2026 National Science Popularization Month · Shanghai Fuxing Park
Connectinghealthy ageingandquantitative medicine
CN-Age360 brings medical artificial intelligence into a real-world public science setting. From 11–13 September 2026, every day15:00–19:00, we will be at theEast Gate of Shanghai Fuxing Parkfor an interactive public science experience on healthy ageing and medical AI. You can also use your phone to enter the online experience and complete face, voice, eye region, neck, hand dorsum, nails, upper-arm skin, PPG and 4-m gait modules; grip strength and other modules requiring dedicated devices can be completed on site.
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View the Fuxing Park event
Multimodal experience system
Explore research platforms
Public science experience and research technology showcase · Results are not medical diagnoses or treatment advice
CN-Age360Research technology stack
Digital phenotypingFaceAge · VoiceAgeLow-burden, repeatable, multimodal
Quantitative imagingUltrasound AIRecognition, segmentation, measurement, reporting
Paediatric reference valuesIntelligent growth and development assessmentPercentiles · Z-scores · Growth trajectories
ValidationPopulation and multicentre studiesExternal validation and long-term outcome research
3 core platformsDigital phenotyping · Ultrasound quantification · Paediatric reference values
Multimodal inputsImaging · Voice · Function · Physiological signals
Population validationFrom model performance to population and external validation
End-to-end research workflowAcquisition → Quantification → Standardisation → Research translation
2026 National Science Popularization Month · On-site event
Healthy ageing × Medical AI: see you at Fuxing Park
11–13 September 2026, 15:00–19:00 daily, East Gate of Shanghai Fuxing Park. The on-site experience focuses on low-burden, non-invasive interaction and scientific explanation; during busy periods, visitors can scan a QR code to continue suitable low-hardware modules on their phone and complete device-dependent modules on site.
Fuxing Park · Shanghai
09.11–13
East Gate of Shanghai Fuxing Park
15:00–19:00
Healthy ageing and medical AI public science experience
On-site participation · Mobile overflow · Continued access after the event Meeting / experience point: East Gate, Fuxing Park
On-site participation · Mobile overflow · Continued access after the event Meeting / experience point: East Gate, Fuxing Park
Coming soon
Start with one ID and complete one multimodal healthy-ageing experience across different stations
We want to turn abstract medical AI into public science content that people can understand and experience themselves. Phones can complete face, voice, local imaging, camera-based PPG and 4-m gait modules; grip strength and Polar PPG can be experienced at the East Gate of Fuxing Park.
Date11–13 September 2026
Time15:00–19:00 daily
LocationEast Gate of Shanghai Fuxing Park
Imaging phenotypesFace age, eye region, neck, hand dorsum, nails and upper-arm skin are captured using standardised framing.
Voice phenotypeNatural speech is recorded; the original WAV is retained, with a standardised analysis copy and current model output generated.
Functional and physiological signalsGrip strength, Polar Verity Sense PPG and 4-m gait capture functional and physiological signals.
Multi-station collaborationEnter the same ID on any computer to retrieve completed modules, results and pending modules; there is no fixed testing order.
Participation and privacy notice:Participation is voluntary. Before any module involving photography, audio recording or physiological signal acquisition, we explain what is collected, how it is used and relevant privacy information. If data are intended for later research, the on-site information and consent process applies. Visitors who only view the science content do not need to provide personal data. Immediate results are for public science and technology demonstration only and are not disease diagnoses, treatment advice or individual health-risk conclusions.
CNage360 HEALTHY AGING EXPERIENCE
10 healthy-ageing experience modules
Face, voice, eye region, neck, hand dorsum, nails, upper arm, PPG and gait all have online entry points; grip strength and other modules requiring dedicated devices can be completed on site. Choose any module to begin.
ONLINE EXPERIENCE
Start online experience →
You can start on your phone now
The online experience page contains entry points for all 10 modules. Local imaging, camera-based PPG and 4-m gait can be completed directly in the browser; FaceAge and VoiceAge use the existing model pages.
Acquisition principlesPrioritise retention of raw dataPhotos, WAV files, raw PPG streams, gait videos and other raw data are saved first, then analysed in the background.
Background analysisContinue immediately after acquisitionModel analysis enters a background queue, so participants do not need to wait at the current station.
Cross-station synchronisationMinimise data loss even when offlineSave locally first; if the central server is temporarily unavailable, data enter a pending synchronisation queue.
On-site resultsUnified experience reportCompleted modules and current derived results are summarised for public science feedback and research records.
Public science video · SCIENCE VIDEO
One video to understand healthy ageing and medical artificial intelligence
If you want to know why these measurements are performed, watch the science video and read the scientific rationale for each category here. Understanding the principles is not required before starting the experience; return whenever you are interested.
Why look at the face, eye region, neck and skin?Skin texture, colour and morphology are influenced by age, sun exposure, lifestyle and health status. Standardised images can turn these visible features into comparable digital phenotypes.
Why record the voice?Voice is produced jointly by breathing, vocal-fold vibration and neuromuscular control. Acoustic features such as pitch, stability and rhythm can change with age and physical condition.
Why measure grip strength and 4-m gait?Grip strength reflects muscular strength, while gait reflects mobility, balance and overall function. Both are intuitive measures of physical functional status.
Why measure PPG?PPG uses light to record pulse-wave fluctuations. It can demonstrate heart rate and signal stability and provide digital signals for research on circulation and autonomic function.
Why ask about diseases, smoking and alcohol?Chronic diseases and lifestyle can affect many physical characteristics. Recording this background helps explain individual differences and supports stratified analyses in later research.
Why not measure just one “age”?Ageing is multidimensional. Looking at appearance, voice, muscle strength, mobility and physiological signals together illustrates changes across different aspects of the body better than a single number.
The video area is reserved in the final site structure. When no video file is present, a science cover is shown; after the final MP4 is added, the page automatically switches to the player.
9:16 vertical videoSuitable for mobile QR access and on-site large screens
CN-Age360 · Public science
healthy ageing
× Medical artificial intelligence
Understand how the body changes with age—and what AI can and cannot do.
Science video · Coming soon
Our research
Three research directions, one unified methodological framework
We do not treat the homepage as a “feature list”. CN-Age360 is a research platform: each module addresses a defined question while sharing a common framework for standardised acquisition, quality control, external validation and multicentre translation.
01 / Digital phenotyping
Healthy-ageing digital phenotypes
Use face, voice and low-burden functional/physiological signals to build repeatable age-related phenotypes for community, cohort and remote research settings.
Learn more →02 / Medical imaging AI
General ultrasound automated quantification
Integrate organ recognition, view quality control, segmentation/key-point localisation, and standard measurements of diameters, areas and volumes into a complete quantitative workflow.
Open platform →03 / Paediatric reference values
Paediatric organ growth and development assessment
Build age-, sex- and body-size-specific organ reference distributions, with percentiles, Z-scores and longitudinal trajectories so measurements gain interpretable clinical-research context.
Open platform →Our mission
Not to build another “AI demo”, but to make new digital phenotypes and imaging measuresstandardised, externally validated and usable in real settings。
From the outset, the platform considers input standards, quality control, repeat measurements, reference ranges, multicentre differences and interpretation boundaries. For research, these are often more important than the highest accuracy on a single test set.
01
Standardised acquisitionStandardise photography, audio recording, ultrasound views and sensor acquisition conditions as far as possible.
02
AI quantitative analysisConvert unstructured images, voice and physiological signals into repeatable quantitative phenotypes.
03
Cross-population validationFocus on independent validation, multicentre and cross-device performance, and long-term health outcomes.
04
Integration into real research workflowsBring results into cohort studies, clinical research, long-term follow-up and real-world data accumulation.
Flagship platforms
Two independent platforms: general intelligent ultrasound measurement + paediatric CT multi-organ reference assessment
The two research tracks remain separate: the ultrasound platform supports general automated measurements in children and adults; the paediatric reference platform currently centres on 3D CT multi-organ quantification to establish age-, sex- and body-size-adjusted reference curves, percentiles and Z-scores.
General intelligent ultrasound measurement
General intelligent ultrasound organ measurement and quantitative analysis platform
For multi-organ and multi-view ultrasound, automatically perform organ recognition, image-quality assessment, segmentation and key-point localisation, then generate standard diameters, areas, volumes and structured reports.
Automatic organ / view recognition
Image quality control
Segmentation / key-point localisation
Standardised measurements
Area and volume estimation
Structured reporting
AI ultrasound quantitative analysis workspace
Organ recognition✓ Passed
Long axis 12.45 cmNormal
Volume 413.6 mLNormal
AI confidence0.94
Paediatric CT multi-organ reference values
Paediatric organ-size reference and growth assessment platform
Current research uses paediatric CT 3D multi-organ volumes and diameters as primary inputs. Combined with age, sex, height, weight and BSA, measurements are mapped to paediatric reference distributions to output percentiles, Z-scores, left-right differences and longitudinal growth trajectories.
P3–P97 reference curves
Age / sex stratification
Height / BSA adjustment
Z-score standardisation
Longitudinal trajectory assessment
Multicentre reference comparison
Paediatric CT multi-organ reference and growth assessment
Internal pilot reference library · v0.1Liver volume820 mL
Spleen volume165 mL
Bilateral kidney difference4.4%
PercentileP96
Z-score+1.90
Body-size adjustmentBSA
Liver volume · Age/sex/body-size reference curve
P97P90P50P10P3Age (years)
OrganMeasurementPercentileResult
Digital phenotyping
Low-burden digital phenotypes: suitable for large populations, communities and repeated follow-up
FaceAge and VoiceAge are two independent core modules. They are not presented as a “universal biological age”, but as repeatable age-related digital phenotypes for further validation against molecular, functional and long-term outcomes.
Image-derived digital phenotype
FaceAge
After quality control and deep visual representation of a standardised single-face frontal photograph, an age-related phenotype is produced. Its core value is non-contact, repeatable acquisition suitable for on-site, community and cohort studies.
InputStandardised frontal photo
OutputFaceAge / age gap
StrengthNon-contact, fast
Quality controlLighting / pose / makeup
Voice-derived digital phenotype
VoiceAge
Natural continuous speech undergoes audio standardisation, quality control and deep speech representation to produce an age-related phenotype. It is suitable for mobile, remote and low-burden on-site acquisition.
Input5–10 seconds of natural speech
OutputVoiceAge / age gap
StrengthRemote-friendly
Quality controlLanguage / noise / device
About CN-Age360
Interdisciplinary research capability from model development to multicentre validation
CN-Age360 is built by researchers from medicine, public health, data science and related fields. The platform is supported by a growing methodological foundation in population and longitudinal research, molecular ageing, AI medical imaging, multimodal data and cross-centre external validation.
Population and longitudinal research
Cohort and repeated-measurement studies on healthy ageing, chronic disease, frailty, functional change and long-term outcomes.
Ageing biomarkers
Experience in applying and comparatively validating DNA-methylation ageing algorithms and other biomarker research.
AI imaging and multimodal data
Covers ultrasound, CT, face, voice and low-burden sensor signals, with emphasis on quantification and reproducibility.
External and multicentre validation
Focuses on cross-device, cross-centre, independent validation and real-world research workflows—not only performance on a single training set.
Selected publications
Selected research
Previous work demonstrates methodological experience in ageing biomarkers, frailty and long-term health outcomes. These studies form part of the academic foundation of the platform, but do not mean that every current digital phenotype and imaging module has already completed clinical validation.
Nature Communications · 2022
Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults
Development and validation of an epigenetic frailty risk score.
View paper →EBioMedicine · 2021
Comparative validation of three DNA methylation algorithms of ageing and a frailty index in relation to mortality
Comparative validation of DNA-methylation ageing algorithms and mortality risk.
View paper →eClinicalMedicine · 2022
Association of longitudinal repeated measurements of frailty index with mortality
Repeated frailty-index measurements and mortality risk.
View paper →Continue exploring CN-Age360 from Fuxing Park
If you arrived here by scanning a QR code on site, you are welcome to continue exploring digital phenotypes, medical imaging AI and how we validate models. The website will remain available after the event.
Frequently asked questions
Scope of use
The site aims to clearly separate “research tools” from “clinical diagnosis”. Current outputs are primarily for research, public science experience and clinical research support.
Are FaceAge / VoiceAge biological age?
They should not currently be interpreted that way. They are age-related digital phenotypes derived from different modalities and require external validation against molecular, functional and long-term outcomes.
Can ultrasound AI directly replace physicians?
No. The platform is positioned as a tool for automated measurement, quantitative analysis and decision support; final imaging diagnosis and clinical management remain the responsibility of licensed physicians.
Are the paediatric reference values already formal clinical thresholds?
The current page mainly demonstrates the technical framework. Formal reference ranges require adequate sample sizes, multicentre data and prespecified modelling and validation plans before publication.