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Jang YH, Ham J, Kasani PH, Kim H, Lee JY, Lee GY, Han TH, Kim BN, Lee HJ. Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity. Sci Rep 2024; 14:9331. [PMID: 38653988 DOI: 10.1038/s41598-024-58682-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
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.
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Affiliation(s)
- Yong Hun Jang
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Jusung Ham
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA, 52242, USA
| | - Payam Hosseinzadeh Kasani
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Hyuna Kim
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Joo Young Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Gang Yi Lee
- Department of Translational Medicine, Hanyang University Graduate School of Biomedical Science and Engineering, Seoul, Republic of Korea
| | - Tae Hwan Han
- Division of Neurology, Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyun Ju Lee
- Department of Pediatrics, Hanyang University Hospital, Hanyang University College of Medicine, 222-1, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea.
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
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Kojima K, Kline JE, Altaye M, Kline-Fath BM, Parikh NA. Corpus Callosum Abnormalities at Term-Equivalent Age Are Associated with Language Development at 2 Years' Corrected Age in Infants Born Very Preterm. JOURNAL OF PEDIATRICS. CLINICAL PRACTICE 2024; 11:200101. [PMID: 38827483 PMCID: PMC11138257 DOI: 10.1016/j.jpedcp.2024.200101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 06/04/2024]
Abstract
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.
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Affiliation(s)
- Katsuaki Kojima
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Julia E. Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Beth M. Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
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Demirci GM, Kittler PM, Phan HTT, Gordon AD, Flory MJ, Parab SM, Tsai CL. Predicting mental and psychomotor delay in very pre-term infants using machine learning. Pediatr Res 2024; 95:668-678. [PMID: 37500755 PMCID: PMC10899098 DOI: 10.1038/s41390-023-02713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/25/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023]
Abstract
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.
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Affiliation(s)
- Gözde M Demirci
- Computer Science Department, The Graduate Center of the City University of NY, New York, NY, USA
| | - Phyllis M Kittler
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Ha T T Phan
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Anne D Gordon
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Michael J Flory
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
| | - Santosh M Parab
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Chia-Ling Tsai
- Computer Science Department, Queens College of the City University of NY, Flushing, NY, USA.
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4
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Kojima K, Kline JE, Altaye M, Kline-Fath BM, Parikh NA. Corpus callosum abnormalities at term-equivalent age are associated with language development at two years corrected age in infants born very preterm. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.20.23295848. [PMID: 37790343 PMCID: PMC10543245 DOI: 10.1101/2023.09.20.23295848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
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.
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Affiliation(s)
- Katsuaki Kojima
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
| | - Julia E Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
| | - Mekibib Altaye
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
| | - Beth M Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Radiology, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH 45267
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Gay JD, Dangcil E, Nacipucha J, Botrous JE, Suresh N, Tucker A, Carayannopoulos NL, Khan MR, Meng R, Yao JD, Wackym PA, Mowery TM. An Animal Model of Neonatal Intensive Care Unit Exposure to Light and Sound in the Preterm Infant. Integr Comp Biol 2023; 63:585-596. [PMID: 37164937 PMCID: PMC10503467 DOI: 10.1093/icb/icad020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/19/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023] Open
Abstract
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.
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Affiliation(s)
- Jennifer D Gay
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
- Rutgers Brain Health Institute, New Brunswick, NJ, USA
| | - Evelynne Dangcil
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Jacqueline Nacipucha
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Jonathon E Botrous
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Nikhil Suresh
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Aaron Tucker
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Nicolas L Carayannopoulos
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Muhammad R Khan
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Raphael Meng
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
| | - Justin D Yao
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
- Rutgers Brain Health Institute, New Brunswick, NJ, USA
| | - P Ashley Wackym
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
- Rutgers Brain Health Institute, New Brunswick, NJ, USA
| | - Todd M Mowery
- Department of Otolaryngology—Head and Neck Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08854, USA
- Rutgers Brain Health Institute, New Brunswick, NJ, USA
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6
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Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of Artificial Intelligence in Neonatology. APPLIED SCIENCES 2023; 13:3211. [DOI: 10.3390/app13053211] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
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.
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Affiliation(s)
- Roberto Chioma
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Annamaria Sbordone
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Maria Letizia Patti
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Alessandro Perri
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Giovanni Vento
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
| | - Stefano Nobile
- Department of Life Sciences and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy
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Baker S, Kandasamy Y. Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review. Pediatr Res 2023; 93:293-299. [PMID: 35641551 PMCID: PMC9153218 DOI: 10.1038/s41390-022-02120-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/25/2022] [Accepted: 05/08/2022] [Indexed: 12/24/2022]
Abstract
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.
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Affiliation(s)
- Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, QLD, 4878, Australia.
| | - Yogavijayan Kandasamy
- grid.417216.70000 0000 9237 0383Department of Neonatology, Townsville Hospital and Health Service, Townsville, QLD 4810 Australia ,grid.1011.10000 0004 0474 1797College of Medicine and Dentistry, James Cook University, Townsville, QLD 4810 Australia
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Kline JE, Dudley J, Illapani VSP, Li H, Kline-Fath B, Tkach J, He L, Yuan W, Parikh NA. Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology. Neuroimage 2022; 264:119727. [PMID: 36332850 PMCID: PMC9908008 DOI: 10.1016/j.neuroimage.2022.119727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
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.
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Affiliation(s)
- Julia E Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jon Dudley
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Venkata Sita Priyanka Illapani
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Beth Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Jean Tkach
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Weihong Yuan
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Abstract
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.
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10
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Kiese-Himmel C. [Early detection of primary developmental language disorders-increasing relevance due to changes in diagnostic criteria?]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2022; 65:909-916. [PMID: 35861864 PMCID: PMC9436846 DOI: 10.1007/s00103-022-03571-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2022] [Indexed: 11/29/2022]
Abstract
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.
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Affiliation(s)
- Christiane Kiese-Himmel
- Phoniatrisch/Pädaudiologische Psychologie, Institut für Medizinische Psychologie und Medizinische Soziologie, Universitätsmedizin Göttingen, Georg-August-Universität Göttingen, Waldweg 35, 37075, Göttingen, Deutschland.
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11
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Airaksinen M, Gallen A, Kivi A, Vijayakrishnan P, Häyrinen T, Ilén E, Räsänen O, Haataja LM, Vanhatalo S. Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants. COMMUNICATIONS MEDICINE 2022; 2:69. [PMID: 35721830 PMCID: PMC9200857 DOI: 10.1038/s43856-022-00131-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
- Manu Airaksinen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Anastasia Gallen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Anna Kivi
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Pavithra Vijayakrishnan
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Taru Häyrinen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elina Ilén
- Department of Design, Aalto University, Otaniementie 14, FI-02150 Espoo, Finland
| | - Okko Räsänen
- Unit of Computing Sciences, Tampere University, P.O. Box 553, FI-33101 Tampere, Finland
| | - Leena M. Haataja
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
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