Diagnosis Of Neurological Disorders Research Articles

Interaction of MRI Contrast Agent [Gd(DOTA)]- with Lipid Membranes: A Molecular Dynamics Study.

Contrast agents are important imaging probes in clinical MRI, allowing the identification of anatomic changes that otherwise would not be possible. Intensive research on the development of new contrast agents is being made to image specific pathological markers or sense local biochemical changes. The most widely used MRI contrast agents are based on gadolinium(III) complexes. Due to their very high charge density, they have low permeability through tight biological barriers such as the blood-brain barrier, hampering their application in the diagnosis of neurological disorders. In this study, we explore the interaction between the widely used contrast agent [Gd(DOTA)]- (Dotarem) and POPC lipid bilayers by means of molecular dynamics simulations. This metal complex is a standard reference where several chemical modifications have been introduced to improve key properties such as bioavailability and targeting. The simulations unveil detailed insights into the agent's interaction with the lipid bilayer, offering perspectives beyond experimental methods. Various properties, including the impact on global and local bilayer properties, were analyzed. As expected, the results indicate a low partition coefficient (KP) and high permeation barrier for this reference compound. Nevertheless, favorable interactions are established with the membrane leading to moderately long residence times. While coordination of one inner-sphere water molecule is maintained for the membrane-associated chelate, the physical-chemical attributes of [Gd(DOTA)]- as a MRI contrast agent are affected. Namely, increases in the rotational correlation times and in the residence time of the inner-sphere water are observed, with the former expected to significantly increase the water proton relaxivity. This work establishes a reference framework for the use of simulations to guide the rational design of new contrast agents with improved relaxivity and bioavailability and for the development of liposome-based formulations for use as imaging probes or theranostic agents.

Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review

Background Natural language processing (NLP), a branch of artificial intelligence that analyzes unstructured language, is being increasingly used in health care. However, the extent to which NLP has been formally studied in neurological disorders remains unclear. Objective We sought to characterize studies that applied NLP to the diagnosis, prediction, or treatment of common neurological disorders. Methods This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) standards. The search was conducted using MEDLINE and Embase on May 11, 2022. Studies of NLP use in migraine, Parkinson disease, Alzheimer disease, stroke and transient ischemic attack, epilepsy, or multiple sclerosis were included. We excluded conference abstracts, review papers, as well as studies involving heterogeneous clinical populations or indirect clinical uses of NLP. Study characteristics were extracted and analyzed using descriptive statistics. We did not aggregate measurements of performance in our review due to the high variability in study outcomes, which is the main limitation of the study. Results In total, 916 studies were identified, of which 41 (4.5%) met all eligibility criteria and were included in the final review. Of the 41 included studies, the most frequently represented disorders were stroke and transient ischemic attack (n=20, 49%), followed by epilepsy (n=10, 24%), Alzheimer disease (n=6, 15%), and multiple sclerosis (n=5, 12%). We found no studies of NLP use in migraine or Parkinson disease that met our eligibility criteria. The main objective of NLP was diagnosis (n=20, 49%), followed by disease phenotyping (n=17, 41%), prognostication (n=9, 22%), and treatment (n=4, 10%). In total, 18 (44%) studies used only machine learning approaches, 6 (15%) used only rule-based methods, and 17 (41%) used both. Conclusions We found that NLP was most commonly applied for diagnosis, implying a potential role for NLP in augmenting diagnostic accuracy in settings with limited access to neurological expertise. We also found several gaps in neurological NLP research, with few to no studies addressing certain disorders, which may suggest additional areas of inquiry. Trial Registration Prospective Register of Systematic Reviews (PROSPERO) CRD42021228703; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=228703

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Harnessing Graphdiyne for Selective Cu2+ Detection: A Promising Tool for Parkinson's Disease Diagnostics and Pathogenesis.

Cu2+ accelerates the viral-like propagation of α-synuclein fibrils and plays a key role in the pathogenesis of Parkinson's disease (PD). Therefore, the accurate detection of Cu2+ is essential for the diagnosis of PD and other neurological diseases. The Cu2+ detection process is impeded by substances that have similar electrochemical properties. In this study, graphdiyne (GDY), a new kind of carbon allotrope with strong electron-donating ability, was utilized for the highly selective detection of Cu2+ by taking advantage of its outstanding adsorption capacity for Cu2+. Density functional theory (DFT) calculations show that Cu atoms are adsorbed in the cavity of GDY, and the absorption energy between Cu and C atoms is higher than that of graphene (GR), indicating that the cavity of GDY is favorable for the adsorption of Cu atoms and electrochemical sensing. The GDY-based electrochemical sensor can effectively avoid the interference of amino acids, metal ions and neurotransmitters and has a high sensitivity of 9.77 μA·μM-1·cm-2, with a minimum detectable concentration of 200 nM. During the investigating pathogenesis and therapeutic process of PD with α-synuclein as the diagnostic standard, the concentration of Cu2+ in cells before and after L-DOPA and GSH treatments were examined, and it was found that Cu2+ exhibits high potential as a biomarker for PD. This study not only harnesses the favorable adsorption of the GDY and Cu2+ to improve the specificity of ion detection but also provide clues for deeper understanding of the role of Cu2+ in neurobiology and neurological diseases.

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

Background: Rare diseases, predominantly caused by genetic factors and often presenting neurological manifestations, are significantly underrepresented in research. This review addresses the urgent need for advanced research in rare neurological diseases (RNDs), which suffer from a data scarcity and diagnostic challenges. Bridging the gap in RND research is the integration of machine learning (ML) and omics technologies, offering potential insights into the genetic and molecular complexities of these conditions. Methods: We employed a structured search strategy, using a combination of machine learning and omics-related keywords, alongside the names and synonyms of 1840 RNDs as identified by Orphanet. Our inclusion criteria were limited to English language articles that utilized specific ML algorithms in the analysis of omics data related to RNDs. We excluded reviews and animal studies, focusing solely on studies with the clear application of ML in omics data to ensure the relevance and specificity of our research corpus. Results: The structured search revealed the growing use of machine learning algorithms for the discovery of biomarkers and diagnosis of rare neurological diseases (RNDs), with a primary focus on genomics and radiomics because genetic factors and imaging techniques play a crucial role in determining the severity of these diseases. With AI, we can improve diagnosis and mutation detection and develop personalized treatment plans. There are, however, several challenges, including small sample sizes, data heterogeneity, model interpretability, and the need for external validation studies. Conclusions: The sparse knowledge of valid biomarkers, disease pathogenesis, and treatments for rare diseases presents a significant challenge for RND research. The integration of omics and machine learning technologies, coupled with collaboration among stakeholders, is essential to develop personalized treatment plans and improve patient outcomes in this critical medical domain.

Artificial Intelligence in Neurology: Current Applications and Future Prospects

Artificial intelligence (AI) is reshaping the field of neurology, enhancing diagnosis, treatment, and management of neurological disorders. This article explores AI's role in neurology, highlighting its ability to process vast amounts of data to improve diagnostic accuracy and personalize treatments. AI applications, from neuroimaging to clinical decision support, have shown promising results in enhancing patient care. However, challenges such as data security, ethical concerns, and the need for stringent regulatory frameworks remain significant. The potential of AI in neurology continues to grow, promising revolutionary changes in patient outcomes and healthcare practices, provided these challenges are effectively managed.

Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review

AbstractWearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological and psychiatric diseases. These devices capture signals related to physiological and behavioral changes, including heart rate, sleep patterns, and motor functions. Their emergence has resulted in significant advancements in the management of such conditions. Traditional clinical diagnosis and assessment methods heavily rely on patient reports and evaluations conducted by healthcare professionals, often leading to a detachment of patients from their home environment and creating additional burdens for both patients and healthcare providers. The increasing popularity of wearable devices offers a potential solution to these challenges. This review focuses on the clinical utility of wearable devices in diagnosing and monitoring neurological and psychiatric diseases. Through research findings and practical examples, we highlight the potential role of wearable devices in diagnosing and monitoring conditions such as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, and other diseases. Additionally, the review discusses the benefits and limitations of wearable devices in clinical applications, while highlighting the challenges they face. Finally, it provides prospects for enhancing the value of wearable devices in the monitoring and diagnosis of neurological and psychiatric diseases.

Black Phosphorus Nanosheets Protect Neurons by Degrading Aggregative α-syn and Clearing ROS in Parkinson's Disease.

Although evidence indicates that the abnormal accumulation of α-synuclein (α-syn) in dopamine neurons of the substantia nigra is the main pathological feature of Parkinson's disease (PD), no compounds that have both α-syn antiaggregation and α-syn degradation functions have been successful in treating the disease in the clinic. Here, it is shown that black phosphorus nanosheets (BPNSs) interact directly with α-syn fibrils to trigger their disaggregation for PD treatment. Moreover, BPNSs have a specific affinity for α-syn through van der Waals forces. And BPNSs are found to activate autophagy to maintain α-syn homeostasis, improve mitochondrial dysfunction, reduce reactive oxygen species levels, and rescue neuronal death and synaptic loss in PC12 cells. It is also observed that BPNSs penetrate the blood-brain barrier and protect against dopamine neuron loss, alleviating behavioral disorders in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced mouse model and hA53T α-syn transgenic mice. Together, the study reveals that BPNSs have the potential as a novel integrated nanomedicine for clinical diagnosis and treatment of neurological diseases.

Functional Neurological Disorder Presenting After Concussion: A Retrospective Case Series.

Although a majority of individuals recover from a concussion within weeks of the index injury, a substantial minority of patients report persistent postconcussion symptoms. Some of these symptoms may reflect a diagnosis of functional neurological disorder (FND). The authors evaluated the relationship between persistent postconcussion symptoms and FND symptoms. In this retrospective chart review, the authors characterized demographic and clinical information from 50 patients with a confirmed diagnosis of FND whose functional neurological symptoms started after a concussion. Patients who developed FND after a concussion had high rates of baseline risk factors for both persistent postconcussion symptoms and FND. After the concussive event, functional neurological symptoms presented abruptly or developed insidiously over time. Functional neurological symptoms ranged widely and included gait symptoms, seizures, speech and language symptoms, weakness, sensory symptoms, tremors, and vision and oculomotor symptoms. Functional neurological symptoms can arise after a concussion. FND should be considered in the differential diagnosis of individuals presenting with neurological symptoms beginning after a concussion. By failing to recognize functional symptoms, clinicians may inadvertently reinforce negative health-related beliefs regarding a patient's injured brain.

Identification and validation of N-acetylputrescine in combination with non-canonical clinical features as a Parkinson’s disease biomarker panel

Parkinson’s disease is a progressive neurodegenerative disorder in which loss of dopaminergic neurons in the substantia nigra results in a clinically heterogeneous group with variable motor and non-motor symptoms with a degree of misdiagnosis. Only 3–25% of sporadic Parkinson’s patients present with genetic abnormalities that could represent a risk factor, thus environmental, metabolic, and other unknown causes contribute to the pathogenesis of Parkinson’s disease, which highlights the critical need for biomarkers. In the present study, we prospectively collected and analyzed plasma samples from 194 Parkinson’s disease patients and 197 age-matched non-diseased controls. N-acetyl putrescine (NAP) in combination with sense of smell (B-SIT), depression/anxiety (HADS), and acting out dreams (RBD1Q) clinical measurements demonstrated combined diagnostic utility. NAP was increased by 28% in Parkinsons disease patients and exhibited an AUC of 0.72 as well as an OR of 4.79. The clinical and NAP panel demonstrated an area under the curve, AUC = 0.9 and an OR of 20.4. The assessed diagnostic panel demonstrates combinatorial utility in diagnosing Parkinson’s disease, allowing for an integrated interpretation of disease pathophysiology and highlighting the use of multi-tiered panels in neurological disease diagnosis.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning

Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network which is crucial for understanding and diagnosing neurological disorders. To tackle these issues, we introduce a novel prompt learning model by learning graph prompts during the fine-tuning process of multimodal large models for diagnosing neurological disorders. Specifically, we first leverage GPT-4 to obtain relevant disease concepts and compute semantic similarity between these concepts and all patches. Secondly, we reduce the weight of irrelevant patches according to the semantic similarity between each patch and disease-related concepts. Moreover, we construct a graph among tokens based on these concepts and employ a graph convolutional network layer to extract the structural information of the graph, which is used to prompt the pre-trained multimodal large models for diagnosing neurological disorders. Extensive experiments demonstrate that our method achieves superior performance for neurological disorder diagnosis compared with state-of-the-art methods and validated by clinicians.

Neuropsychiatric Manifestations and Neuroimaging Changes of Neurologic Wilson Disease–A Cross Sectional Study

Background: Wilson disease is genetic disorder of copper metabolism which causes hepatic & neurologic manifestation mainly. Chelator therapy is usually given as first line treatment in symptomatic wilson disease. Objective: Objective of our study was to evaluate the neuropsychiatric menufestations of wilson disease. Methods: A Cross sectional study was conducted at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, a tertiary care premier Postgraduate Medical Institution of Bangladesh. Forty five (45) patients of neurologic wilson disease were evaluated at In-patient department of Paediatric Neurology, during the period of January 2015 to December 2020. Diagnosis of Neurologic wilson disease was done, based on the characteristic clinical features of progressive deterioration of scholastic performances, dysarthria and gait disorder along with ophthalmologic findings, biochemical & neuroimaging findings. Results: Total numbers of studied cases were 45. Mean age was 124.65± 20.69 months and male to female ratio was 1.8:1. Most of the patient (83.33%) arrived from poor socio-economic background and rural area (66.67%) of Bangladesh. Among them 20.0% had history of consanguineous mating parents and 17.78% had another affected sibs. Common presenting features were progressive deterioration of school performance (89.74%), gait disturbance (92.31%), dysarthria (92.31%) and dystonia (41.03%) of our studied children. Neuropsychiatric manifestations were very common among the affected children. Changes in behavior was present in 62.22% cases, and among them mania were present in 57.77% children. Other common neuropsychiatric manifestations were depression (35.55%), generalized anxiety disorder in 31.11% and loss of memory in 20% cases. Ophthalmological manifestations like KF ring (83.33%) also found in majority of patient. Neuroimaging study showed basal ganglia hyperintensity in 63.63.% , white matter hyper intense signal changes 18.18% cases and ischemic change in grey matter in 18.18% cases. Conclusion: In our study about two third cases of Neurologic Wilson diseases may present with Neuropsychiatric manifestations in children. Behavior changes were commonest neuropsychiatric manifestations which includes depression, mania, generalized anxiety disorder and loss of memory.In neuroimaging study bilateral basal ganglia hyperintensity were found in about three-fourth cases of our studied children. J Bangladesh Coll Phys Surg 2024; 42: 144-148

Electrochemically actuated microelectrodes for minimally invasive peripheral nerve interfaces.

Electrode arrays that interface with peripheral nerves are used in the diagnosis and treatment of neurological disorders; however, they require complex placement surgeries that carry a high risk of nerve injury. Here we leverage recent advances in soft robotic actuators and flexible electronics to develop highly conformable nerve cuffs that combine electrochemically driven conducting-polymer-based soft actuators with low-impedance microelectrodes. Driven with applied voltages as small as a few hundreds of millivolts, these cuffs allow active grasping or wrapping around delicate nerves. We validate this technology using in vivo rat models, showing that the cuffs form and maintain a self-closing and reliable bioelectronic interface with the sciatic nerve of rats without the use of surgical sutures or glues. This seamless integration of soft electrochemical actuators with neurotechnology offers a path towards minimally invasive intraoperative monitoring of nerve activity and high-quality bioelectronic interfaces.

Visual Field Tests: A Narrative Review of Different Perimetric Methods.

Visual field (VF) testing dates back to fifth century B.C. It plays a pivotal role in the diagnosis, management, and prognosis of retinal and neurological diseases. This review summarizes each of the different VF tests and perimetric methods, including the advantages and disadvantages and adherence to the desired standard diagnostic criteria. The review targets beginners and eye care professionals and includes history and evolution, qualitative and quantitative tests, and subjective and objective perimetric methods. VF testing methods have evolved in terms of technique, precision, user-friendliness, and accuracy. Consequently, some earlier perimetric techniques, often still effective, are not used or have been forgotten. Newer technologies may not always be advantageous because of higher costs, and they may not achieve the desired sensitivity and specificity. VF testing is most often used in glaucoma and neurological diseases, but new objective methods that also measure response latencies are emerging for the management of retinal diseases. Given the varied perimetric methods available, clinicians are advised to select appropriate methods to suit their needs and target disease and to decide on applying simple vs. complex tests or between using subjective and objective methods. Newer, rapid, non-contact, objective methods may provide improved patient satisfaction and allow for the testing of children and the infirm.

SQUID magnetoneurography: an old-fashioned yet new tool for noninvasive functional imaging of spinal cords and peripheral nerves.

We are engaged in the development and clinical application of a neural magnetic field measurement system that utilizes biomagnetic measurements to observe the activity of the spinal cord and peripheral nerves. Unlike conventional surface potential measurements, biomagnetic measurements are not affected by the conductivity distribution within the body, making them less influenced by the anatomical structure of body tissues. Consequently, functional testing using biomagnetic measurements can achieve higher spatial resolution compared to surface potential measurements. The neural magnetic field measurement, referred to as magnetoneurography, takes advantage of these benefits to enable functional testing of the spinal cord and peripheral nerves, while maintaining high spatial resolution and noninvasiveness. Our magnetoneurograph system is based on superconducting quantum interference devices (SQUIDs) similar to the conventional biomagnetic measurement systems. Various design considerations have been incorporated into the SQUID sensor array structure and signal processing software to make it suitable for detecting neural signal propagation along spinal cord and peripheral nerve. The technical validation of this system began in 1999 with a 3-channel SQUID system. Over the course of more than 20 years, we have continued technological development through medical-engineering collaboration, and in the latest prototype released in 2020, neural function imaging of the spinal cord and peripheral nerves, which could also be applied for the diagnosis of neurological disorders, has become possible. This paper provides an overview of the technical aspects of the magnetoneurograph system, covering the measurement hardware and software perspectives for providing diagnostic information, and its applications. Additionally, we discuss the integration with a helium recondensing system, which is a key factor in reducing running costs and achieving practicality in hospitals.

The discovery of regional neurotoxicity-associated metabolic alterations induced by carbon quantum dots in brain of mice using a spatial metabolomics analysis

BackgroundRecently, carbon quantum dots (CQDs) have been widely used in various fields, especially in the diagnosis and therapy of neurological disorders, due to their excellent prospects. However, the associated inevitable exposure of CQDs to the environment and the public could have serious severe consequences limiting their safe application and sustainable development.ResultsIn this study, we found that intranasal treatment of 5 mg/kg BW (20 µL/nose of 0.5 mg/mL) CQDs affected the distribution of multiple metabolites and associated pathways in the brain of mice through the airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique, which proved effective in discovery has proven to be significantly alerted and research into tissue-specific toxic biomarkers and molecular toxicity analysis. The neurotoxic biomarkers of CQDs identified by MSI analysis mainly contained aminos, lipids and lipid-like molecules which are involved in arginine and proline metabolism, biosynthesis of unsaturated fatty acids, and glutamine and glutamate metabolism, etc. as well as related metabolic enzymes. The levels or expressions of these metabolites and enzymes changed by CQDs in different brain regions would induce neuroinflammation, organelle damage, oxidative stress and multiple programmed cell deaths (PCDs), leading to neurodegeneration, such as Parkinson’s disease-like symptoms. This study enlightened risk assessments and interventions of QD-type or carbon-based nanoparticles on the nervous system based on toxic biomarkers regarding region-specific profiling of altered metabolic signatures.ConclusionThese findings provide information to advance knowledge of neurotoxic effects of CQDs and guide their further safety evaluation.

Implementation of an Objective Structured Clinical Examination (OSCE) to Improve the Delivery of a Functional Neurological Disorder (FND) Diagnosis (P4-7.004)

Implementation of an Objective Structured Clinical Examination (OSCE) to Improve the Delivery of a Functional Neurological Disorder (FND) Diagnosis (P4-7.004)

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis.

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

Intelligent MRI diagnosis of neurological alterations in infants from 4 to 12 months

AbstractMagnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.