)caused by other developmental disorders or diseases. In the German modification of the International Statistical Classification of Diseases and Related Health Problems (ICD-10-GM-22), primary SES are referred to as "circumscribed developmental disorders of speech and language" (in German: USES; international previously known as Specific Language Impairment SLI), with an intelligence quotient (IQ) < 85 as an exclusion criterion, among other criteria. In ICD-11, primary SES are listed as "developmental language disorders" (DLD).German-speaking speech and language therapists would now like to replace the term "USES" with "DLD" using the diagnostic criteria proposed by the international CATALISE consortium (Criteria and Terminology Applied to Language Impairments Synthesizing the Evidence), in an effort to redefine the disorder. However, according to this conceptualization, only children with an intellectual disability (IQ < 70) would be excluded from the diagnosis. This change in the diagnostic criteria would most likely result in an increase in prevalence of DLDs. This makes the issue of early detection more important than ever. This discussion paper explains that the public health relevance of primary SES is growing and that systematic early detection examinations will play an even more important role. With early diagnosis and treatment, risks in the areas of mental health, behaviour and skill development can be mitigated.Currently, diagnosis (and therapy) are usually carried out relatively late. The way out could lie in the application of neurobiological parameters. However, this requires further studies that examine child cohorts for early indicators in a prospective longitudinal design. The formation of an early detection index from several indicators should also be considered.

Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants

Background

Early neurodevelopmental care needs better, effective and objective solutions for assessing infants' motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants' spontaneous motor abilities across all motor milestones from lying supine to fluent walking.

Methods

A multi-sensor infant wearable was constructed, and 59 infants (age 5-19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity.

Results

Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants' motor abilities, and it correlates very strongly (Pearson's r = 0.89, p < 1e-20) to the chronological age of the infant.

Conclusions

The results show that out-of-hospital assessment of infants' motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants' age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.