System for aiding the diagnosis of neurodevelopmental disorders and associated mental health disorders in a child or adolescent user

Abstract

The present invention relates to a system for aiding the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, the system comprising: an interface module configured to present an interactive program, preferably a game, to the user; an acquisition module configured to simultaneously receive a plurality of temporal biometric signals resulting from the user's interaction with the interface module when the user interacts with the program; an extraction module configured to extract or calculate, on the basis of each temporal biometric signal, at least one relevant characteristic for the evaluation of at least one symptom related to at least one neurodevelopmental disorder or associated mental health disorder in the child or adolescent; and a classification module configured to assign to the user, on the basis of the characteristics extracted or calculated by the extraction module and a machine learning model pretrained on a set of annotated data collected from child users, one or more scores relating to one or more symptoms relating to one or more neurodevelopmental disorders or associated mental health disorders.

Description

SYSTEM FOR AID IN THE DIAGNOSIS OF ASSOCIATED NEURODEVELOPMENTAL AND MENTAL HEALTH DISORDERS IN A CHILD OR ADOLESCENT USER FIELD OF THE INVENTION

[0001] The present invention relates to a system for assisting in the diagnosis of neurodevelopmental and associated mental health disorders in a child or adolescent user, and an associated computer-implemented method.

[0002] The invention may correspond to a medical device making it possible to automatically determine at least one score relating to a symptom associated with one or more neurodevelopmental and mental health disorders associated with the child or adolescent thanks to the combination of biometrics and machine learning techniques. STATE OF THE ART

[0003] Worldwide, one in six children and adolescents suffer from neurodevelopmental and mental health disorders associated with long-lasting negative impacts: loss of self-esteem, academic failure, and suicidal thoughts. An estimated 166 million young people are affected in their daily lives, whether academically, behaviorally, or socially.

[0004] Neurodevelopmental disorders (NDDs) begin in childhood and can become chronic conditions that persist throughout life and lead to a reduction in life expectancy of 10 to 15 years. The earlier these disorders are detected, the sooner it is possible to put in place appropriate help and support in and outside of school, which has shown significant improvements on several levels (reduction of symptoms, better neurological development, etc.).

[0005] Unfortunately, currently, it takes between two and four years and multiple visits with different healthcare practitioners to obtain a diagnosis. Furthermore, there is little cultural adaptation of psychiatric services to clinical assessment. Current computerized patient records do not offer this functionality. Finally, few clinical data resources are structured.

[0006] Furthermore, up to 10% of children have one or more neurodevelopmental disorders. For example, learning difficulties are present in 44% of children with Attention-Deficit / Hyperactivity Disorders (ADHD) and in 65-85% of children with autism, and the co-occurrence of ADHD and autism ranges from 30 to 70%. Finally, associated neurodevelopmental and mental health disorders present complex clinical phenotypes often associated with transnosographic dimensions such as emotion dysregulation and executive dysfunction. These dimensions impair adaptive functioning, often leading to negative consequences such as peer rejection, bullying, harsh parenting, or severe neglect.Furthermore, the phenotypes of neurodevelopmental disorders overlap significantly, complicating diagnosis and treatment planning in everyday clinical practice.

[0007] Thus, there is a real need to be able to capture a complex set of characteristics of each child on a large scale using robust and meaningful means, which would allow understanding the underlying mechanisms and thus provide effective support to those who need it. However, at the moment, there is no single, consensual digital tool that facilitates the initial assessment and diagnosis of patients, as well as their referral to the most appropriate therapy. The objective of this tool is to better understand the full range of patients' symptoms in a more agnostic and holistic (transdiagnostic) way, to monitor the progression and severity of a patient's symptoms and to provide indicators for personalized therapy. The invention aims to address this gap.

[0008] Finally, it should be noted that the brain structure of children and adolescents as well as their social behaviors being different from adults, it is essential to have a tool adapted to these specificities. Indeed, the application of tools developed for adults, on children and adolescents whose cognitive, behavioral and biological development is not complete and whose physiological reactions are not comparable would not allow for obtaining correctly exploitable and interpretable results. SUMMARY OF THE INVENTION

[0009] Thus, to address these various issues, the subject of the present disclosure is a system for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising: an interface module configured to present to the user an interactive program, preferably the program is a game, an acquisition module configured to simultaneously receive a plurality of temporal biometric signals resulting from the interaction of the user with the interface module when the user interacts with said program, an extraction module configured to extract or calculate from each temporal biometric signal, at least one characteristic relevant for the evaluation of at least one neurodevelopmental or associated mental health disorder in the child or adolescent, and a classification module configured to,from the characteristics extracted or calculated by the extraction module and a machine learning model previously trained on a set of annotated data collected on child or adolescent users, assigning to the user one or more scores relating to one or more associated neurodevelopmental or mental health disorders.,

[0010] Preferably, the score(s) assigned by the classification module relate to one or more symptoms, which in turn relate to one or more associated neurodevelopmental or mental health disorders. This allows each symptom to be independently quantified, the presence and severity of which may vary depending on the disorder being diagnosed.

[0011] Regarding the annotated training data collected on child or adolescent users, these were previously annotated to determine the presence or absence of at least one symptom relating to at least one disorder of the neurodevelopment and / or at least one associated mental health disorder, and the annotated data includes at least one severity rating relating to said symptoms of the diagnosed disorders.

[0012] The various temporal biometric signals (i.e. containing characteristic information of a user) recorded by the acquisition module while the user interacts with the interface module, make it possible to obtain precise measurements of the user's response to one or more external stimuli. These multimodal signals are then processed by the extraction module which will extract or calculate characteristics from these signals.

[0013] The characteristics can be obtained directly by mathematical calculation or involve a machine learning model. The characteristics are advantageously chosen in relation to the associated neurodevelopmental or mental health disorder(s). These characteristics can constitute all or part of a digital phenotype of the user.

[0014] The final classification is carried out by the classification module which, based on the extracted characteristics, will be able to assign one or more scores to one or more disorders, allowing assistance in diagnosing the disorders considered in the child or adolescent user. An appropriate therapeutic response can then be proposed to the user based on the score(s) obtained.

[0015] Until now, no prior art system allows the allocation of scores facilitating transdiagnosis and then orientation towards personalized treatment of neurodevelopmental disorders in children or adolescents in an automated manner and from several types of signals analyzed concomitantly and over time, as proposed by the present disclosure.

[0016] Preferably, the neurodevelopmental disorders may be selected from the following: attention deficit hyperactivity disorder, communication disorder, specific learning disorder, and / or motor disorder. As Defined by the ICD-10 classification, neurodevelopmental disorder codes are F-80 to F-98.

[0017] Preferably, associated mental health disorders may be selected from the following: depression, anxiety, sleep disturbances, social interaction disturbances, and / or emotional dysregulation disorders. These correspond to codes F-20 to F-59 defined in the ICD-10 classification.

[0018] Preferably, the plurality of temporal biometric signals may be selected from: a video signal comprising tracking of the user's eyes or face, a position signal of a limb of the user, an audio signal of the user's voice, an electroencephalogram, and / or a physiological signal of the user.

[0019] These biometric signals are particularly relevant for the assessment of associated neurodevelopmental or mental health disorders in children or adolescents.

[0020] Preferably, the acquisition module may comprise a camera and is configured to receive a video signal comprising the tracking of the user's eyes, and a characteristic extracted from the video signal is chosen from the following: projection of the gaze onto a surface, heat map of the projection of the gaze onto a surface, emotional profile of the user, facial action units, blinking of the user's eyes, acceleration of the projection of the user's gaze, angle formed by the user's gaze relative to the perpendicular to a surface, saccades in the trajectory of the gaze, the number of fixations of the gaze on a specific item, the time spent looking at a specific item.

[0021] These features are advantageously relevant for the assessment of associated neurodevelopmental or mental health disorders from a video signal of the user's eyes and / or face.

[0022] Preferably, the acquisition module may comprise a touch surface or a specific acquisition device and is configured to receive a signal from position of at least one finger or hand of the user, and a feature extracted from the position signal is chosen from: trajectory of the finger or hand, speed of the finger or hand, acceleration of the finger or hand, pressure of the finger or hand, heat map of the position of the finger or hand, number of touches of the screen by the user.

[0023] These features are advantageously relevant for the assessment of neurodevelopmental or mental health disorders associated with a signal of positioning of the user's fingers or hand on a surface such as the trajectory, speed, acceleration, finger pressure or gyroscopic generated by the hand. Preferably, the surface is a touch screen, and the interface module displays the interactive program on said touch screen.

[0024] Preferably, the acquisition module may comprise a microphone and is configured to receive an audio signal, and a characteristic extracted from the audio signal is chosen from: a transcription of the user's speech, the variations in the frequency of the user's voice, the acoustic characteristics of the speech signal such as: instability (called "jitter"), shimmer (called "shimmer"), tremor, harmonic-noise ratio, frequency disturbance ratio, amplitude disturbance ratio, peak slope, average of the 1st harmonic (Fl), average of the 2nd harmonic (F2), variability of Fl, variability of F2, range of Fl, vowel space, linear predictive coding coefficients, frequency-Mel cepstral coefficients (MFCC), average of the fundamental frequency (FO), variability of FO, range of FO, average of the intensity, variability of the intensity, variance of the energy, velocity of the energy, maximum phonation time, speech rate,articulatory rate, average utterance duration, average pause duration, pause rate, total pauses.,

[0025] These features are advantageously relevant for the assessment of neurodevelopmental or associated mental health disorders from an audio signal comprising the user's voice or acoustic reactions.

[0026] Preferably, the acquisition module may comprise at least one of: a sensor for measuring a physiological signal, a touch screen, a mouse, a microphone, a camera, a keyboard, an accelerometer, a gyroscope.

[0027] For example, a physiological signal can be an electroencephalogram, an electrocardiogram, a heart rate, a blood pressure.

[0028] Preferably, the system may be included in a tablet equipped with a touch screen, a camera, and a microphone.

[0029] Such a system is compact and easy to use for a child or adolescent user.

[0030] Preferably, the extraction module may involve at least one machine learning algorithm previously trained on a set of annotated signals collected from child or adolescent users.

[0031] In particular, the annotation of signals can be carried out by a healthcare professional using a questionnaire.

[0032] Preferably, the machine learning model of the classification module may comprise at least one artificial neural network.

[0033] Preferably, the artificial neural network may comprise a single layer.

[0034] Such a neural network makes it possible to improve the explainability of the classification obtained, which makes it possible to know the contribution of the different characteristics to the final classification.

[0035] Preferably, the system may comprise a comparison module, configured to compare the score(s) assigned to the user with one or more reference values.

[0036] Preferably, the system may further comprise a display module comprising a display for displaying the obtained score(s) to the user or an authorized practitioner.

[0037] Preferably, the interface module may comprise a display device, and the acquisition, extraction and classification modules are included in one or more processing units each comprising a processor.

[0038] According to another embodiment, the present disclosure also relates to a system for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising: a human-machine interface configured to present to the user an interactive program, preferably the program is a game, and to simultaneously receive a plurality of temporal biometric signals resulting from the interaction of the user with the interface module when the user interacts with said program, at least one processor configured to: o extract or calculate from each temporal biometric signal, at least one characteristic relevant for the assessment of at least one neurodevelopmental or associated mental health disorder in the child or adolescent,and o assign to the user one or more scores relating to one or more associated neurodevelopmental or mental health disorders, based on the characteristics extracted or calculated by the extraction module and a machine learning model previously trained on a set of annotated training data collected on child or adolescent users having been annotated to determine the presence or absence of at least one neurodevelopmental disorder and / or at least one associated mental health disorder, the annotated data comprising at least one degree of severity relating to said diagnosed disorders;, at least one output interface (i.e., at least one output) configured to provide the score(s) assigned to the user, optionally said at least one output interface is configured to send the score(s) assigned to the user to a health practitioner able to diagnose at least one neurodevelopmental and / or mental health disorder associated with the user.

[0039] Advantageously, said at least one processor is also configured to identify a probability that the child or adolescent user presents at least one symptom relating to a neurodevelopmental and / or associated mental health disorder.

[0040] The present disclosure also relates to a computer-implemented method for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising at least the following steps: presenting an interactive program to the user, simultaneously acquiring a plurality of temporal biometric signals resulting from the user's interaction with the interactive program, extracting or calculating, for each temporal biometric signal, at least one characteristic relevant for the assessment of an associated neurodevelopmental or mental health disorder in the child or adolescent, and attributing, using the extracted or calculated characteristics and a machine learning model previously trained on a set of annotated data collected on child users,of one or more scores relating to one or more neurodevelopmental disorders.,

[0041] The present disclosure also relates to a computer-implemented method for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising at least the following steps: presenting an interactive program to the user, simultaneously acquiring a plurality of temporal biometric signals resulting from the user's interaction with the interactive program, extracting or calculating, for each temporal biometric signal, at least one characteristic relevant for the assessment of a symptom relating to an associated neurodevelopmental or mental health disorder in the child or adolescent, and attributing, using the extracted or calculated characteristics and a pre-trained machine learning model on a set of annotated data collected on child users, of one or more scores relating to one or more symptoms of neurodevelopmental disorders

[0042] The preferential and advantageous characteristics related to the system previously described are also applicable to the methods described below. DEFINITIONS

[0043] In this disclosure, the terms below are defined as follows:

[0044] "Neurodevelopmental disorders" are characterized by a disruption in the child's cognitive, behavioral or emotional development that impairs adaptive school, social and family functioning.

[0045] A "multimodal signal" refers to a set of components that are represented or transmitted simultaneously across multiple sensory modes or communication channels. These sensory modes can include vision, hearing, touch, taste, and smell. The components of a multimodal signal allow the receiver to extract different information. By combining components from different sensory modalities, a multimodal signal allows for richer and more complex information, reflecting the diversity of sensory stimuli present in our environment. For example, a video may contain both images and sounds, two different modalities.

[0046] The terms "Adapted" and "Configured" are used in this disclosure to broadly encompass the initial configuration, subsequent adaptation or addition of this device, or any combination thereof, whether by hardware or software (including firmware) means.

[0047] The term "Processor" shall not be construed as being limited to hardware capable of executing software, and generally refers to a device for processing, which may, for example, include a computer, a microprocessor, an integrated circuit, or a programmable logic device (PLD). The processor may also include one or more graphics processing units (GPUs), whether used for computer graphics and image processing or other functions. In addition, the instructions and / or data for performing the associated and / or resulting functionalities may be stored on any medium readable by the processor, such as, for example, an integrated circuit, a hard disk drive, a CD (Compact Disc), an optical disk such as a DVD (Digital Versatile Disc), RAM (Random-Access Memory), or ROM (Read-Only Memory). The instructions may be stored, inter alia, in hardware, software, firmware, or any combination thereof. BRIEF DESCRIPTION OF THE FIGURES

[0048] Figure 1 is a diagram illustrating a system according to one embodiment of the invention.

[0049] Figure 2 represents another embodiment of a system according to the invention.

[0050] Figure 3 is a flowchart illustrating the different steps of a computer-implemented method according to one embodiment of the invention.

[0051] Ligures 4A and 4B illustrate an example. DETAILED DESCRIPTION

[0052] This description illustrates the principles of the present disclosure. It is noted that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its scope.

[0053] All examples and conditional language cited in this document are intended to help the reader understand the principles of disclosure and the concepts made by the inventors to the advancement of the art, and should be construed as not being limited to these specifically cited examples and conditions.

[0054] In addition, all statements of principles, aspects, and embodiments of the disclosure, as well as their specific examples, are intended to encompass their structural and functional equivalents. Furthermore, it is intended that these equivalents include both currently known equivalents and equivalents developed in the future, i.e., any developed element that performs the same function, regardless of structure.

[0055] Thus, for example, those skilled in the art will understand that the block diagrams presented herein may represent conceptual views of circuits illustrating the principles of the disclosure. Similarly, it will be appreciated that any flowcharts, step diagrams, and the like depicting various processes may be substantially present in computer-readable media and operating systems, whether or not that computer or processor is explicitly depicted.

[0056] The functions of the various elements illustrated in the figures may be performed by the use of dedicated hardware as well as hardware capable of executing software in association with the appropriate software. When performed by a processor, the functions may be performed by a single dedicated processor, a single shared processor, or a plurality of individual processors, some of which may be shared.

[0057] It is understood that the elements shown in the figures may be implemented in various forms of hardware, software, or combinations thereof. Preferably, these elements are implemented in a combination of hardware and software on one or more suitably programmed general-purpose devices, which may include a processor, memory, and inputs / outputs.

[0058] System

[0059] A system 100 according to one embodiment of the invention is shown in Figure 1. The system 100 is configured to enable assistance in the diagnosis of disorders of the neurodevelopment or mental health disorders associated with these neurodevelopmental disorders, in children or adolescents.

[0060] It should be noted that neurodevelopmental disorders and / or associated mental health disorders, such as autism spectrum disorder, can sometimes be detected in the first months of life and diagnosed around the age of 2 or later. In adolescents, science estimates that biological brain development continues until around the age of 20. Thus, System 100 is best suited for children aged around 2 (i.e. the age at which the child is able to interact with System 100) and older, up to young adults aged around 20.

[0061] Neurodevelopmental disorders can be chosen from among the following: attention deficit hyperactivity disorder, communication disorder, specific learning disorder, and / or motor disorder. As defined by the ICD-10 classification, neurodevelopmental disorder codes are F-80 to F-98. The ICD stands for the International Classification of Diseases, which is a medical classification system developed by the World Health Organization, WHO, and the 10 corresponds to the 10 eme edition of this classification.

[0062] Associated mental health disorders can be chosen from the following: depression, anxiety, sleep disorders, social interaction disorders, and / or emotional dysregulation disorders. These correspond to codes F-20 to F-59 defined in the ICD-10 classification.

[0063] The system 100 comprises in particular: an interface module 110, an acquisition module 120, a processing unit 130 itself comprising an extraction module 131, a classification module 133, and possibly a comparison module 135.

[0064] The interface module 110 is configured to present to the user 1 an interactive program, i.e. one or more tasks for stimulating the user and which require a response from the latter. Preferably, the interactive program is a game, allowing better adherence of child or adolescent users. The type of game can advantageously be adapted to the detection of specific symptoms. The interface module 110 can comprise a display screen and means for the user to interact with the interactive program (for example a touch surface, a mouse, and / or a keyboard).

[0065] Advantageously, when the interactive program is a game, it may include progression events as well as challenges to be achieved, making it possible to evaluate different levels of user reactions. For example, the game may be organized so as to define an expected behavioral trajectory from users based on biometric signals and characteristics targeted according to a neurodevelopmental disorder or an associated mental health disorder. The game may also include a notion of failure or success at each level played by the user.

[0066] Advantageously, the times of events are also recorded, such as the time spent on a level, the time between two game actions, etc. In the context of recording the position signal of at least one finger, the touch events can be calculated to construct a complete trace of all digital inputs collected on a touch surface, such as the speed of gestures, their length, their duration, etc.

[0067] The acquisition module 120 is configured to simultaneously receive a plurality of different temporal biometric signals 121 resulting from the interaction of the user with the interface module 110 when the user interacts with said program. A temporal biometric signal is a signal that is characteristic of the user and specific to his person. Such a signal can be acquired in various ways, either by using the means of interaction between the user and the interface module (for example the touch surface, the mouse and / or the keyboard), or with distinct specific sensors (for example a camera, a microphone, or a biological signal sensor).

[0068] Advantageously, each temporal biometric signal is unimodal (i.e.: auditory, visual, tactile, etc.), for example: a temporal biometric signal corresponds to the sound emitted by the use, a temporal biometric signal corresponds to the interaction of the user with the touch surface, a time-based biometric signal corresponds to the user's video recording, etc.

[0069] The plurality of temporal biometric signals 121 may be chosen, for example, from: a video signal comprising tracking of the user's eyes or face, a position signal of a member of the user, an audio signal, an electroencephalogram, and / or a physiological signal.

[0070] In an exemplary embodiment, the acquisition module 120 comprises a camera. The camera can be configured to receive a video signal comprising a succession of images of the user. The video signal can in particular allow the tracking of the user's eyes. A characteristic extracted from the video signal is chosen from the following: projection of the gaze onto a surface, heat map of the projection of the gaze onto a surface, emotional profile of the user, facial action units, blinking of the user's eyes, acceleration of the projection of the user's gaze, angle formed by the user's gaze relative to the perpendicular to a surface, saccades in the trajectory of the gaze, the number of fixations of the gaze on a specific item, the time spent looking at a specific item.

[0071] In an exemplary embodiment, the acquisition module 120 comprises a touch surface or a specific acquisition device and is configured to receive a position signal from at least one finger or hand of the user, and a characteristic extracted from the position signal is chosen from: acceleration of the finger or hand of the user, heat map of the position of the finger or hand, number of touches of the screen by the user.

[0072] In an exemplary embodiment, the acquisition module 120 further comprises at least one microphone. The microphone is configured to receive an audio signal, in particular an audio comprising the sound produced by the user's voice during interaction with the program. A characteristic extracted from the audio signal is chosen from: a transcription of the user's speech, variations in the frequency of the user's voice, acoustic characteristics of the speech signal such as: jitter, shimmer, tremor, harmonic-noise ratio, frequency disturbance ratio, ratio amplitude disturbance, peak slope, 1st harmonic mean (Fl), 2nd harmonic mean (F2), Fl variability, F2 variability, Fl range, vowel space, linear predictive coding coefficients, Mel-frequency cepstral coefficients (MFCC), fundamental frequency (FO) mean, FO variability, FO range, intensity mean, intensity variability, energy variance, energy velocity, maximum phonation time, speech rate, articulatory rate, mean utterance duration, mean pause duration, pause rate, total pauses.

[0073] Table 1 describes the different usable acoustic characteristics:

[0074] In an exemplary embodiment, the acquisition module 120 comprises at least one of: a sensor for measuring a physiological signal, a touch surface, a touch screen, a mouse, a microphone, a camera, a keyboard, an accelerometer, a gyroscope. The acquisition module 120 can alternatively use the touch surface, the touch screen or the mouse of the interface module.

[0075] The processing unit 130, which may comprise one or more different processors, comprises the extraction module 131 and the classification module 133.

[0076] The extraction module 131 is configured to extract or calculate, from each temporal biometric signal, at least one characteristic relevant for the evaluation of at least one symptom relating to a neurodevelopmental disorder or associated mental health in the child or adolescent. The extraction module 131 can perform a calculation on a biometric signal (for example a mathematical transformation, the application of a specific mathematical function) or involve a machine learning model. When it is a machine learning model, the model has been previously trained on a training database, comprising a set of data collected on child or adolescent users and annotated, making it possible to obtain better results on these users than known methods.

[0077] For example, when each temporal biometric signal is unimodal, such as for an audio signal for example, the extraction may be the extraction of each of the characteristics, such as the acoustic characteristics mentioned above (see Table 1), into an associated temporal signal. Thus, the machine learning model may advantageously comprise a plurality of already trained artificial intelligence models. For example, the use of already trained deep learning networks for the detection of face and eye points, head position, emotion recognition, gaze vector estimation.

[0078] Advantageously, the machine learning model(s) used include an explainability module allowing extraction of features in such a way that they are interpretable and effective for prediction based on human experience.

[0079] Since different temporal biometric signals can be acquired with different sampling rates (e.g.: video at a rate of 28 frames per second, audio signal: 16 kHz, tactile signal: ~ 100 Hz), it is advantageous to align the acquired data to allow comparison both on the same user or between different users. Preferably, features are extracted for each modality and then aggregated at the lowest sampling rate (usually that of the video) using an average or sum function, depending on the feature considered. Thus, temporally aligned multimodal data advantageously consists of n features per frame (video), where n is an integer greater than or equal to 1.

[0080] In some cases, since temporal biometric signals may have longer or shorter durations depending on the interactions with users, it is advantageous to introduce a concept of grouped data, in which the signals of each user are divided into an arbitrary number of time intervals (e.g. p time intervals where p would be an integer greater than or equal to 1, for example but not limited to p would be equal to 100). Thus, an interval can group a variable number of images per participant, since the number of images varies according to the participants but also according to the interactive program used, but the measured data are distributed in the same number of intervals. In each interval, the characteristics can, for example, be aggregated over the images using an average or sum function, depending on the characteristic studied.

[0081] Thus the extraction model 131 receives as input the acquired biometric signals, corresponding to a time series comprising a set of characteristics, which it transforms into a matrix of size n xp comprising n characteristics extracted or calculated and divided into p intervals.

[0082] The classification module 133 is further configured to, based on the characteristics extracted or calculated by the extraction module and a machine learning model (previously trained on a set of annotated data collected on child or adolescent users), assign to the user one or more scores relating to one or more symptoms relating to at least one or more associated neurodevelopmental or mental health disorders, representing the mental state of the user as a network of symptoms. These scores must correspond to the scores of the scales that have been demonstrated to have clinical meaning. Indeed, each scale concerns at least one symptom relating to at least one disorder whose score makes it possible to evaluate the degree of presence or severity.

[0083] For example, one of the most widely used assessment instruments for the autism spectrum is the DSM-V (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition), published by the American Psychiatric Association. The DSM-V provides the official diagnostic criteria for autism spectrum disorders (ASD). The DSM-V, which replaced the DSM-IV in 2013, introduced the category of Autism spectrum disorder, which encompasses several pre-existing disorders, such as childhood autism, Asperger's syndrome, and pervasive developmental disorder not otherwise specified. The DSM-V diagnostic criteria for ASD are based on two main domains: persistent deficits in social communication and restricted and repetitive behaviors. For each criterion, severity is rated on a scale from 1 to 3 or 0 to 3, depending on the specific criterion. An overall assessment of symptom severity in ASD can be obtained by combining these ratings. For example, for deficits in nonverbal social communication behaviors, the severity scale is: 0 - No deficit observed, 1 - Mild deficit, 2 - Moderate deficit, and 3 - Severe deficit.These severity ratings are used for each diagnostic criterion, and they contribute to an overall assessment of ASD based on the presence and severity of symptoms in the areas of social communication and restricted and repetitive behaviors.

[0084] For training data collected from child or adolescent users, the data has been previously annotated to determine the presence or absence of at least one symptom related to at least one neurodevelopmental disorder and / or at least one associated mental health disorder, the annotations including at least one severity level related to said symptoms of the diagnosed disorders. When the child or adolescent does not have any symptoms related to a neurodevelopmental or associated mental health disorder, this level may be zero. Preferably, healthy children and adolescents have been screened to ensure that they have no parent-reported history of major psychiatric disorders, neurological disorders, brain injuries, and other medical conditions that could affect their brain development.Children born prematurely (<36 weeks gestational age) with significant exposure to prenatal toxicants, including alcohol or drugs, are preferably excluded.

[0085] Advantageously, the training database can be divided into k equal parts, k being an integer greater than or equal to 1, in order to perform cross-validation (also called "A-fold validation" in English) of the machine learning model.

[0086] The machine learning model used may advantageously comprise an artificial neural network. Preferably, this artificial neural network comprises a single layer to allow simplified explainability of the score(s) obtained, i.e. to determine an estimate of the contribution of the different characteristics to obtaining a given score.

[0087] Alternatively, the machine learning model may include a decision tree (alone or in a forest), or a k-nearest neighbor algorithm. Alternatively, the machine learning model may include several of these types of models, and the output of each of the models may be aggregated (e.g., by taking the average of the outputs of each of the models).

[0088] In an advantageous embodiment, the machine learning model may be a clustering representation learning model on incomplete time series (called “Clustering Representation Learning on Incomplete time-series” or CRLI in English) having the advantage of allowing the synchronization of the different acquired biometric signals, the identification of particular biomarkers thanks to the groupings of the characteristics extracted or calculated in a non-temporal structured latent space, but not of reducing the temporal dimension.

[0089] According to another advantageous embodiment, the machine learning model may be a masked hierarchical cluster-wise contrastive learning model (MHCCL), having the advantage of allowing the identification of particular biomarkers thanks to the grouping of the characteristics extracted or calculated in a non-temporal structured latent space and the reduction of the temporal dimension but not of synchronizing the different acquired biometric signals.

[0090] In other embodiments, the machine learning model may also be a variational autoencoder (VAE) also including the construction of a latent network, or a Valence Aware Dictionary and Sentiment Reasoner (VADER) for short, which is a lexicon and a simple rule-based model for sentiment analysis.

[0091] Thus, the classification module 133 receives as input the matrix of size nxp comprising the n characteristics extracted or calculated and divided into p intervals, then transforms it into a reduced vector via a latent space which makes it possible to group or “cluster” the model’s training data.

[0092] As noted, the machine learning model used may have been previously trained on a dataset collected with child or adolescent users. For example, training data may be collected by recording the temporal biometric signals of a child or adolescent user as they interact with a game, then features are extracted from these signals, and a questionnaire administered by a specialist is used to obtain one or more scores relating to a neurodevelopmental disorder or related disorder. The model is thus trained based on the features and questionnaire scores obtained for a set of users.A non-exhaustive list of usable questionnaires is given below: “ADHD scale”, MADRS (“Montgomery-Asberg Depression Rating Scale”), SHAPS (“Smith Hamilton Pleasure Scale”), ADOS (“Autism Diagnostic Observation Schedule”), WISC (“Wechsler Intelligence Scale for Children”), CBCL (“Child Behavior Checklist”), SDQ, (“Strengths and Difficulties Questionnaire”), KSAD (“Kiddie Schedule for Affective Disorders and Schizophrenia”). Advantageously, the choice of the questionnaire(s) is made according to the symptom(s) relating to neurodevelopmental or associated mental health disorders studied. Thus, the objective is to optimize the parameters of the machine learning model so that it can predict as accurately or more accurately the score obtained from the clinical assessment traditionally carried out using the various questionnaires cited.

[0093] In addition, the machine learning model advantageously makes it possible to identify specific digital biomarkers as a composition of multiple characteristics extracted or calculated from the acquired biometric signals, such as the user's playability (including for example: all tactile events related to the game or its reactivity, its facial expressions, its shouts / sounds emitted and their particularities...), the pose of the head of the use (i.e. the orientation of its head) or the estimation of the user's gaze (i.e. the orientation of its gaze) including the detection of saccades, fixations, blinking, checking whether the user is looking at the screen, etc., or certain landmarks on their face.

[0094] In an advantageous embodiment, the machine learning model of the interface module 110 and the machine learning model of the acquisition module are common (i.e. the system 100 comprises a single machine learning model common to both modules) and in an alternative embodiment these machine learning models are different.

[0095] The interface module 110 may further comprise a display for displaying the score(s) obtained.

[0096] The system 100 may also comprise a comparison module 135 configured to compare the score(s) assigned to the user with one or more reference values. Such a comparison thus makes it possible to position the user's scores on a diagram to identify the different symptoms relating to neurodevelopmental disorders or associated mental health disorders, and improve assistance in diagnosing these said disorders, and estimate their prevalence.

[0097] The system 100 may advantageously be included in a tablet 200 (FIG. 2), provided with a touch screen 210, a front camera 220 and a microphone 230. The user 1 may interact with the tablet 200 using one or more fingers 202 when touching or sliding the screen. The tablet 200 may record a video of the user's face, including his eyes or gaze 2. The tablet 200 may also record an audio signal using the microphone 230, containing the noises and speech 3 emitted by the user. The tablet 200 may also record a signal representing the interactions between a member of the user, for example his hand or finger 4, and the touch screen 210. The tablet 200 may then process (i.e.extract the characteristics and perform the assignment of scores) the signals recorded locally or send them to a remote unit which will be responsible for carrying out the processing, to then present a report to a practitioner who can make a diagnosis and provide the appropriate therapeutic response. Thus, the system 100 allows the combination of cognitive, emotional, behavioral and cerebral measurements, offering a tool to assist. easy-to-use, comprehensive, and widely deployable diagnosis of neurodevelopmental disorders and / or associated health disorders.

[0098] Process

[0099] A computer-implemented method for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user will now be described in connection with the flowchart of Figure 3.

[0100] First, the method comprises a step E1 of presenting to the user an interactive program, preferably a game.

[0101] Then, the method comprises a step E2 of simultaneously acquiring a plurality of temporal biometric signals resulting from the user's interaction with the interactive program. These signals can be stored in a memory locally or transmitted to a remote memory, for example in a remote server.

[0102] Then, the method comprises a step E3 of extracting or calculating, for each temporal biometric signal, at least one characteristic relevant for the evaluation of a neurodevelopmental or mental health disorder associated with the child or adolescent.

[0103] The method finally comprises a step E4 during which, using the extracted or calculated characteristics and a machine learning model previously trained on a set of annotated data collected on child users, one or more scores relating to one or more neurodevelopmental disorders are assigned to the user.

[0104] This or these scores can finally be compared, in a step E5, to one or more reference values, making it possible to construct a network of symptoms specific to a user.

[0105] Example

[0106] A game is presented to the user on a tablet that allows them to be stimulated, and a video signal is simultaneously recorded from a front camera. From this video signal (sequence of images) containing the user's face, a machine learning algorithm trained on children's faces can extract a score on several emotions, in particular 7 emotions including "fear". The machine learning algorithm can compare the position of several marks on the user's face to deduce scores for each emotion, for example by using Facial Action Units. As an output of this algorithm, we obtain a vector (feature vector) for each image of the sequence of images containing a score for each of the emotions tested.Then, a second algorithm trained on the basis of video signals and questionnaires relating to attention deficit disorder (“ADHD scale”) and sleep disorder (“PSQI” Pittsbugh Sleep Quality Index), makes it possible, from the set of vectors generated, to obtain a score on the “ADHD scale” or on the “PSQI” scale. The second algorithm can advantageously be a recurrent neural network (“RNN”).

[0107] Figure 4A shows the prediction of attention deficit disorder (ADHD) scores based on the score for the emotion “fear” for four different users. Each point corresponds to the score calculated on the ADHD scale for one frame in each user’s video sequence (each video contains 5000 frames in the example). A good correlation is observed for all four users tested.

[0108] Figure 4B shows the prediction of sleep disturbance scores (“PSQI”) as a function of the score obtained for the emotion “fear” for the same 4 users. A good correlation is also observed for all users tested.

Claims

ZI CLAIMS 1. System (100) for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising: - an interface module (110) configured to present to the user an interactive program, preferably the program is a game, - an acquisition module (120) configured to simultaneously receive a plurality of temporal biometric signals (121) resulting from the interaction of the user with the interface module (110) when the user interacts with said program, - an extraction module (131) configured to extract or calculate from each temporal biometric signal, at least one characteristic relevant for the evaluation of at least one neurodevelopmental or mental health disorder associated in the child or adolescent, and - a classification module (133) configured to assign to the user one or more scores relating to one or more symptoms relating to one or more associated neurodevelopmental or mental health disorders, from the characteristics extracted or calculated by the extraction module (131) and a machine learning model previously trained on a set of annotated training data collected on child or adolescent users having been annotated to determine the presence or absence of at least one symptom relating to at least one neurodevelopmental disorder and / or at least one associated mental health disorder, the annotated data comprising at least one degree of severity relating to said symptoms of the diagnosed disorders.

2. System (100) according to claim 1, wherein the neurodevelopmental disorders are chosen from the following: attention deficit disorder with or without hyperactivity, communication disorders, specific learning disorders, and / or motor disorders.

3. System (100) according to any one of the preceding claims, wherein the associated mental health disorders are chosen from the following: depression, anxiety, sleep disorders, social interaction disorders, and / or emotional dysregulation disorders.

4. System (100) according to any one of the preceding claims, wherein the plurality of temporal biometric signals (121) is selected from: a video signal comprising tracking of the user's eyes or face, a position signal of a limb of the user, an audio signal, an electroencephalogram, and / or a physiological signal.

5. System (100) according to any one of the preceding claims, wherein the acquisition module (120) comprises a camera and is configured to receive a video signal comprising the tracking of the user's eyes, and a characteristic extracted from the video signal is chosen from the following: projection of the gaze on a surface, heat map of the projection of the gaze on a surface, emotional profile of the user, facial action units, blinking of the user's eyes, acceleration of the projection of the user's gaze, angle formed by the user's gaze with respect to the perpendicular to a surface, saccades in the trajectory of the gaze, the number of fixations of the gaze on a specific item, the time spent looking at a specific item.

6. System (100) according to any one of the preceding claims, wherein the acquisition module (120) comprises a touch surface or a specific acquisition device and is configured to receive a position signal of at least one finger or hand of the user, and a characteristic extracted from the position signal is chosen from: trajectory of the finger or hand, speed of the finger or hand, acceleration of the finger or hand, pressure of the finger or hand, heat map of the position of the finger or hand, number of touches of the screen by the user.

7. System (100) according to any one of the preceding claims, wherein the acquisition module (120) comprises a microphone and is configured to receive an audio signal, and wherein the extraction module is configured to extract a feature extracted from the audio signal is chosen from: a transcription of the user's speech, the variations in the frequency of the user's voice, the acoustic characteristics of the speech signal such as: jitter, shimmer, tremor, harmonic-to-noise ratio, frequency disturbance ratio, amplitude disturbance ratio, peak slope, average of the 1st harmonic (Fl), average of the 2nd harmonic (F2), variability of Fl, variability of F2, range of Fl, vowel space, predictive linear coding coefficients, frequency-Mel cepstral coefficients (MFCC), average of the fundamental frequency (FO), variability of FO, range of FO, average of the intensity, variability of the intensity,energy variance, energy velocity, maximum phonation time, speech rate, articulatory rate, average utterance duration, average pause duration, pause rate, total pauses., 8. System (100) according to any one of the preceding claims, wherein the acquisition module (120) comprises at least one of: a sensor for measuring a physiological signal, a touch screen, a mouse, a microphone, a camera, a keyboard, an accelerometer, a gyroscope.

9. System (100) according to any one of the preceding claims, being included in a tablet (200) equipped with a touch screen (210), a camera (220), and a microphone (230).

10. System (100) according to any one of the preceding claims, in which the extraction module (131) uses at least one machine learning model previously trained on a set of annotated signals collected from child or adolescent users.

11. System according to any one of the preceding claims, wherein the machine learning model of the classification module (133) comprises at least one artificial neural network.

12. System according to the preceding claim, in which the artificial neural network comprises a single layer.

13. System according to any one of the preceding claims, comprising a comparison module (135), configured to compare the score(s) attributed to the user with one or more reference values.

14. A system according to any preceding claim, wherein the interface module (110) comprises a display for displaying the score(s) obtained to the user or an authorized practitioner.

15. A computer-implemented method for assisting in the diagnosis of neurodevelopmental disorders and / or associated mental health disorders in a child or adolescent user, comprising at least the following steps: presenting (E1) to the user an interactive program, preferably a game, simultaneously acquiring (E2) a plurality of temporal biometric signals resulting from the user's interaction with the interactive program, extracting or calculating (E3), for each temporal biometric signal, at least one characteristic relevant for the assessment of a symptom relating to an associated neurodevelopmental or mental health disorder in the child or adolescent, and assigning (E4) to the user one or more scores relating to one or more symptoms relating to one or more associated neurodevelopmental or mental health disorders defining a symptom network of the user,using the extracted or computed features and a machine learning model previously trained on an annotated training dataset collected from child users having, been annotated to determine the presence or absence of at least one symptom relating to at least one neurodevelopmental disorder and / or at least one associated mental health disorder, the annotated data including at least one severity level relating to said symptoms relating to the diagnosed disorders.

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