476 results about "Autism spectrum disorder" patented technology

The invention provides an autism assessment system and method based on multi-modal time sequence data fusion, and relates to the technical field of children autism spectrum disorder assessment calculation processing. The invention innovatively provides a dual time sequence alignment method based on dynamic time warping (DTW) and LSTM prediction, the problem of time sequence dislocation of cross-modal data (heart rate / eye movement / limb movement) caused by acquisition frequency difference is solved, millisecond-level synchronization precision is realized, the technical problem of cross-modal data time sequence dislocation in an existing autism assessment system is solved, and the accuracy of time sequence alignment of the cross-modal data in the autism assessment system is improved. The small-range time deviation caused by acquisition equipment delay or physiological response difference is eliminated, and the robustness and reliability of the autism evaluation system are improved.

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis scenes.

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention discloses a data classification method and system based on a deep multi-path attention adaptive graph convolutional network, and relates to the technical field of medical image processing. Rs-fMRI data is acquired and preprocessed to obtain a BOLD sequence, a function connection feature vector is constructed and input into a DMAGCN model, and finally an optimal model is acquired through five-fold cross validation for classification. The data quality is guaranteed through data preprocessing, and a foundation is laid for accurate analysis. Functional connection feature vectors effectively represent data, after a model is input, a Transform backbone network and an MLP branch network can extract multi-source domain features, non-imaging data are utilized in combination with a graph network, the model can learn rich features, and generalization and adaptability are enhanced. According to the method, the classification accuracy is improved, the performance is excellent when uncertain data classification such as autism spectrum disorder is processed, related disease research is promoted, and an efficient and reliable method is provided for medical data classification.

Disclosed herein is a method for stereotyped behavior detection for supporting diagnosis of Autism Spectrum Disorder (ASD). The method includes detecting a target object to be assessed in an input video, detecting a section in which a periodic behavior occurs using an image sequence of the target object, and classifying a stereotyped behavior in the section in which the periodic behavior occurs.

The invention belongs to the field of autism spectrum disorder recognition, and particularly discloses an autism spectrum disorder recognition method based on a multi-mode graph convolutional network, and the method comprises the steps: collecting patient data, preprocessing the data, and constructing the graph convolutional network. The MPPCA method based on the threshold value is designed, prior information related to noise variance and the PCA denoising threshold value are combined, signals with dominant noise can be better distinguished, the function of the prior information related to the noise variance and the like is fully played, and a better data basis is provided for follow-up phenotype prediction; according to the method, a multi-modal graph convolutional network is used, phenotypes are predicted according to brain imaging data, behavior data and gene data of a patient, advantage complementation among different data types is utilized, and specific parameterization design is performed on the weight of a filter, so that the recognition accuracy of the autism spectrum disorder is improved.

The invention relates to the field of artificial intelligence, in particular to an autism spectrum disorder knowledge graph construction method and system. The method comprises the following steps: acquiring multi-source heterogeneous autism spectrum disorder information; constructing an autism spectrum disorder original corpus; performing information extraction by using a hybrid convolutional neural network-long and short-term memory network-hidden Markov model; designing a credibility dynamic evolution mechanism; constructing and optimizing knowledge topology self-organization; and generating an autism spectrum disorder knowledge graph. According to the method, information is efficiently and accurately extracted by constructing the hybrid model, so that the analysis efficiency of the unstructured medical text is remarkably improved; by designing a credibility dynamic evolution mechanism, dynamic iterative optimization of the triad credibility is realized; by introducing an adversarial verification mechanism, the polarity of the triple is efficiently and accurately identified, and the manual intervention requirement is greatly reduced; by constructing a multi-granularity interpretation engine, cross-level correlation analysis of gene pathway-neural circuit-behavior phenotype is realized, and the analysis accuracy is improved.

The invention discloses a traditional Chinese medicine composition for treating autism spectrum disorder and application thereof, and belongs to the technical field of traditional Chinese medicine compositions. The traditional Chinese medicine composition is prepared from the following components in parts by mass: 4 to 10 parts of cortex cinnamomi, 4 to 10 parts of radix morindae officinalis, 4 to 10 parts of radix polygalae, 4 to 10 parts of rhizoma acori graminei, 5 to 10 parts of radix rehmanniae praeparata, 4 to 10 parts of radix angelicae sinensis, 8 to 20 parts of tortoise shell and 5 to 10 parts of herba dendrobii. From the perspective of overall compatibility, the cinnamon and the morinda officinalis in the composition can warm and tonify kidney yang, the prepared rehmannia root, the tortoise shell and the dendrobe nourish yin and tonify kidney to prevent excessive warm tonification, the angelica sinensis can enrich blood and activate blood and give consideration to spleen and stomach, the polygala tenuifolia can traffic heart and kidney, soothe the nerves and promote intelligence, and the rhizoma acori graminei can reduce phlegm, induce resuscitation and induce resuscitation. The medicines are matched with one another to jointly achieve the effects of tonifying spleen and kidney, replenishing essence and benefiting marrow, and inducing resuscitation and promoting intelligence, and the symptoms of autism are comprehensively improved from the fundamental pathogenesis of spleen-kidney deficiency and by considering pathological changes such as phlegm clogging the heart and the like.

Disclosed is a method and an oral composition for treating CNS disorders. An embodiment of the invention comprises orally administering an allopregnanolone containing composition to a subject having a CNS disorder. Exemplary CNS disorders include sleep disorders, mood disorders, dysthymic disorders, bipolar disorders, anxiety disorders, stress disorders, compulsive disorders, schizophrenia spectrum disorders, convulsive disorders, memory disorders, cognition disorders, movement disorders, personality disorders, autism spectrum disorders, substance abuse disorders, dementia, pain, traumatic brain injury (TBI), vascular disease, withdrawal syndrome, and tinnitus. The composition and methods disclosed herein exhibit effective oral absorption / bioavailability.

Systems and methods are described for treatment of neurological conditions in which transcranial illumination using infrared, near-infrared and / or red wavelengths of light are delivered into the brain of a patient using a portable head wearable device. Arrays of light emitters are simultaneously controlled to provide a selected power density and distribution during a therapeutic session to treat a condition of the patient. An external controller such as a touchscreen tablet or laptop computer can be used to select illumination parameters for each patient. The system can be used to provide treatment to children such as those having autism spectrum disorder (ASD).

Described herein are systems, methods, and other techniques for training and deploying a random forest model to predict a presence of ASD in a subject. A training dataset can be obtained including abundance measurements of a first cohort of subjects with ASD and a second cohort of subjects without ASD across the set of microbial markers. Multiple candidate random forest models are trained by generating a validation dataset comprising one of k subsets of the training dataset and a training subset comprising the remaining k-1 subsets, training a candidate random forest model of the k candidate random forest models using the training subset, and assessing the candidate random forest model using the validation dataset by computing performance metrics for the candidate random forest model. Candidate random forest models are evaluated by computing an AUC.

The present technology relates to a method of treating one or more behavioral symptoms of Autism Spectrum Disorder (ASD) in a subject by transdermally administering an effective amount of cannabidiol (CBD) to the subject wherein one or more behavioral symptoms of ASD are treated in the subject.

The invention discloses a data acquisition and management system for autism spectrum disorder child information, which comprises a puzzle game unit, a data acquisition unit, a data processing unit, a remote monitoring unit, a network transmission unit and a scheme customization unit, and belongs to the technical field of autism spectrum disorder. Intelligence game interaction is combined with various technical means of angle acquisition, sound wave acquisition, heart rate acquisition and response acquisition; comprehensive and objective data acquisition of expressions (such as correct rate and attention concentration degree), emotional fluctuation (through sound wave amplitude intensity), physiological status (such as heart rate change) and social reaction (such as response value) of children in the puzzle game is realized, and furthermore, the acquired data is scientifically analyzed through an algorithm formula, so that the data acquisition accuracy is improved. The accuracy and objectivity of evaluation are greatly improved, and multi-dimensional and all-around evaluation of the illness state of the child is achieved.

A system and method for stimulating the activation of neural mirroring mechanisms through personalized avatars to enhance learning and skill development. The system creates hyper-realistic or stylized digital representations of users and animates these avatars to present educational content or demonstrate skills, stimulating mirror neuron activation when users observe themselves demonstrating mastery. Applications include academic learning, language acquisition, professional training, personal development, athletic performance enhancement, and therapeutic interventions including for Autism Spectrum Disorder.

The invention provides an autism spectrum disorder classification method and device, electronic equipment and a storage medium, and relates to the field of image processing. The method comprises the following steps: acquiring a target image; the target image is an image comprising a target object; obtaining a fusion feature by capturing an internal relationship among a plurality of local regions in the target image and an interaction relationship between the local regions and a global region in the target image; classifying autism spectrum disorders according to the fusion features to obtain a classification result; the classification result is used for indicating the autism spectrum disorder type to which the target object belongs. According to the method and the apparatus, the problem of low classification accuracy of autism spectrum disorders in related technologies is solved.

The present technology relates to a method of treating one or more behavioral symptoms of Autism Spectrum Disorder (ASD) in a subject by transdermally administering an effective amount of cannabidiol (CBD) to the subject wherein one or more behavioral symptoms of ASD are treated in the subject.

The invention discloses a children autism spectrum disorder prediction method based on a brain enlightenment causal large model. The method comprises the following steps: S1, collecting and processing behavior data and brain data; forming a multi-modal data set by the behavior data and the brain data; s2, forming a unified multi-modal data set, and constructing a causal graph by adopting a structural equation model; s3, integrating data processing on the core dimension; s4, coding the knowledge graph text by adopting a mask language model; s5, Bayesian network learning and causal rule mining; s6, adopting an anti-fact strategy and a depolarization strategy; s7, proposing a training and optimizing method; and S8, predicting and evaluating the children autism spectrum disorder based on the brain enlightenment causal large model. According to the children autism spectrum disorder prediction method based on the brain enlightenment causal large model, multi-modal data are integrated, the causal large model is constructed by using the knowledge graph, and the potential causal relationship among the factors of the autism spectrum disorder ASD is revealed.

The invention provides a neural developmental disorder prediction model construction method, system and device and a medium, and the method comprises the steps: obtaining the clinical information of a child patient, the clinical information comprises severity of developmental retardation / dyspepsia, complexity of syndromic neurodevelopmental disorder, autism spectrum disorder, epilepsy, attention deficit hyperactivity disorder, head circumference deformity, brain development deformity, hearing / visual function impairment and related ear-eye development deformity; applying logistic regression analysis to screen out predictive factors significantly related to the positive diagnosis rate of third-generation whole exon genome sequencing in the clinical information; constructing a neural developmental disorder prediction model based on binary logistic regression according to the prediction factor; and verifying the neurodevelopmental disorder prediction model by applying data of the internal verification group and the external verification group so as to evaluate the prediction efficiency of the neurodevelopmental disorder prediction model, so that the probability of obtaining positive diagnosis after the child patient receives trio-WES detection can be accurately evaluated, and the prediction efficiency is remarkably improved.

Disclosed are compounds, and corresponding methods of treatment, that activate myocyte-specific enhancer factor 2 (MEF2) transcriptional activity that are therefore useful in treating deficits in MEF2C activity found in autism spectrum disorder (ASD), intellectual disability (ID), attention deficit and hyperactivity disorder (ADHD), and in diseases characterized by cognitive decline such as Alzheimer's disease (AD), Lewy body dementia (LED), Frontotemporal dementia (FTD), and other forms of dementia, as well as movement disorders such as Parkinson's disease (PD) and parkinsonism from other causes.

The invention is suitable for the technical field of medical diagnosis, and provides a diagnosis processing method and system for autism spectrum disorders, and the method comprises the steps: obtaining electroencephalogram signal data and eye movement thermodynamic images of a to-be-diagnosed person in a plurality of diagnosis tasks; according to a preset extraction condition, feature extraction is carried out on the electroencephalogram signal data and the eye movement thermodynamic images of the multiple diagnosis tasks, and electroencephalogram feature vectors and eye movement features of the multiple diagnosis tasks are obtained; respectively carrying out cross fusion on the electroencephalogram feature vectors and the eye movement features of the plurality of diagnosis tasks to obtain multi-modal fusion attention features of the plurality of diagnosis tasks; and performing classification prediction on the multiple multi-modal fusion attention features to obtain a diagnosis result of the to-be-diagnosed person, thereby solving the problems of strong subjectivity, high misdiagnosis rate and limitation in data processing due to dependence on behavior observation and questionnaire survey in the diagnosis process of the autism spectrum disorder person in the prior art.

A system for recognizing autism based on hybrid deep learning includes a data acquisition module, a skeleton keypoint extraction module and a recognition and classification module. The data acquisition module is configured for obtaining a dataset based on a parent-child dyad block game protocol. The skeleton keypoint extraction module is configured for identifying a plurality of skeleton keypoints of a target and a position of each of the plurality of skeleton keypoints in the video data based on a high-resolution network to generate a skeleton sequence. The recognition and classification module is configured for classifying the child into autism spectrum disorder (ASD) children and typically developing (TD) children by inputting the skeleton sequence in a graph form into a Two-stream Graph Attention Long Short-Term Memory (2sG-ALSTM) network architecture.

This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an α5-containing GABAA receptor agonist (e.g., an α5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an α5-containing GABAA receptor agonist (e.g., an α5-containing GABAA receptor positive allosteric modulator) in treating brain cancers (including brain tumors, e.g., medulloblastomas), and cognitive impairment associated therewith.

Aspects of the disclosure relate to methods and compositions for diagnosing and / or treating subjects having one or more symptoms of Autism Spectrum Disorder (ASD) or subjects at risk of developing ASD. In some embodiments, methods comprise determining and / or modulating levels of chorismate and / or a molecule within a chorismate metabolic pathway in a subject. In some embodiments, methods comprise determining and / or modulating levels of a microbial feature (e.g., a Sarcina bacterium) in a subject. In some embodiments, methods comprise determining and / or modulating levels of proton flux in a gastrointestinal tract in a subject.

The invention discloses an autism spectrum disorder diagnosis method and device, equipment and a medium, and the method comprises the steps: obtaining audio and video data of a test patient, and converting the audio and video data into behavior log data; and converting the behavior log data into script description data, inputting the script description data and doctor question data into a preset large language model, and obtaining a diagnosis reason and a diagnosis result of the autism spectrum disorder of the tested patient. Compared with the prior art, the method has the advantages that massive clinical data is not needed, and only video signals and audio signals of normal form interaction between a doctor and a tested patient during diagnosis in the testing process need to be converted into script description data which can be understood by a large language model; and then, a diagnosis reason and a diagnosis result of the autism spectrum disorder of the tested patient are generated by utilizing logical reasoning and text generation capability of the large language model, so that the diagnosis result is quickly obtained, and the diagnosis credibility of a doctor is further improved.

The invention discloses a medicine for treating autism, application of the medicine for treating autism in preparation of a medicine for improving social competence damage of autism animals or people, and application of the medicine for treating autism in preparation of a medicine for improving anxiety behaviors of autism animals or people. When the high-dose vitamin D is used for intervening autism mice, the method has the advantages of being economical, convenient to take and good in effect, animal experiment evidence is provided for supplementing large-dose VD to intervene autism spectrum disorders in the early stage of clinical experiments, and a new thought is provided for early prevention and treatment of neurodevelopment disorders of clinical children.

The invention relates to an autism spectrum disorder screening application system which comprises a front-end user interaction device and a background data processing server. The front-end user interaction device comprises a child information management module; the scale screening module is used for providing at least one autism screening scale for the user to select and fill, and generating a scale screening score after receiving a scale answer filled by the user; the photo screening module is used for receiving at least one child front face image uploaded by the user, preprocessing the face image and then calling a photo analysis model to generate a photo screening score; the screening process of the front-end user interaction device comprises the steps that after a user selects a target child through the child information management module, operation of the scale screening module and the photo screening module is completed in sequence, and the background data processing server generates a comprehensive score and a risk level for scale screening scores and photo screening scores through a weighted voting mechanism. Through the application system, accurate ASD pre-diagnosis classification can be quickly provided.

The invention relates to the technical field of neuroscience and artificial intelligence, in particular to an autism spectrum disorder prediction method based on EEG machine learning, which comprises the following steps: step 1, collecting EEG data for children with autism spectrum disorder and healthy control children; step 2, preprocessing the collected EEG data, then extracting sleep spindle wave signals in the EEG data, and analyzing the sleep spindle wave signals to obtain feature information data; step 3, performing standardization processing on the obtained feature information data to obtain a data set, and then dividing the data set into a training set and a test set; 4, training the training set through different machine learning algorithms to obtain corresponding training models, and selecting an optimal model from the training models through a five-fold cross validation strategy; and 5, inputting the test set into each optimal model for evaluation, and comparing evaluation results to obtain a target model. The method provides a scientific basis for early intervention of ASD.

Disclosed herein is a method for supporting Autism Spectrum Disorder (ASD) diagnosis based Artificial Intelligence (AI). The method includes extracting a detection area and voice corresponding to an inspector from an input video, extracting a detection area and voice corresponding to an assessment subject from the input video, extracting a feature of the inspector and a feature of the assessment subject, and extracting an interaction feature using the feature of the inspector and the feature of the assessment subject.

Provided herein are 19-nor C3,3-disubstituted C21-pyrazolyl steroids of Formula (I):and pharmaceutically acceptable salts thereof; wherein , R1, R2, R3a, R3b, R4a, R4b, R5, R6, and R7 are as defined herein. Such compounds are contemplated useful for the prevention and treatment of a variety of CNS-related conditions, for example, treatment of sleep disorders, mood disorders, schizophrenia spectrum disorders, convulsive disorders, disorders of memory and / or cognition, movement disorders, personality disorders, autism spectrum disorders, pain, traumatic brain injury, vascular diseases, substance abuse disorders and / or withdrawal syndromes, and tinnitus.