Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
  • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
  • A61B5/4088—Diagnosing of monitoring cognitive diseases, e.g. Alzheimer, prion diseases or dementia
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
  • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training

Definitions

• DSM Diagnostic and Statistical Manual
• Types of neurodevelopmental disorders include, but are not limited to, Intellectual Disabilities, Intellectual Disability (Intellectual Developmental Disorder),
• developmental disorders such as Rett Syndrome, dyslexia, dyspraxia, dyscalculia, dysgraphia, auditory processing disorder, language processing disorders, non-verbal learning disability, visual perceptual and visual motor deficit, Tourette syndrome, Turner syndrome, Fragile X, Neurofibromatosis, Down Syndrome, Klinefelter syndrome, Phenylketonuria, Seizure disorders, Prader-Willi syndrome, Williams syndrome etc.
• An emerging method of identifying a neurodevelopmental disorder is to detect a digital cognitive biomarker(s) resulting from the aggregation and analysis of performance metrics.
• Biomarkers can indicate or predict specific conditions or diseases, as well as track their progression or response to treatment.
• Digital cognitive biomarkers arise from the non-invasive collection of behavioral performance data. Several such biomarkers may be combined to create a cognitive signature for the individual and/or specific conditions.
• Biomarkers in the medical field are increasingly beneficial for the detection of disease-related information.
• a biomarker provides a measurable indicator of a biological state or condition, and can be used to examine bodily functions and/or indicate onset of disease.
• the present invention also provides a device comprising a processor and a user interface, wherein the processor is configured to generate, when the device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain or a cognitive assessment task associated with a cognitive domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task or cognitive assessment task; to generate, when the device is in a second mode, a training task associated with the neurodevelopmental domain or cognitive domain on the user interface; and to change the first device from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.
• the present invention provides a computer-implemented method to identify and/or measure a cognitive biomarker indicative of a neurodevelopmental disorder or cognitive deficit in a subject, comprising: obtaining performance metric data from a neurodevelopmental assessment or a cognitive assessment task task performed by a subject on a first user interface; obtaining contextual data from an input provided by a third party on a second user interface; receiving, at a processor, the performance metric data and the contextual data; performing a statistical analysis on the performance metric data in combination with the contextual data; and identifying and/or measuring the cognitive biomarker based on the statistical analysis.
• the present invention also provides a system for identifying and/or measuring a cognitive biomarker indicative of a neurodevelopmental disorder or a cognitive deficit in a subject, the system comprising an assessment device comprising a first processor and a first user interface; a secondary device associated with the assessment device, the secondary device comprising a second processor and a second user interface; and an external memory associated with the first processor and the second processor, and configured to store an aggregated set of data received in relation to a user of the assessment device; wherein the first processor is configured to: cause the first user interface to display an assessment task; obtain performance metric data associated with the assessment task; and store the performance metric data in the external memory; and wherein the second processor is configured to: obtain contextual data input on the second user interface, and store the contextual data in the external memory.
• FIG. 1 is a schematic of a first device with a user interface and a processor.
• FIG. 4 is a schematic of an external server, comprising a processor and a memory.
• FIG. 9 is another exemplary graphical user interface of a training task.
• FIG. 13 is an exemplary embodiment of a method for performing the invention.
• FIG. 14 is an exemplary graphical user interface of an assessment task shown on a first user interface.
• embodiments disclosed herein are directed generally to methods and devices for generating a neurodevelopmental assessment task associated with a neurodevelopmental domain, a cognitive training task associated with the neurodevelopmental domain, and switching a mode of a device from a first mode associated with the neurodevelopmental assessment task to a second mode associated with the cognitive training task.
• the neurodevelopmental assessment task allows the system to detect differences in performance within the four neuro-developmental domains. These aggregated performances (either in terms of absolute number or over a defined period of time) may then be translated into a biomarker for delay in motor development, for example. This can then be tracked longitudinally over time as a disease process evolves or a new medicine is introduced. Biomarkers can be obtained for each of the domains (i.e. motor, language, cognitive, and social/personal.
• the biomarker can be narrow and indicative of performance in one of the four neurodevelopmental domains.
• the biomarker can be more broad (taking into account performance in multiple neurodevelopmental domains) and indicative of a specific neurodevelopmental disorder.
• a neurodevelopmental assessment task may be a single task presented to a subject, or it may be a sequence of tasks presented.
• each individual neurodevelopmental assessment task is principally designed to assess a single facet of cognitive development, otherwise known as a neurodevelopmental domain.
• a task may be designed to test for language skill in a subject.
• the method for evaluating and treating one or more neurodevelopmental delays in a subject comprises: generating, when a device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task; generating, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.
• the device processor may be configured to communicate to an external processor and/or an external memory. In this way, the processing of the data can occur in either an external server or a different computer. Such a configuration may be beneficial to avoid the need for local processing on the device, which may be a computationally demanding process.
• the external processor may have the same computational ability as the device processor, and may include a corresponding memory to store the above parameters.
• a “processor” is described, it is understood that the processor may be either the device processor or an external processor.
• a Flanker task is beneficial for testing the ability for a user to inhibit irrelevant competing responses to a nonverbal stimulus. As a result, this task provides performance metric data which may be useful for assessing users who demonstrate a certain level of attention deficit (one marker for ADHD, attention deficit and hyperactivity disorder).
• the training task GUI 1100 may include highlighting the relevant element 1101 in the center of the row.
• the distractions have been made more apparent, so as to highlight the distinctions in the colors. This visual level of guidance allows a user to more easily distinguish between the correct option 1102a and the incorrect option 1102b.
• the neurodevelopmental assessment task may have a time limit associated with completing the task, whilst the time limit is removed in the training task.
• the neurodevelopmental assessment task may provide no auditory, visual, or tactile feedback of any kind during the task.
• a cognitive training task may provide these kinds of feedback in real-time, or may provide suggestions and/or guidance to a subject to complete the task.
• the feedback may be positive or negative - that is, the feedback may be positive, i.e. suited to either provide rewards and guide the user towards a successful result, or negative, i.e. feedback designed to dissuade the user away from a certain, incorrect solution.
• the device may be configured, by the processor, to operate in a hybrid mode.
• a hybrid mode refers to a mixed approach for delivering assessment and training tasks.
• the processor may recognize individual neurodevelopmental delays orcognitive deficits that correspond to performance in the assessment tasks, and for which a subject needs additional training. The processor may then present a training task instead of the corresponding neurodevelopmental assessment task when necessary.
• the performance metric data associated with the neurodevelopmental assessment task comprises at least one parameter.
• at least one parameter comprises one or more of the following: the time taken for the subject to complete the neurodevelopmental assessment task; whether the neurodevelopmental assessment task was completed successfully; the number of times the neurodevelopmental assessment task was completed and was not completed successfully; and feedback from the user interface.
• Additional skills which may be tested include, but are not limited to numeracy. Numeracy skills may be evaluated through a “more or less” style task.
• neurodevelopmental assessment tasks can be used to isolate neurodevelopmental assessment tasks for which a patient performs at a different level of competence. This is beneficial because typical neurodevelopmental assessment profiles comprise a wide variety of parameters which are tested. These parameters of cognitive performance includes one or more of, but not limited to, visual-spatial ability, verbal comprehension, processing speed, working memory and fluid reasoning. A patient with a neurodevelopmental disorder may require no further training in one or more of these areas, but may require more training in others.
• the step of generating the training task comprises generating a training task which is associated with the identified category of the neurodevelopmental assessment task.
• the threshold parameter may comprise a predetermined number of allowable attempts, an allowable time to complete a task, an acceptable score in the task.
• the threshold parameter may also vary, depending on input from the user, which is stored in memory. Such input may include information including, but not limited to, the age, the gender, and the neurodevelopmental disorder of the subject using the device.
• a “score” of a task is referred to, this can be understood to mean an average pass percentage (%) for a patient having certain criteria.
• the “score” of a task could refer to whether or not the performance in the task exceeded a ‘ceiling’ of the task (a maximum expected value for a user of a certain age group), or whether or not the performance in the task fell below a ‘floor’ of the task (a minimum expected value for a user of a certain age group).
• the criteria refers to one of the stored input values.
• a threshold parameter may be chosen from a predetermined value associated with the input value.
• the threshold parameter may represent an acceptable score, which is associated with the typical result for a 5-year old. Therefore, for a 5-year old taking the neurodevelopmental assessment task, a score below the threshold parameter may trigger the device switching into a training mode.
• the threshold parameter is adaptive, in that it can represent a typical value of similar subjects to the neurodevelopmental assessment task, including but not limited to expected success rates based on the average result from a set of historical data of the neurodevelopmental assessment task. This average result may be determined based, in part, upon the contextual data, by comparing the patient against other people who underwent the same neurodevelopmental assessment test with one or more data points of the contextual data (e.g. age) in common.
• the estimated parameter may be a demographic or physiological parameter.
• a physiological parameter includes, but is not limited to, age of the patient. It may further include any of the listed contextual data referred to above.
• the processor may estimate an age of the subject, based upon an individual neurodevelopmental assessment task, a series of neurodevelopmental assessment tasks, or a history of data related to one or more neurodevelopmental assessment tasks.
• the processor is configured to compare this estimated age against the actual age of the subject. If the difference is above a threshold, which relates to an acceptable deviation from the predetermined parameter, then the processor can switch the neurodevelopmental assessment task to a training task.
• the levels of difficulty of a task may be iteratively adjusted (for example, the parameters stored in memory location 501-la may be adjusted as the user of the device continues to use the program), or the difficulty levels may be stored separately in the memory 103, 120.
• the processor 102 is configured to first select a task, then check the difficulty levels stored in memory (based upon previous performance), and then provide an appropriate assessment task on the user interface 101.
• one of the consequences of a successful, or unsuccessful, result in the neurodevelopmental assessment tasks may be to adjust the difficulty or progression of the neurodevelopmental assessment tasks presented to the subject.
• the method further comprises instructing, when the device is in the second mode, the first device to change from the second mode to the first mode only after an authorized override is received, and generating the further neurodevelopmental assessment task on the user interface of the device.
• a processor in the device may be configured to prevent the device from presenting a further neurodevelopmental assessment task in the first, assessment mode once the processor has changed the device mode to the second, training mode until the processor receives an instruction to allow the further neurodevelopmental assessment task to be presented to a user and/or for the device to switch to the first, assessment mode. It may be said that switching modes only after an authorized override refers to re-enabling the assessment mode in the device, for either all neurodevelopmental assessment tasks or a subset of neurodevelopmental assessment tasks.
• a processor may be configured to determine when one or more of the measured parameters of the performance metric data meets or exceeds an expected value of success rate for a specific task associated with the age group of the patient.
• the processor may generate a predetermined number of training tasks in a set before providing an authorized override.
• the processor may record the time at which the device was switched from the assessment mode to the training mode, and only permit the device to operate in the training for a certain period of time.
• One benefit of the above method is that improved training outcomes can be realized, by improving access to large amounts of data in real-time and ensuring patient compliance with training routines. What’s more, by providing specific training tasks only when necessary (and optionally providing customized training tasks based on the patient), a more individualised approach to cognitive development can be realized, and specific interventions can be pinpointed based on patient outcomes and specific neurodevelopmental delays or cognitive deficits.
• Another benefit of the method of the present invention is that it provides improved isolation of factors which are indicative of cognitive performance for a specific person, as well as providing accurate assessments of a patient to generate an improved predictive trajectory of cognitive development.
• the subsequent neurodevelopmental assessment task generated may be identical to the task which triggered the switch to the training mode.
• the processor of the device may be configured to store in memory the last task which was presented before the device was switched to the assessment mode.
• a device comprising a processor and a user interface.
• the processor is configured to generate, when the device is in a first mode, a neurodevelopmental assessment task associated with a neurodevelopmental domain on a user interface of the device and receiving performance metric data associated with the neurodevelopmental assessment task; generate, when the device is in a second mode, a training task associated with the neurodevelopmental domain on the user interface; and instructing the first device to change from the first mode to the second mode, based on the performance metric data received whilst the device is in the first mode.
• the processor of the device is further configured to perform any of the method steps described above.
• Cognitive biomarkers for transient deficits may be useful to assess cognitive performance in relation to time-limited events/tests, such as admission tests e.g. to university, military, police, driving skills, surgical skills or any performance metric which may be affected by external/environmental factors (that can be captured through contextual data)
• the biomarker can be narrow and indicative of performance in one of the four neurodevelopmental domains.
• the biomarker can be more broad (taking into account performance in multiple neurodevelopmental domains) and indicative of a specific neurodevelopmental disorder.
• the third party can either self-report to provide contextual data about the third party, and/or they can report contextual data about the subject.
• a system may include a first device 100 (substantially the same as the device shown in FIG. 11) and a second device.
• the second device 110 includes a second user interface 111 in communication with a second device processor 112.
• the second device processor 112 may also be configured to communicate to the external memory 120, as would be understood by a skilled person.
• a separate communications module (not shown) may be implemented in the second device to communicate data from the second device processor to the external memory in a specific data format.
• the communications link may be either wireless (Wi-fi, Ethernet, Zigbee, Bluetooth, etc.), or it may be a wired connection.
• the first device and, if present, the second device each comprise a device processor, a clock in communication with the device processor, and a memory in communication with the device processor.
• contextual data may be requested or input once and then stored in memory of the relevant device.
• the age of the subject may be input only once and stored in memory.
• the contextual data may be obtained at intermittent or regular intervals.
• the parent or guardian can enter their observations about their child, i.e. the user of the first device. As shown in the figure, they can enter data on how Max slept, his mood that day, his caffeine and sugar intake, and the intensity of his physical exercise.
• the processor configured to receive the performance metric data and contextual data may be one of the device processors described above. That is, the method may comprise receiving at one of the device processors the performance metric data and the contextual data and performing the statistical analysis locally on one of the devices.
• the processor may be an external processor in communication with the device processors in the first and second device processors.
• the processor may be a processor in an external server.
• Such a configuration may be beneficial to avoid the need for local processing on the first or second devices, which may be a computationally demanding process.
• receiving data at the processor refers to obtaining data from a plurality of different sources directly, or indirectly, i.e. from a memory in communication with the processor which stores previously received data.
• a statistical analysis refers to the collection, organization, analysis, interpretation of data.
• the performance metric data and the contextual data are the inputs to the analysis, which is performed on the processor, and the output of the analysis is an identification and/or measurement of the cognitive biomarker.
• a subset of the performance metric data may be compared to a corresponding subset of the contextual data.
• the performance metric data may comprise data points, each with an associated time stamp.
• the contextual data may also have corresponding time stamps.
• the processor may perform an analysis on a certain time range of the performance metric data, or it may perform an analysis on the entirety of the available data in the performance metric data set.
• the method also comprises identifying and/or measuring the cognitive biomarker based on the statistical analysis. As described above, the resulting, novel cognitive biomarker is improved over a generic cognitive biomarker which does not consider any contextual data.
• a data point of the performance metric data may be associated with the closest contextual data point (e.g. the point having the shortest time between the data received).
• exemplary metrics which may be captured during the 3, 6, and 9 box task version include, but are not limited to, the following: NumberOfTimesIncorrectlySearched (the number of incorrect taps on one plane), IncorrectLocationsSearched (an array of all locations tapped more than once).
• the duration of the trial (trialDuration), difficulty of the trial (trialDifficulty), the number of times the task has been attempted (trialNumber) and whether or not the trial was successful (trialSuccessful) are also monitored in this embodiment.
• the above exemplary metrics may all be considered performance metric data, either taken alone or in combination with other data. An evaluation of these metrics against a baseline will generate a profile of how well a user is able to inhibit irrelevant, competing stimuli.
• Data received at the processor may be stored in a single memory location, or in two or more separate memory locations. In other words, the memory can store all data (including both performance metric data and contextual data) in sequential arrangement in a single location. Alternatively, the performance metric data can be stored in a first location in the memory, and the contextual data can be stored in a second location in the memory. Further techniques, such as load balancing and varying server structures can also be utilized such that data is stored and manipulated across different places on the server or servers.
• the threshold may be stored in a memory connected to the processor, and it may be a predetermined threshold. It may also be an adjustable threshold, which can be adjusted by a third party who has access to control the parameters used by the processor.
• the first and second time stamps are received from the clock in the first or second device, from where the respective contextual data and/or the performance metric data was obtained.
• the first and second time stamps may represent the time at which the performance metric and/or contextual data are received by the processor.
• the processor may be in communication with a clock, which provides the processor with a timestamp for use in processing and analyzing the data. This may be useful where data is sent automatically from data sources without clocks, and is received at the processor without an appropriate time stamp.
• the processor can also be configured to identify another subject in the historical data whose cognitive biomarker and/or contextual data matches most closely to the subject in question.
• An output of the analysis to determine which subject in the population data is the closest to the subject in question is that either improved prediction of a neurodevelopmental disorder or a cognitive deficit can be achieved or the two subjects can be monitored against each other and tracked. If one subject receives different treatment, or reacts more positively or more negatively to a certain contextual data point, tracking the two subjects may further help improve cognitive therapies and treatments for both subjects.
• the statistical analysis described above may comprise one or more of the following: a cross-correlation measurement between one or more parameters of the stored contextual data and one or more of the stored performance metrics or a statistical regression analysis between the input parameter and the performance metric parameter.
• a statistical regression analysis may further be useful for determining a functional relationship between an input parameter of the contextual data and a performance metric parameter.
• the processor might compare a slope of the improvement in cognitive performance against input sleep data. This may be useful to determine how important variations in sleep are in detecting changes in the slope of a cognitive development trajectory. For example, such a calculation might help determine that losing a single hour of sleep per night has little to no effect on a specific subject, whilst 3-4 hours of lost sleep has a noticeable effect in cognitive performance.
• the performance metric data may comprise a parameter measured in relation to the neurodevelopmental assessment task.
• This parameter measured corresponds to one or more of the following parameters: time taken to complete the neurodevelopmental assessment task and/or whether or not the assessment task was successfully completed. Any combination of this data can be received by the processor.

Landscapes

• Health & Medical Sciences (AREA)
• Engineering & Computer Science (AREA)
• Life Sciences & Earth Sciences (AREA)
• Public Health (AREA)
• Medical Informatics (AREA)
• Biomedical Technology (AREA)
• General Health & Medical Sciences (AREA)
• Developmental Disabilities (AREA)
• Psychology (AREA)
• Psychiatry (AREA)
• Hospice & Palliative Care (AREA)
• Neurology (AREA)
• Pathology (AREA)
• Child & Adolescent Psychology (AREA)
• Heart & Thoracic Surgery (AREA)
• Veterinary Medicine (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Animal Behavior & Ethology (AREA)
• Physics & Mathematics (AREA)
• Social Psychology (AREA)
• Biophysics (AREA)
• Primary Health Care (AREA)
• Epidemiology (AREA)
• Physiology (AREA)
• Data Mining & Analysis (AREA)
• Databases & Information Systems (AREA)
• Neurosurgery (AREA)
• Educational Technology (AREA)
• Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Applications Claiming Priority (3)

Publications (1)

Family

ID=74732882

Family Applications (1)

Country Status (2)

Families Citing this family (2)

Family Cites Families (3)

• 2021
  • 2021-02-19 EP EP21707919.3A patent/EP4107750A1/de active Pending
  • 2021-02-19 WO PCT/EP2021/054214 patent/WO2021165498A1/en unknown

Also Published As

Similar Documents

Legal Events