Panoramic ai-based screening evaluation method for correlation between hearing and neurodevelopment in children

Abstract

This invention relates to the field of medical information technology, and in particular to a method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI. The method includes: deploying an arc-shaped animal-shaped microphone array to guide children in providing feedback on the location of sound sources; running a multi-mode sound control module to perform hearing threshold testing, sound source localization, word recognition, and multi-step instruction testing; using a 4K panoramic camera and skeletal key point recognition algorithm to determine effective responses such as head turning, and integrating facial, voiceprint, and skeletal features for personalized group tracking; achieving group hearing screening, and assessing neurodevelopmental risks based on a conformity index model and multi-dimensional developmental correlation indicators; integrating a background noise module to simulate a real learning environment; and automatically generating assessment reports and gamified training programs. This application, through the above technical solution, can achieve efficient, objective, and multi-dimensional integrated screening and intervention for hearing and neurodevelopment in children.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, specifically to a method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI. Background Technology

[0002] With the popularization of early childhood development intervention concepts, hearing screening, as a key component of neurodevelopmental assessment, is receiving increasing attention from the medical and educational fields. Hearing is not only the foundation of language acquisition and cognitive development, but also closely related to attention, executive function, and social skills. However, current child hearing health management systems are still limited to clinical diagnostic scenarios, lacking efficient, objective, and multidimensional screening methods for large groups such as schools, making it difficult to achieve early identification and intervention for mild hearing impairment and hearing-related neurodevelopmental risks. Especially in children aged 3 to 18, key indicators such as auditory processing ability, sound source localization performance, and complex instruction comprehension urgently require a comprehensive assessment paradigm that integrates behavioral observation, environmental simulation, and intelligent analysis.

[0003] Among them, the panoramic AI-based screening and assessment method for the correlation between children's hearing and neurodevelopment aims to collect auditory response and behavioral data simultaneously in real or simulated school environments through non-invasive, gamified interactive design, and to establish individualized developmental risk profiles. This method not only focuses on traditional hearing threshold indicators, but also emphasizes the dynamic assessment of children's auditory attention, speech comprehension, and executive functions under conditions of group interaction and noise interference, thereby constructing a closed-loop path from "screening" to "intervention".

[0004] Currently, comprehensive hearing health screening for children is not yet implemented, and existing technologies face multiple bottlenecks in school settings: routine hearing tests rely on isolated equipment for single individuals, which is time-consuming and requires professional operation, failing to meet the needs of batch screening in classes; subjective feedback mechanisms (such as raising hands and responding) are easily affected by children's cooperation and lack objective criteria for judging actions; assessment dimensions are limited to pure tone hearing thresholds, neglecting higher-order auditory cognitive abilities such as sound source localization and multi-step command execution; more importantly, existing solutions have not established a quantitative correlation model between auditory performance and neurodevelopmental abnormalities (such as autism spectrum disorder and attention deficit), nor are they able to accurately distinguish individual responses and eliminate abnormal interference in group tests. In addition, screening results are disconnected from subsequent educational interventions, lacking the ability to automatically generate personalized training programs based on assessment data. Furthermore, children with unilateral hearing loss often go unnoticed by parents or schools in daily life unless they actively respond. Therefore, there is an urgent need for a comprehensive screening and assessment system that integrates sound source array control, multimodal sound stimulation, panoramic AI behavior recognition, group neurodevelopmental risk modeling, and intelligent training recommendations to overcome the technological barriers to hearing health management in schools. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a screening and assessment method for the correlation between hearing and neurodevelopment in children based on panoramic AI. By integrating a sound source generating device, multi-mode sound control, panoramic AI recognition, group behavior analysis and neurodevelopment determination, background noise simulation, and intelligent software design, a complete screening and assessment system for the correlation between hearing and neurodevelopment in children is constructed.

[0006] To achieve the above objectives, this invention provides a method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI, comprising: Step 1: Deploy the sound source generating device, using an arc-shaped animal-shaped microphone array. Configuring plush animal-shaped microphones at equal intervals with a radiation angle greater than 90 degrees, each microphone has a built-in independent audio output unit. The animal shapes guide children to point out the animal's location, improving test cooperation. Set the interval between test sounds to 2 to 3 seconds. Before each round of testing, display a cartoon countdown on a visual screen and simultaneously play auditory instructions to ensure children's concentration. Generate short pure-tone signals that meet pure-tone hearing threshold testing standards and international audiometry standards, covering frequencies from 250 Hz to 8000 Hz, meeting clinical-grade hearing screening needs. Step 2: Run the multi-mode sound control module and execute four test modes for different assessment objectives. Mode 1 is the physiological hearing threshold test, which outputs pure tones at frequencies of 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz, and 8000 Hz sequentially through the short pure tone generation unit, with an intensity range from 10 dB to 60 dB hearing level, to detect hearing thresholds and identify mild or unilateral hearing loss. Mode 2 is the fun location recognition test, which randomly selects sounds from the animal sound library and plays them through a specific animal microphone, guiding children to point out the corresponding animals, assessing sound source localization ability and bilateral hearing symmetry. Mode 3 is the word recognition test, which plays words using a children's common vocabulary library, and children click on the corresponding images on the touchscreen or place stickers under the corresponding animals, assessing speech comprehension and auditory-visual association ability. Mode 4 is the multi-step instruction test, which calls up the campus scene instruction library to play multi-step instructions, and children complete the corresponding actions, assessing auditory attention and executive function. Step 3: Activate the panoramic AI recognition module, using a 4K resolution wide-angle panoramic camera as the panoramic monitoring hardware, installed 2.5 to 3 meters in front of the sound source array, covering the area where 5 to 8 children are being tested simultaneously, ensuring no blind spots; execute AI action judgment logic based on skeletal key point recognition algorithm, focusing on monitoring head turning actions, setting a head turning angle greater than or equal to 30 degrees and a reaction latency less than or equal to 5 seconds as a valid reaction, simultaneously recognizing auxiliary actions such as raising hands, making sounds, and turning around and excluding false triggers; implement group personalized recognition, integrating facial recognition, voiceprint acquisition and skeletal feature triple identity verification, and achieve individual tracking and accurate data allocation through the establishment of an initial feature library and cross-frame matching; Step 4: Execute the group behavior analysis and neurodevelopment assessment module. Based on the conformity index model, calculate the ratio of individual response latency to the group's first response latency and the group's average response latency. When the index is greater than 0.7, it indicates that the individual's response is significantly lagging behind the group, which may indicate neurodevelopmental risks or auditory processing disorders. Add data preprocessing, outlier identification, and group verification data validation steps. By filtering outlier data and dynamically adjusting the calculation benchmark, ensure that the conformity index truly reflects the individual's response characteristics in the group. Combine multiple developmental correlation indicators such as positioning accuracy, command completion rate, and response stability to construct an auditory neurodevelopment correlation assessment system and generate risk levels. Step 5: Integrate the background noise module to simulate common campus background noise. The intensity adjustment range is 35 decibels to 55 decibels. During the test, select the no noise or campus noise mode to assess the child's auditory concentration and anti-interference ability in the learning environment. Step 6: Run the software design module to automatically collect hearing threshold data, localization accuracy, response latency, instruction completion rate, and conformity index, and generate a standardized dataset; generate an auditory health and neurodevelopment assessment report for each individual, mark abnormal items and provide suggestions; and automatically generate gamified training programs based on the assessment results, including attention training, instruction execution training, and neurodevelopment intervention.

[0007] Preferably, in step 1, the arc-shaped animal-shaped microphone array adopts the shapes of plush animals such as lions, tigers, puppies, and ducklings. Each microphone has a built-in independent audio output unit that supports high-fidelity audio playback, ensuring the clarity and consistency of the sound signal. The visual screen uses a high-brightness LCD display, and the cartoon countdown animation design conforms to the cognitive characteristics of young children. The auditory instruction voice has been professionally recorded and optimized, with a moderate speaking speed and a friendly tone to maximize the attraction and maintenance of children's attention. The generation of short pure tone signals strictly follows international audiometry standards, with a frequency accuracy error of less than or equal to 2%, and an intensity control accuracy of ±1 dB hearing level, ensuring the clinical validity and comparability of the test results.

[0008] Preferably, in step 2, the multi-mode sound control module's mode 1 short pure tone generation unit uses digital signal processing technology to generate pure tone signals of precise frequencies through a direct digital synthesizer. The test duration for each frequency point is set to 1 to 2 seconds, and the intensity change step is 5 dB hearing level. The minimum audible intensity at each frequency point is automatically recorded as the hearing threshold. Mode 2 animal sound library contains a variety of common animal sounds, with the duration of each sound controlled between 0.5 and 1 second. The sound pressure level is uniformly calibrated to 50 dB hearing level, and the playback order uses a pseudo-random algorithm to avoid children's sequential memorization affecting the test accuracy. Mode 3 children's commonly used word library is selected from high-frequency children's vocabulary. Each word has clear pronunciation and simple syllables. The image library corresponds one-to-one with the words, and the touch screen response time is less than 100 milliseconds to ensure the smoothness of the test process. Mode 4 campus scene instruction library designs multi-step complex instructions. The instruction length gradually increases from two steps to five steps, and the content involves multiple attributes such as color, shape, and position. The system records the accuracy and time taken for children to complete the instructions as a key indicator for evaluating the performance function.

[0009] Preferably, in step 3, the panoramic monitoring hardware uses a 4K resolution wide-angle lens with a horizontal viewing angle of ≥180 degrees and a vertical viewing angle of ≥120 degrees, a frame rate of ≥30 frames per second, and is equipped with a high-performance image sensor to ensure that clear details of the child's movements can still be captured under indoor lighting conditions; the skeletal key point recognition algorithm is based on a deep learning model to extract the coordinates of 25 key points of the human body in real time; the head turning angle is calculated based on the spatial vectors of the key points of the eyebrows and both ears; the reaction latency is calculated from the start of the sound playback to the moment when the head turning angle first reaches 30 degrees; and the auxiliary action recognition excludes non-target action interference by setting thresholds for action amplitude and duration.

[0010] Preferably, the initial feature library establishment process in the personalized group identification includes: extracting a 128-dimensional feature vector of each child's face using a face detection algorithm, and assigning an independent identity identifier to each detected face; face feature extraction and lip shape detection are performed simultaneously. When the lip shape is determined to be vocal and the confidence level is greater than 0.8, the millisecond-level timestamp of the current frame is recorded as the time anchor point for voiceprint extraction. The corresponding segment is extracted from the audio stream and a 39-dimensional voiceprint feature vector is extracted and directly associated with the identity identifier locked by face recognition in the current frame; in each frame, face detection outputs the bounding box coordinates of the face region, and skeletal feature extraction outputs the coordinates of the pose key points simultaneously. By calculating the spatial overlap between the head skeletal key points and the face bounding box, when the overlap is greater than 0.8, the face features and skeletal features in the same spatial region are bound to the same identity identifier; voiceprint features and skeletal features are associated through the bridge formed by face features, and finally, a robust initial feature library containing face, voiceprint, and skeletal features is constructed for each identity identifier.

[0011] Preferably, in the personalized group identification, to effectively extract high-confidence voiceprint features from the mixed voices of multiple children, the straight-line distance between the target identity identifier and each microphone is calculated based on the spatial coordinates of the target identity identifier in the current frame, and the two microphones with the closest distance are selected, and the audio data of these two microphones are used preferentially; beamforming technology is used to suppress non-target direction noise, and the consistency between audio and lip movements is verified by combining a speech lip-syncing algorithm; a multi-segment feature aggregation method is used to perform a weighted average of the voiceprint features of continuous audio frames to improve the robustness and discriminativeness of the voiceprint features.

[0012] Preferably, the cross-frame matching process includes: in the word recognition test, when identity identifier 002 responds with an apple, the system extracts the voiceprint features of the audio segment and calculates the similarity with the voiceprint template of identity identifier 002 in the initial feature library. If the similarity is greater than 0.8, the corresponding data is stored in the data sequence of identity identifier 002; in the multi-step instruction test, assuming that identity identifier 003 starts to perform the action of picking up the red block at frame 8, the system records the trajectory of the key points of its arm bones. At frame 30, identity identifier 003 performs the action of putting the blue box in. The system compares the bone trajectory of frame 30 with the bone template of identity identifier 003 in the initial feature library to confirm that it is a continuous action of the same identity identifier, thereby realizing the cross-frame connection and data collection of the complete action trajectory from picking up the block to putting the block in.

[0013] Preferably, in step 4, the formula for calculating the conformity index model is the individual response latency minus the population first response latency, divided by the population average response latency. The population first response latency is the minimum latency value in the population after excluding outliers, and the population average response latency is the arithmetic mean of the population latencies after excluding outliers. When the conformity index is greater than 0.7, the system combines other assessment indicators to comprehensively determine whether the individual has a neurodevelopmental risk and provides corresponding prompts in the assessment report.

[0014] Preferably, the data preprocessing step in the data verification process sets dual thresholds based on physiological and testing rules. The lower threshold is set to 0.3 seconds based on the physiological mechanism of auditory response. If an individual's latency is less than 0.3 seconds, it is judged as an abnormally short latency and the data is excluded. The upper threshold is set to 8 seconds based on the system testing rules. If an individual's latency is greater than 8 seconds, it is judged as an abnormally long latency and the data is excluded. The system automatically accumulates group response latency data of the same age group and the same scenario to generate an average latency benchmark library for age groups, which is used to dynamically optimize the threshold range.

[0015] Preferably, the outlier identification step uses the interquartile range method to further identify potential outliers in the preprocessed latency data. The lower quartile and upper quartile of the data are calculated, and the interquartile range is the upper quartile minus the lower quartile. Data that are less than the lower quartile minus 1.5 times the interquartile range or greater than the upper quartile plus 1.5 times the interquartile range are marked as potential outliers. For the marked potential outliers, the system associates the individual's positioning accuracy and command completion data. If the positioning accuracy is less than 60% and the command completion is less than 50%, then it is confirmed as a special interference individual and its latency data is formally excluded.

[0016] Preferably, the group verification step is initiated when the proportion of abnormal data in the group is greater than 30%. The formula for calculating the proportion of abnormal data is the sum of the number of preprocessed exclusions and the number of anomalies identified by the interquartile range method, divided by the total number of people in the group. For groups with a high proportion of abnormal data, the system divides the group into a fast subgroup and a slow subgroup according to the relative level of individual latency. The fast subgroup includes individuals with a latency period less than or equal to the group median, and the slow subgroup includes individuals with a latency period greater than the group median. The conformity index is calculated based on the statistical characteristics of the fast and slow subgroups respectively, to ensure that the evaluation results more accurately reflect the relative responsiveness of individuals in the group.

[0017] Preferably, the multidimensional developmental correlation indicators include localization accuracy, command completion rate, and response stability. A localization accuracy rate of less than 80% indicates bilateral hearing asymmetry, a command completion rate of less than 60% indicates attention problems, and response stability is determined by latency fluctuations greater than 2 seconds in three consecutive tests, indicating inattention. The system integrates these indicators to construct an auditory nerve development correlation assessment system, classifies the risk level into three levels: low, medium, and high, and defines clear judgment criteria and intervention recommendations for each level.

[0018] Preferably, in step 5, the background noise module uses digital audio synthesis technology to simulate common campus noises such as classroom conversations and corridor footsteps. The noise spectrum characteristics are measured and calibrated to ensure a high degree of similarity to real environmental noise. The intensity adjustment uses digital gain control with a step size of 1 dB. The test mode switching can be completed with one click through the software interface. The system automatically records the noise mode and intensity during the test as environmental background information for the evaluation results.

[0019] Preferably, in step 6, the data acquisition and processing module automatically obtains raw data from the sound source generating device, the multi-mode sound control module, the panoramic AI recognition module, and the group behavior analysis and neurodevelopment determination module. After data cleaning, format conversion, and normalization, a standardized dataset containing hearing threshold data, localization accuracy, response latency, instruction completion rate, and conformity index is generated. The dataset is stored in a structured format, supporting fast querying and statistical analysis.

[0020] Preferably, the intelligent report generation module automatically generates an individualized hearing health and neurodevelopment assessment report based on a standardized dataset and risk assessment results. The report includes a test summary, detailed results for each mode, anomaly markers, risk level determination, and targeted recommendations. Anomaly markers use a combination of color coding and text descriptions; for example, a hearing threshold of 45 dB at 2000 Hz is marked as mild hearing loss, and a conformity index of 0.7 is marked as neurodevelopmental risk. The recommendations section provides specific guidance based on the type of anomaly, such as referral to an ENT specialist or auditory attention training.

[0021] Preferably, the personalized training generation module automatically matches and generates gamified training programs based on the evaluation results and a preset training rule library; the focus training program is designed as a 1 to 5 minute break game, such as an animal sound location quiz, to train children to quickly identify sound sources in a distracting environment; the instruction execution training program is designed as a 20 to 45 minute classroom training, such as a multi-step instruction block game, to gradually improve children's auditory memory and execution ability; the neurodevelopmental intervention program designs auditory-motor linkage training for high-risk children, such as performing corresponding actions after hearing a specific animal sound, to promote the coordinated development of auditory and motor functions; all training programs provide detailed implementation steps, required materials, and expected goals.

[0022] Preferably, the method further includes a system calibration and maintenance process, regularly calibrating the sound pressure level of the sound source generating device to ensure the accuracy of the output sound; calibrating the panoramic AI recognition module to correct the mapping relationship between camera parameters and spatial coordinates; and performing data backup and performance optimization on the software design module to ensure long-term stable operation of the system and data security.

[0023] Preferably, the method supports parallel testing and data management for multiple groups. By deploying multiple sound source generating devices and panoramic monitoring hardware, multiple groups of children can be screened and assessed simultaneously in different areas. The central management platform uniformly receives, stores, and analyzes all test data, generates group statistical reports and trend analyses, and provides decision support for educational institutions and health departments.

[0024] Compared with the closest existing technology, the present invention has the following advantages: By integrating sound source generation devices, multi-mode sound control, panoramic AI recognition, group behavior analysis and neurodevelopment assessment, background noise simulation, and intelligent software design, a complete screening and assessment system for the correlation between children's hearing and neurodevelopment was constructed. This method significantly improves children's cooperation during testing by utilizing gamified interaction and animal-shaped design. Through multi-mode testing, it covers a comprehensive assessment dimension from basic hearing thresholds to higher-order auditory cognition. Based on panoramic AI and multi-factor authentication, it achieves accurate collection and matching of individual response data in group testing. It innovatively introduces a conformity index model and multi-dimensional developmental correlation indicators to achieve early identification of neurodevelopmental risks. Combined with background noise simulation, it enhances the effectiveness of assessment in real-world environments. Through automated report generation and personalized training recommendations, it forms a closed-loop path from screening to intervention. Overall, it solves the problems of low children's cooperation, single assessment dimensions, large subjective errors, difficulty in identifying individuals in groups, lack of assessment of neurodevelopmental risks, lack of assessment in real-world environments, high operational thresholds, and disconnect between screening and training in existing technologies. It provides an efficient, objective, multi-dimensional, and easy-to-implement solution for large-scale screening of children's hearing health and neurodevelopment. Attached Figure Description

[0025] Figure 1 This is a flowchart of the screening and assessment method for the correlation between hearing and neurodevelopment in children based on panoramic AI provided by the present invention; Figure 2 This is a flowchart illustrating the specific implementation of the behavioral data collection method for the screening and assessment of the correlation between hearing and neurodevelopment in children based on panoramic AI, provided by this invention. Figure 3 This is a schematic diagram of the principle framework of the panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children provided by this invention. Detailed Implementation

[0026] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1: This invention provides a method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI, such as... Figure 1As shown, it includes: an integrated sound source generating device, multi-mode sound control, panoramic AI recognition, group behavior analysis and neurodevelopment assessment, background noise simulation and intelligent software design, constructing a closed-loop path from "screening" to "intervention", and applying it to a panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children.

[0029] In the aforementioned panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children, step 1 involves deploying a sound source generating device. This device employs an array of curved animal-shaped microphones, with 6 to 8 plush animal-shaped microphones arranged at equal intervals with a radius of 1.5 to 2.0 meters and a radiation angle of 120 to 180 degrees. Each microphone has a built-in independent audio output unit. The animal shapes guide children to point out the location of the animal and provide feedback on the sound source, thus improving their cooperation during the test. The interval between test sounds is set to 2 to 3 seconds. Before each round of testing, a cartoon countdown is displayed on a visual screen, and auditory instructions are played simultaneously to ensure the children's attention is focused. Short pure tone signals that meet the pure tone hearing threshold test standards and international audiometry standards are generated, with a frequency range of 250 Hz to 8000 Hz, meeting the needs of clinical-grade hearing screening. Specifically, the arc-shaped animal-shaped microphone array adopts the shapes of plush animals such as lions, tigers, puppies, and ducklings. Each microphone has a built-in independent audio output unit that supports high-fidelity audio playback, ensuring the clarity and consistency of the sound signal. The visual screen uses a high-brightness LCD display, and the cartoon countdown animation design is in line with the cognitive characteristics of young children. The auditory instructions are professionally recorded and optimized, with a moderate speaking speed and a friendly tone to maximize the attraction and maintenance of children's attention. The generation of short pure tone signals strictly follows international audiometry standards, with a frequency accuracy error of less than or equal to 2%, and an intensity control accuracy of ±1 dB hearing level, ensuring the clinical validity and comparability of the test results. See the sound source generating device for details. Figure 1 The front-end interaction layer in the system architecture shown has a physical layout that forms a fan-shaped sound field centered on the test area, ensuring that the spatial distribution of each sound source point is uniform and unobstructed, thereby providing an accurate spatial benchmark for subsequent sound source localization capability assessment.

[0030] In the above-mentioned screening and assessment method for the correlation between hearing and neurodevelopment in children based on panoramic AI, step 2 involves running a multi-mode sound control module to execute four test modes for different assessment objectives. Specifically, Mode 1 is a physiological hearing threshold test, which uses a short pure tone generation unit to sequentially output pure tones at frequencies of 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz, and 8000 Hz, with an intensity range from 10 dB to 60 dB hearing level, to detect hearing thresholds and identify mild or unilateral hearing loss; Mode 2 is a fun location recognition test, which randomly selects sounds from an animal sound library and plays them through a specific animal microphone, guiding children to point out the corresponding animal, assessing sound source localization ability and bilateral hearing symmetry; Mode 3 is a word recognition test, which uses a children's common vocabulary library to play words, and children click on the corresponding images on the touchscreen or place stickers under the corresponding animals, assessing speech comprehension and auditory-visual association ability; Mode 4 is a multi-step instruction test, which calls up a campus scene instruction library to play multi-step instructions, and children complete the corresponding actions, assessing auditory attention and executive function. Further details are provided. The Mode 1 short pure tone generation unit employs digital signal processing technology, generating precise frequency pure tone signals via a direct digital synthesizer. The test duration for each frequency point is set to 1 to 2 seconds, with an intensity variation step of 5 dB hearing level. It automatically records the minimum audible intensity at each frequency point as the hearing threshold. Mode 2, the animal sound library, contains various common animal sounds, each with a duration controlled between 0.5 and 1 second. The sound pressure level is uniformly calibrated to 50 dB hearing level, and the playback order uses a pseudo-random algorithm to prevent children from relying on sequential memorization, which could affect test accuracy. Mode 3, the children's common vocabulary library, selects high-frequency children's words. Each word has clear pronunciation and simple syllables. The image library corresponds one-to-one with the words, and the touchscreen response time is less than 100 milliseconds, ensuring a smooth testing process. Mode 4, the campus scene instruction library, designs multi-step complex instructions, with instruction length gradually increasing from two steps to five steps. The content involves multiple attributes such as color, shape, and position. The system records the accuracy and time taken for children to complete the instructions as a key indicator for performance evaluation. This multi-mode sound control module serves as… Figure 1 The core logic unit of the system architecture shown contains four independent but collaboratively schedulable subroutines, each corresponding to one of the four test modes. The system dynamically switches modes according to preset test procedures or operator instructions, and monitors the parameter output and user interaction status in each mode in real time.

[0031] In the aforementioned panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children, such as Figure 2As shown, in step 3, the panoramic AI recognition module is activated, using a 4K resolution wide-angle panoramic camera as the panoramic monitoring hardware, installed 2.5 to 3 meters in front of the sound source array, covering the area where 5 to 8 children are tested simultaneously, ensuring no blind spots; the AI ​​action judgment logic is executed based on the skeletal key point recognition algorithm, focusing on monitoring head turning actions, setting the head turning direction direction angle greater than or equal to 30 degrees and the reaction latency less than or equal to 5 seconds as a valid reaction, simultaneously recognizing auxiliary actions such as raising hands, making sounds, and turning around and excluding false triggers; personalized group recognition is implemented, integrating facial recognition, voiceprint collection and skeletal feature triple identity verification, and achieving individual tracking and accurate data allocation through the establishment of an initial feature library and cross-frame matching. Specifically, the panoramic monitoring hardware employs a 4K resolution wide-angle lens with a horizontal viewing angle of ≥180 degrees and a vertical viewing angle of ≥120 degrees, a frame rate of at least 30 frames per second, and is equipped with a high-performance image sensor to ensure clear capture of children's movement details even under indoor lighting conditions. The skeletal key point recognition algorithm is based on a deep learning model, extracting the coordinates of 25 key points on the human body in real time. The head turning angle is calculated based on the spatial vectors of the brow and bilateral ear key points. The reaction latency is calculated from the start of sound playback to the moment the head turning angle first reaches 30 degrees. The auxiliary action recognition excludes non-target action interference by setting thresholds for action amplitude and duration. This panoramic AI recognition module ensures full coverage of the entire test area, while its software algorithm constitutes the core engine for individual behavior data collection.

[0032] The personalized identification process for the group comprises two key stages: initial feature library establishment and cross-frame matching. In the initial feature library establishment stage, the system first extracts a 128-dimensional feature vector from each child's face using a facial detection algorithm, assigning an independent identity to each detected face. Facial feature extraction and lip-sync detection are performed simultaneously. When the lip-sync is determined to be vocal and the confidence level is greater than 0.8, the millisecond-level timestamp of the current frame is recorded as the time anchor for voiceprint extraction. A corresponding segment is extracted from the audio stream, and a 39-dimensional voiceprint feature vector is extracted and directly associated with the identity locked by facial recognition in the current frame. In each frame, facial detection outputs the bounding box coordinates of the facial region, and skeletal feature extraction simultaneously outputs the coordinates of pose key points. By calculating the spatial overlap between the head skeletal key points and the facial bounding box, when the overlap is greater than 0.8, facial features and skeletal features within the same spatial region are bound to the same identity. Voiceprint features and skeletal features are associated through a bridge formed by facial features, ultimately constructing a robust initial feature library containing facial, voiceprint, and skeletal features for each identity. In the cross-frame matching stage, the system uses this initial feature library to continuously track individuals in subsequent frames. For example, in the word recognition test, when ID 002 responds with "apple", the system extracts the voiceprint features of the audio segment and calculates the similarity with the voiceprint template of ID 002 in the initial feature library. If the similarity is greater than 0.8, the corresponding data is stored in the data sequence of ID 002. In the multi-step instruction test, assuming that ID 003 starts to perform the action of picking up the red block at frame 8, the system records the trajectory of the key points of its arm bones. At frame 30, ID 003 performs the action of putting the blue box in. The system compares the bone trajectory of frame 30 with the bone template of ID 003 in the initial feature library to confirm that it is a continuous action of the same ID, realizing the cross-frame connection and data collection of the complete action trajectory from picking up the block to putting the block in. In addition, to effectively extract high-confidence voiceprint features from the mixed voices of multiple children, the system calculates the straight-line distance between the target identity identifier and each microphone based on the spatial coordinates of the target identifier in the current frame, selects the two closest microphones, and prioritizes the audio data from these two microphones; it suppresses non-target direction noise through beamforming technology, verifies the consistency between audio and lip movements by combining a speech lip-syncing algorithm, and uses a multi-segment feature aggregation method to perform a weighted average of the voiceprint features of continuous audio frames to improve the robustness and discriminativeness of the voiceprint features.

[0033] In the aforementioned panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children, step 4 involves executing a group behavior analysis and neurodevelopment determination module. Based on the conformity index model, the ratio of an individual's response latency to the group's first-response latency and the group's average response latency is calculated. When the index is greater than 0.7, it indicates that the individual's response is significantly lagging behind the group, potentially indicating neurodevelopmental risk or auditory processing disorder. New data preprocessing, outlier identification, and group verification data validation steps are added. By filtering outlier data and dynamically adjusting the calculation benchmark, the conformity index ensures that it accurately reflects the individual's response characteristics within the group. Combining multi-dimensional developmental correlation indicators such as positioning accuracy, command completion rate, and response stability, an auditory neurodevelopment correlation assessment system is constructed, and a risk level is generated. Specifically, the calculation formula for the conformity index model is: The primacy response latency is the minimum latency value in the population after excluding outliers, and the average response latency is the arithmetic mean of the population latency after excluding outliers. When the conformity index is greater than 0.7, the system combines other assessment indicators to comprehensively determine whether an individual has a neurodevelopmental risk and provides corresponding prompts in the assessment report. The core principle framework of this model can be found in [link to relevant documentation]. Figure 3 As shown.

[0034] To ensure the accuracy of the conformity index calculation, the system introduces a three-level data verification mechanism. The first level is data preprocessing, which sets dual thresholds based on physiological and testing rules. The lower threshold is set at 0.3 seconds based on the physiological mechanism of auditory response. If an individual's latency is less than 0.3 seconds, it is judged as an abnormally short latency and the data is excluded. The upper threshold is set at 8 seconds based on the system's testing rules. If an individual's latency is greater than 8 seconds, it is judged as an abnormally long latency and the data is excluded. The system automatically accumulates group reaction latency data for the same age group and the same scenario to generate an average latency benchmark library for age groups, which is used to dynamically optimize the threshold range. The second level is outlier identification. The interquartile range (IQR) method is used to further identify potential outliers in the preprocessed latency data. The lower and upper quartiles of the data are calculated, with the IQR calculated as the upper quartile minus the lower quartile. Data points smaller than the lower quartile minus 1.5 times the IQR or larger than the upper quartile plus 1.5 times the IQR are marked as potential outliers. For marked potential outliers, the system correlates the individual's location accuracy and command completion rate. If the location accuracy is less than 60% and the command completion rate is less than 50%, the individual is confirmed as a special interference case and its latency data is formally excluded. The third level is group validation, which is initiated when the proportion of outlier data in the group exceeds 30%. The formula for calculating the proportion of outlier data is the sum of the number of preprocessed exclusions and the number of outliers identified by the interquartile range method, divided by the total number of people in the group. For groups with a high proportion of outlier data, the system divides the group into a fast subgroup and a slow subgroup based on the relative level of individual latency. The fast subgroup includes individuals with a latency period less than or equal to the group median, while the slow subgroup includes individuals with a latency period greater than the group median. The conformity index is calculated based on the statistical characteristics of the fast and slow subgroups respectively, to ensure that the evaluation results more accurately reflect the relative responsiveness of individuals in the group.

[0035] The multidimensional developmental correlation indicators include localization accuracy, command completion rate, and response stability. A localization accuracy rate below 80% suggests bilateral hearing asymmetry, a command completion rate below 60% suggests attention problems, and response stability is determined by latency fluctuations greater than 2 seconds across three consecutive tests, indicating inattention. The system integrates these indicators to construct an auditory nerve development correlation assessment system, classifying risk levels into low, medium, and high, and defining clear judgment criteria and intervention recommendations for each level. The integrated judgment logic of this assessment system is also reflected in… Figure 2 The text demonstrates how the herd index, along with other indicators, works together to produce the final risk level output.

[0036] In the aforementioned panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children, step 5 integrates a background noise module to simulate common school background noise. The intensity adjustment range is 35 to 55 decibels. During the test, either a noise-free mode or a school noise mode is selected to assess the child's auditory focus and anti-interference ability in the learning environment. Specifically, the background noise module uses digital audio synthesis technology to simulate common school noises such as classroom conversations and corridor footsteps. The noise spectrum characteristics have been measured and calibrated to ensure a high degree of similarity to real environmental noise. The intensity adjustment uses digital gain control with a step size of 1 decibel. The test mode switching is completed with one click through the software interface. The system automatically records the noise mode and intensity during the test as environmental background information for the assessment results. This module serves as... Figure 1 The environmental simulation unit of the system architecture shown can mix its output with the signal of the multi-mode sound control module in real time, so that background noise can be superimposed in all modes such as physiological hearing threshold test and fun positioning recognition to evaluate the auditory performance of young children in real learning environment.

[0037] In the aforementioned panoramic AI-based screening and assessment method for the correlation between hearing and neurodevelopment in children, step 6 involves running the software design module to automatically collect hearing threshold data, localization accuracy, response latency, instruction completion rate, and conformity index, generating a standardized dataset. For each individual, a hearing health and neurodevelopment assessment report is generated, annotating abnormalities and providing suggestions. Based on the assessment results, a gamified training program is automatically generated, including attention training, instruction execution training, and neurodevelopmental intervention. Specifically, the data acquisition and processing module automatically obtains raw data from the sound source generating device, the multi-mode sound control module, the panoramic AI recognition module, and the group behavior analysis and neurodevelopmental assessment module. After data cleaning, format conversion, and normalization, a standardized dataset containing hearing threshold data, localization accuracy, response latency, instruction completion rate, and conformity index is generated. The dataset is stored in a structured format, supporting rapid querying and statistical analysis. The intelligent report generation module automatically generates individualized hearing health and neurodevelopment assessment reports based on standardized datasets and risk assessment results. The report includes a test summary, detailed results for each mode, anomaly annotations, risk level determination, and targeted recommendations. Anomaly annotations use a combination of color coding and text descriptions. For example, a hearing threshold of 45 dB at 2000 Hz is labeled as mild hearing loss, and a conformity index of 0.7 is labeled as neurodevelopmental risk. The recommendations section provides specific guidance based on the type of anomaly, such as referral to an ENT specialist or auditory attention training. The personalized training generation module automatically matches and generates gamified training programs based on the assessment results and a preset training rule base. The concentration training program is designed as a 1- to 5-minute break game, such as a quiz game to locate animal sounds, training children to quickly identify sound sources in a noisy environment. The instruction execution training program is designed as a 20- to 45-minute classroom training, such as a multi-step instruction block game, to gradually improve children's auditory memory and execution ability. The neurodevelopmental intervention program is designed for high-risk children to conduct auditory-motor linkage training, such as performing corresponding actions after hearing a specific animal sound, promoting the coordinated development of auditory and motor functions. All training programs provide detailed implementation steps, required materials, and expected goals.

[0038] To ensure the long-term stable operation of the system, the method also includes system calibration and maintenance procedures. This involves periodically calibrating the sound pressure level of the sound source generator to ensure the accuracy of the output sound; calibrating the panoramic AI recognition module to correct the mapping relationship between camera parameters and spatial coordinates; and performing data backup and performance optimization on the software design module to ensure long-term stable operation and data security. Furthermore, the method supports parallel testing and data management for multiple groups. By deploying multiple sound source generators and panoramic monitoring hardware, multiple groups of children can be screened and assessed simultaneously in different areas.

[0039] In this embodiment, the specific process of the screening and assessment method for the correlation between hearing and neurodevelopment in children based on panoramic AI is as follows; (a) Hardware deployment 1. Sound source array installation: Select 8 plush animal-shaped microphones (lion, tiger, dog, duck, sheep, cat, rabbit, and bear), and fix them on the front wall of the classroom at equal intervals with a radius of 1.5m and a radiation angle of 180°. The microphone height is 1.2m (adaptable to the line of sight of children in sitting posture). 2. Panoramic camera deployment: Install a 4K wide-angle camera 2.8m high in front of the array, with the lens angled downwards at 15°, to ensure coverage of the test area where 5 children are seated at the same time (0.8m apart). 3. Control terminal connection: The sound source array, panoramic camera and tablet computer (health teacher's operating terminal) are wirelessly connected. The tablet computer has system software installed, which supports parameter setting, data viewing and report export.

[0040] (II) Parameter Settings 1. Hearing threshold test parameters: short pure audio frequency sequence 250Hz→500Hz→1000Hz→2000Hz→4000Hz→8000Hz, initial intensity 30dB HL, increasing by 5dB each time when there is no response (maximum 60dB HL), decreasing by 5dB each time when there is a response, and recording the threshold. 2. AI recognition parameters: head turning angle ≥30°, reaction latency threshold ≤5s, and action recognition accuracy ≥95% (adaptability to children's actions is ensured through pre-training). 3. Background noise setting: Select 45dB (simulating normal classroom noise) for daily screening, and switch to 55dB (high interference environment) for special needs (such as suspected attention deficit).

[0041] (III) Group Testing Procedure (Taking a Kindergarten Class of 30 Students as an Example) 1. Group testing: Divide 30 people into 6-10 groups (2-5 people in each group), test each group for 5-15 minutes, and the total time is about 1.5 hours (3-5 times more efficient than single mode). 2. Test Startup: The school nurse selects "Class Screening Mode" on the tablet, and the system automatically plays a guiding animation ("Listen to the sounds with the animals and find the talking animals"). 3. Multi-mode testing: First round (2-5 minutes): Mode 1 (hearing threshold test) + Mode 2 (location recognition), short pure tones and animal sounds are played randomly, and AI records the child's reaction; Second round (2-5 minutes): Mode 3 (word recognition) + Mode 4 (instruction test), play words and block instructions, and the tablet displays the captured images of the child's actions simultaneously; Data Upload: After a single test is completed, the data is automatically uploaded to the system cloud platform, generating a temporary report; Class summary: After 6-10 groups of tests are completed, the system generates a class summary report, marking "children who need special attention" (such as those with abnormal hearing thresholds or conformity index > 0.7).

[0042] (iv) Examples of data interpretation and intervention Individual report example 1: Child's name: XXX, Age: 4 years old Hearing threshold: 45dB HL at 2000Hz (slight decrease), normal at other frequencies; Location accuracy: 75% (possibly due to bilateral hearing asymmetry); Conformity Index: 0.65 (No significant developmental risk); Recommendation: Refer to the ENT department to investigate the cause of hearing loss and conduct unilateral auditory localization training.

[0043] Individual report example 2: Child's name: XXX, Age: 8 years old Hearing threshold: Average threshold of 20 dB HL in the 500-4000 Hz range, normal at all frequencies; Location accuracy: 45% (bilateral hearing asymmetry); Conformity Index: 0.8 (Indicating potential hearing risk or related neurodevelopmental risk) Recommendations: Refer to an ENT specialist to check bilateral hearing thresholds. Refer to a pediatric or neurological specialist to rule out neurological risks.

[0044] Example of group intervention: For the five children in the class whose "instruction completion rate is less than 60%", a training program of "instruction block game" of 20 minutes / session was generated, once a day, for two weeks. After retesting, the concentration achievement rate increased to more than 80%.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A screening and assessment method for the correlation between hearing and neurodevelopment in children based on panoramic AI, characterized in that, include: Step 1: Deploy the sound source generating device, using an arc-shaped animal-shaped microphone array, and equidistantly arranged plush animal-shaped microphones with a radiation angle greater than 90 degrees. Each microphone has a built-in independent audio output unit. The children are guided to point out the location of the animal by the animal shapes to provide feedback on the sound source. Set the interval between test sounds to 2 to 3 seconds. Before each round of testing, a cartoon countdown is displayed on the visual screen and auditory instructions are played simultaneously. Generate short pure tone signals with a frequency range of 250 Hz to 8000 Hz that meet the pure tone hearing threshold test standards and international audiometry standards. Step 2: Run the multi-mode sound control module and execute four test modes. Mode 1 is the physiological hearing threshold test, which outputs pure tones at frequencies of 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz, and 8000 Hz in sequence, with an intensity range from 10 dB to 60 dB hearing level. Mode 2 is the fun location recognition test, which randomly selects sounds from the animal sound library and plays them through a specific animal microphone, guiding children to point out the corresponding animals. Mode 3 is the word recognition test, which plays words using a children's common vocabulary library, and children tap the corresponding images on the touchscreen or place stickers under the corresponding animals. Mode 4 is the multi-step instruction test, which calls the campus scene instruction library to play multi-step instructions, and children complete the corresponding actions. Step 3: Activate the panoramic AI recognition module. A 4K resolution wide-angle panoramic camera is installed 2.5 to 3 meters in front of the sound source array, covering the testing area for 5 to 8 children. Based on the skeletal key point recognition algorithm, head turning movements are monitored. A valid response is defined as a head turning angle greater than or equal to 30 degrees and a reaction latency less than or equal to 5 seconds. Implement personalized group recognition, integrating facial recognition, voiceprint acquisition, and skeletal feature triple identity verification. Individual tracking is achieved through the establishment of an initial feature database and cross-frame matching. Step 4: Execute the group behavior analysis and neurodevelopment assessment module. Based on the conformity index model, calculate the ratio of individual response latency to the group's first response latency and the group's average response latency. When the index is greater than 0.7, it indicates neurodevelopmental risk. Combine the positioning accuracy, command completion rate and response stability to construct an auditory neurodevelopment correlation assessment system and generate a risk level. Step 5: Integrate the background noise module to simulate common campus background noise. The intensity adjustment range is 35 dB to 55 dB. Select the no noise or campus noise mode during the test. Step 6: Run the software design module to automatically collect hearing threshold data, localization accuracy, response latency, instruction completion rate, and conformity index, and generate a standardized dataset; generate an auditory health and neurodevelopment assessment report for each individual, and automatically generate a gamified training program based on the assessment results.

2. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: In step 1, the arc-shaped animal-shaped microphone array uses plush animal shapes such as lions, tigers, puppies, and ducks; the visual screen uses a high-brightness LCD display, and the cartoon countdown animation is in line with the cognitive characteristics of young children; the frequency accuracy error of the short pure tone signal is less than or equal to 2%, and the intensity control accuracy is ±1 decibel hearing level.

3. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: In step 2, the duration of each frequency point test in mode 1 is 1 to 2 seconds, and the intensity change step is 5 dB hearing level; the duration of animal sounds in mode 2 is 0.5 to 1 second, the sound pressure level is calibrated to 50 dB hearing level, and the playback order adopts a pseudo-random algorithm; the touch screen response time in mode 3 is less than 100 milliseconds; the instruction length in mode 4 gradually increases from two steps to five steps, and the content involves multiple attributes such as color, shape, and position.

4. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: In step 3, the panoramic monitoring hardware has a horizontal viewing angle of ≥180 degrees, a vertical viewing angle of ≥120 degrees, and a frame rate of ≥30 frames per second; the skeletal key point recognition algorithm extracts the coordinates of 25 key points of the human body in real time; the head turning angle is calculated based on the spatial vector of the key points of the eyebrows and both ears; and the reaction latency is calculated from the start of the sound playback to the moment when the head turning angle first reaches 30 degrees.

5. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: The initial feature library establishment process in the personalized group identification includes: extracting a 128-dimensional feature vector of each child's face using a facial detection algorithm and assigning an independent identity identifier; when the lip shape is determined to be vocalization and the confidence level is greater than 0.8, recording the current frame's millisecond-level timestamp as the time anchor point for voiceprint extraction, extracting audio segments, and extracting a 39-dimensional voiceprint feature vector; by calculating the spatial overlap between the head skeleton key points and the facial bounding box, when the overlap is greater than 0.8, binding the facial features and skeletal features to the same identity identifier.

6. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 5, characterized in that: In the personalized group identification, the straight-line distance between the target identity identifier and each microphone is calculated based on the spatial coordinates of the target identity identifier in the current frame. The audio data of the two closest microphones is selected and used preferentially. Beamforming technology is used to suppress non-target direction noise. The consistency between audio and lip movements is verified by combining the speech lip-syncing algorithm. The voiceprint features of continuous audio frames are weighted and averaged.

7. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: In step 4, the formula for calculating the herd index model is (individual response latency - population first response latency) / population average response latency; both the population first response latency and the population average response latency are calculated based on population latency data after excluding outlier data.

8. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 7, characterized in that: The data verification process includes data preprocessing, outlier identification, and group verification. Data preprocessing sets a lower threshold of 0.3 seconds and an upper threshold of 8 seconds to exclude abnormally short latency periods and abnormally long latency periods. Outlier identification uses the interquartile range method, marking data that are less than the lower quartile minus 1.5 times the interquartile range or greater than the upper quartile plus 1.5 times the interquartile range as potential outliers. These outliers are then confirmed and excluded based on a positioning accuracy of less than 60% and a command completion rate of less than 50%. Group validation is initiated when the proportion of abnormal data in the population exceeds 30%. The population is divided into a fast subgroup and a slow subgroup based on the median latency, and the conformity index is calculated for each subgroup.

9. The method for screening and assessing the correlation between hearing and neurodevelopment in children based on panoramic AI according to claim 1, characterized in that: Among the multidimensional developmental correlation indicators, a positioning accuracy rate of less than 80% indicates bilateral hearing asymmetry, a command completion rate of less than 60% indicates attention problems, and response stability is determined by a latency fluctuation of more than 2 seconds in three consecutive tests. The risk level is divided into three levels: low, medium, and high, and clear judgment criteria and intervention suggestions are defined for each level.

10. A method for screening and assessing the correlation between auditory and neurodevelopmental characteristics in children based on panoramic AI, as described in claim 1, characterized in that: The gamified training program in step 6 includes attention training, instruction execution training, and neurodevelopmental intervention. Attention training is designed as 1 to 5 minutes of short games during breaks, instruction execution training is designed as 20 to 45 minutes of classroom training, and the neurodevelopmental intervention program is designed for high-risk children with auditory-motor linkage training. All training programs provide implementation steps, required materials, and expected goals.

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