Identifying cerebral palsy phenotypes objectively

Leviton, Alan

doi:10.1111/dmcn.14604

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Recent exome sequencing and copy number variant array studies have demonstrated that between 10 and 31% of CP cases have a genetic cause 6 – 14 , with a heterogeneous underlying genetic landscape. Nevertheless, the cohorts included in genetic studies have been diverse and reporting of clinical characteristics of cohorts has been variable 15 , leading to some scepticism amongst the medical community about the validity of these findings 16 . Prior genomic analyses (exome sequencing, DNA microarray, or gene panel 7 , 11 , 14 ) of more than 300 cases from the Australian CP Biobank cohort suggest that genetic aetiology is not limited to individuals without other risk factors, or with other neurodevelopmental comorbidities like intellectual disability, epilepsy or autism.…”

Section: Introductionmentioning

confidence: 99%

Yield of clinically reportable genetic variants in unselected cerebral palsy by whole genome sequencing

et al. 2021

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Cerebral palsy (CP) is the most common cause of childhood physical disability, with incidence between 1/500 and 1/700 births in the developed world. Despite increasing evidence for a major contribution of genetics to CP aetiology, genetic testing is currently not performed systematically. We assessed the diagnostic rate of genome sequencing (GS) in a clinically unselected cohort of 150 singleton CP patients, with CP confirmed at >4 years of age. Clinical grade GS was performed on the proband and variants were filtered, and classified according to American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG-AMP) guidelines. Variants classified as pathogenic or likely pathogenic (P/LP) were further assessed for their contribution to CP. In total, 24.7% of individuals carried a P/LP variant(s) causing or increasing risk of CP, with 4.7% resolved by copy number variant analysis and 20% carrying single nucleotide or indel variants. A further 34.7% carried one or more rare, high impact variants of uncertain significance (VUS) in variation intolerant genes. Variants were identified in a heterogeneous group of genes, including genes associated with hereditary spastic paraplegia, clotting and thrombophilic disorders, small vessel disease, and other neurodevelopmental disorders. Approximately 1/2 of individuals were classified as likely to benefit from changed clinical management as a result of genetic findings. In addition, no significant association between genetic findings and clinical factors was detectable in this cohort, suggesting that systematic sequencing of CP will be required to avoid missed diagnoses.

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Section: Introductionmentioning

confidence: 99%

Yield of clinically reportable genetic variants in unselected cerebral palsy by whole genome sequencing

et al. 2021

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“…Abnormal reflection does not disappear. Some children with cerebral palsy may also be accompanied by mental retardation, epilepsy, behavior disorder, visual, and hearing impairment, which seriously affects their normal life [ 2 – 5 ]. Moreover, there is no cure for cerebral palsy so far; only the symptoms can be alleviated, which brings extremely serious burden and pain to individuals, families, and even the society [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging

Yang

Zuo

Han

et al. 2021

Journal of Healthcare Engineering

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In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 children with cerebral palsy were rolled into the observation group (n = 35), which received MRI for diagnosis after segmentation of brain tissue, and control group (n = 35), which were diagnosed by computed tomography (CT). The diagnosis results of the two groups were compared. The validity experiment verified that the image segmentation method based on CNN algorithm can obtain effective style graphics. In clinical trials, the diagnostic accuracy of 88.6% in the observation group was evidently superior to that of 80% in the control group ( P < 0.05 ). In the observation group, one patient was diagnosed as normal, four patients had white matter lesions, 17 patients had corpus callosum lesions, and five patients had basal ganglia softening foci. In the control group, two patients were diagnosed as normal, two patients had white matter lesions, 19 patients had corpus callosum lesions, and four patients had basal ganglia softening foci. No notable difference was found between the two groups ( P > 0.05 ). According to the research results, in the diagnosis of cerebral palsy in children, the image segmentation of brain 3D model based on CNN to obtain the MRI image of segmented brain tissue can effectively improve the detection accuracy. Moreover, the specific symptoms can be diagnosed clearly. It can provide the corresponding diagnostic basis for clinical diagnosis and treatment and was worthy of clinical promotion.

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“…Indeed, artificial intelligence and machine learning support a range of other technological developments for which we can anticipate ethical discussion relating to cerebral palsy (13). In a not-so-distant future, artificial intelligence may allow reliable objective characterization of clinical presentations (14), possibly fed by wearable sensor technology (15,16). As previously demonstrated in other populations, machine learning can now recognize a range of relevant physical activity behaviors in individuals with cerebral palsy with great accuracy (17).…”

Section: Technological Issuesmentioning

confidence: 94%

New Ethical Issues in Cerebral Palsy

Dan

2021

Front. Neurol.

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Current societal and technological changes have added to the ethical issues faced by people with cerebral palsy. These include new representations of disability, and the current International Classification of Functioning, Disability, and Health, changes in legislation and international conventions, as well as applications of possibilities offered by robotics, brain–computer interface devices, muscles and brain stimulation techniques, wearable sensors, artificial intelligence, genetics, and more for diagnostic, therapeutic, or other purposes. These developments have changed the way we approach diagnosis, set goals for intervention, and create new opportunities. This review examines those influences on clinical practice from an ethical perspective and highlights how a principled approach to clinical bioethics can help the clinician to address ethical dilemmas that occur in practice. It also points to implications of those changes on research priorities.

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Identifying cerebral palsy phenotypes objectively

Abstract: This commentary is on the systematic review by Pham et al. on pages 1024–1030 of this issue.

Cited by 4 publications

References 6 publications

Yield of clinically reportable genetic variants in unselected cerebral palsy by whole genome sequencing

Yield of clinically reportable genetic variants in unselected cerebral palsy by whole genome sequencing

Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging

New Ethical Issues in Cerebral Palsy

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