Identifying cerebral palsy phenotypes objectively

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

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.

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

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.

New Ethical Issues in Cerebral Palsy

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.