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Neuropediatrics 2019; 50(03): 178-187
DOI: 10.1055/s-0039-1685525
Original Article
Georg Thieme Verlag KG Stuttgart · New York

Using Artificial Intelligence to Identify Factors Associated with Autism Spectrum Disorder in Adolescents with Cerebral Palsy

Carlo M. Bertoncelli
1   Department of Pediatric Orthopaedic Surgery, Lenval University Pediatric Hospital of Nice, Nice, France
2   EEAP H. Germain, Departement of Physical Therapy, Fondation Lenval–Children Hospital, Nice, France
,
Paola Altamura
3   Department of Medicinal Chemistry and Pharmaceutical Technology, University of Chieti, Chieti, Italy
,
Edgar Ramos Vieira
4   Department of Physical Therapy, Florida International University, Miami, Florida, United States
,
Domenico Bertoncelli
5   Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy
,
Federico Solla
1   Department of Pediatric Orthopaedic Surgery, Lenval University Pediatric Hospital of Nice, Nice, France
› Author Affiliations
Further Information

Publication History

05 October 2018

20 February 2019

Publication Date:
24 April 2019 (online)

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Abstract

Autism spectrum disorder (ASD) is common in adolescents with cerebral palsy (CP) and there is a lack of studies applying artificial intelligence to investigate this field and this population in particular. The aim of this study is to develop and test a predictive learning model to identify factors associated with ASD in adolescents with CP. This was a multicenter controlled cohort study of 102 adolescents with CP (61 males, 41 females; mean age ± SD [standard deviation] = 16.6 ± 1.2 years; range: 12–18 years). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected between 2005 and 2015. Statistical analysis included Fisher's exact test and multiple logistic regressions to identify factors associated with ASD. A predictive learning model was implemented to identify factors associated with ASD. The guidelines of the “transparent reporting of a multivariable prediction model for individual prognosis or diagnosis” (TRIPOD) statement were followed. Type of spasticity (hemiplegia > diplegia > tri/quadriplegia; OR [odds ratio] = 1.76, SE [standard error] = 0.2785, p = 0.04), communication disorders (OR = 7.442, SE = 0.59, p < 0.001), intellectual disability (OR = 2.27, SE = 0.43, p = 0.05), feeding abilities (OR = 0.35, SE = 0.35, p = 0.002), and motor function (OR = 0.59, SE = 0.22, p = 0.01) were significantly associated with ASD. The best average prediction model score for accuracy, specificity, and sensitivity was 75%. Motor skills, feeding abilities, type of spasticity, intellectual disability, and communication disorders were associated with ASD. The prediction model was able to adequately identify adolescents at risk of ASD.

Keywords

prediction model - cerebral palsy - statistics - machine learning

Prediction Model Identifying Codes

True positive (TP), true negative (TN), false negative (FN), false positive (FP), etiology (ET), type of spasticity (SP), dystonia (D), epilepsy (E), sex (SE), psychotropic drugs (PS), autistic features (A), feeding abilities (EDACS), communication (CFCS), and gross motor (GMFCS) function.


Disclosure Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.


Financial Support

None.


 

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