FAQ

SenoClock Gold needs users to register using a unique link that will be shared via email. Once logged in, they can take a psychological survey to improve their mental resilience and wellbeing. Regular Blood Reports can be uploaded to receive biological age prediction and personalised recommendations to improve health.

SenoClock Gold brings aging clock technology to the end users and allows a novel approach to improve physical and mental health. Health parameters can be tracked over time using simple dashboards. Detailed reports and actions can be journaled and maintained within the platform. Our AI-powered recommendation engine represents an advanced and comprehensive solution for not only tracking aging progress but also facilitating wellness improvements.

We are open to that. There’ll be a customization cost, but the platform can be tailormade for your business.

The platform offers two levels of analytics at this stage, at a user level where all relevant health parameters are provided in neat dashboards and at an organization level where aggregate analytics is offered for the entire population of users allowing the organization or the enterprise to effectively measure health trends.

Every implementation is treated in a unique manner. We will create custom plans for deployments. Deep Longevity offers standard support plans which can be modified based on business needs.

Deep Longevity prioritizes the security of personal data and personal information. We’ve data encryption, access control, and ongoing monitoring in place. The entire platform is hosted on AWS and benefits from top-of-the-line infrastructure.

SenoClock Gold API offers effortless integration with your existing healthcare systems, electronic health records (EHRs), and other health-related databases, ensuring a smooth and cohesive data management experience.

Our AI models use information from all the blood biomarkers in a clinical panel to tell how old a person is. Our aging clocks learn to recognize patterns in these panels associated with particular aging groups to predict biological age.

The AI system can generate highly customized health insights and recommendations based on age and gender and individual’s health status. These insights are used to develop personalized wellness plans that address areas such as nutrition, exercise, lifestyle, and other preventive health measures like use of supplements.

Aging clocks are currently offered to corporations and businesses, providing comprehensive health insights for organizational wellness programs, employee support, and customer engagement initiatives.

Yes, there is a trial version available for testing the functionality of the aging clocks. For more information, please reach out to our support team at info@deeplongevity.com.

The commercials are standard for all our customers but based on the scale of the operations, Deep Longevity can create custom pricing.

Using APIs and secure data transfer protocols, we facilitate the smooth integration of our service, enabling comprehensive data synchronization between our platform and your existing healthcare infrastructure. Our dedicated implementation and support team provides thorough guidance and assistance throughout the integration process.

To access the API, please send an email to info@deeplongevity.com. Our technical team will provide you with the necessary documentation and guidelines to get started with the integration process.

Blood Age uses regular clinical blood reports to predict biological age. Blood reports can be from any lab, any country or region, any language and format. Deep Longevity algorithms can handle all that complexity and using those inputs generate a blood summary report that predicts biological age, outlines which blood biomarkers are aging and anti-aging and gives recommendations to improve longevity and health.

Aging is the process of damage accumulation spread across decades. Due to cross-individual differences in genetics, lifestyle, and medical history, each person accrues aging-related damage at a different rate. While chronological age determined by one’s date of birth is a useful proxy for the amount of this type of damage, it fails to capture the variability in the true, biological aging rate.

Biological age may be understood as a golden standard of the aging process developed by measuring its molecular footprints. Expressed in the same units as chronological age, years, it offers a more personalized approach to longevity. While one cannot reverse the time to decrease their chronological age, it is absolutely possible to reduce the biological age and become a younger version of onseself.

Featured in a number of peer-reviewed academic studies on aging, Blood Age has been proven to be a better predictor of mortality than chronological age in Asian, European, and North American populations. The increased pace of aging has been shown to be associated with a variety of health conditions and mortality.

You may read more about Blood Age research in our Science Section.

Blood Age is not a certified diagnostic tool and was developed to assist healthcare professionals provide personalized care and advice to their patients. Physicians may employ Blood Age to provide patients with a unique longevity-focused perspective on their health situation and differentiate their practice in the marketplace.

We believe that professional expertise is the best approach to medicine, so we advise using Blood Age-generated reports in supervised settings only.

Along with biological age, Blood Age also reveals the main factors contributing to one’s aging rate and possible solutions or treatments to decrease it.
Professional supervision is advised in conjunction with the automatically generated recommendations.

Blood Age creates numerous digital lookalikes of the original submitted blood panel with slight variation and searches for a configuration of blood biomarkers that yield the greatest improvement in terms of biological age. The exact method we are using is called “particle swarm optimisation”. In the end, Blood Age checks a curated database of evidence-based interventions that can result in the desired blood biomarker configuration.

The efficiency of the recommendations applied greatly depends on how healthy your lifestyle was before trying Blood Age. Generally speaking, people with an increased pace of aging can expect the greatest benefits by following the recommendations provided by our system. If you have previously delved into biohacking territory, your biological age gains might not be as significant since you might already be following the recommendations in our database. Instead, your focus should be on monitoring the pace of aging and trying to align it with the recent changes in lifestyle.

To be more specific, our recommendations can help a fast ager become a slow ager by erasing years of biological age and allow a slow-ager to maintain that pace and even improve it.

Yes. Blood Age recommendation database has some possible dietary supplement solutions.
Expert supervision is strongly advised before you start a new supplement course.

Deep Longevity may use patient data for further studies and research projects. It helps us develop better tools to improve human health and longevity.

We use state-of-the-art cybersecurity technology and encryption to secure all patient data. The platform deletes all data after 72 hours unless needed by the customer. Further, the information is protected by international and local laws.

Visit Deep Longevity’s aging clock platform: SenoClock.

Blood Age Summary reports can be generated within a few minutes. Please check your internet connection and reach out to our support team if you run into any problems.

We offer training seminars to hospital staff on how to use Blood Age in their practice. The user interface is quite intuitive a. And the training doesn’t take more than one hour. For more information, watch our YouTube live demos.

There is no limit on the number of reports. You can generate as many reports as you need in one day.

Yes. Our API can be used to integrate Deep Longevity with any program. Contact info@deeplongevity.com to request a technical meeting so we can help you set up programmatic access to Blood Age or any other aging clocks.

Yes. The aging clock platform, SenoClock, comes with a robust API. It has more features compared to the GUI version and is the best solution for companies who wish to build their own longevity medicine platform.

Version 1.0 of the Blood Age Report offers a comprehensive analysis of 45 essential biomarkers and organ health scores for four vital organs. In contrast, Version 2.0 includes an additional 10 biomarkers, expanded organ health scores for eight key organs, and personalized health plans with weekly diet, exercise, supplement, and lifestyle recommendations. This upgrade provides users with more detailed insights and tailored guidance for optimizing their health and well-being.

Yes, there are differences in the types of organ health scores provided between Version 1.0 and Version 2.0 of the Blood Age Report.

In Version 1.0, the organ health scores focus on four vital organs: kidney, liver, glucose, and lipid health. These scores are derived from a comprehensive analysis of relevant biomarkers associated with these organs.

However, in Version 2.0, the organ health scores have been expanded to include detailed assessments for eight key organs: immune, bone, inflammation, glucidic, cardiovascular, mineral, kidney, and liver health. This expanded evaluation offers users deeper insights into the functionality and condition of their vital organs, enhancing their ability to make informed decisions about their health and well-being.

The inclusion of additional biomarkers in Version 2.0, such as C-reactive protein (CRP) and Vitamin D, along with expanded organ health scores for eight key organs, provides a more holistic assessment of health compared to Version 1.0. This comprehensive analysis offers deeper insights into various physiological processes and organ functions, enabling users to gain a more nuanced understanding of their overall health status and potential areas for improvement.

If you’re currently using Version 1 and wish to upgrade to Version 2, please reach out to us at: support@deeplongevity.com.

If the report includes fewer biomarkers, the report will still be generated successfully. However, the accuracy of the organ health scores may vary depending on the number of biomarkers provided.

Human motivations and emotions evolve as people grow older. Some of these psychological trends are so reliable that they may be used to guess one’s current chronological age. Psychological age is thus one’s position in this array of psychological transformations expressed as years of age.

Due to a variety of personal histories and innate tendencies some people go through their aging-related psychological progression faster than others. Unlike biological age, it is not always better to have lower psychological age. One’s life goals and priorities determine if their current psychological age is suboptimal: some people would benefit from adopting a more mature outlook while others might need to become younger at heart.

Good mental health is an essential part of healthy aging healthy aging. Our research has shown that poor psychological health acelerates one’s biological pace of aging. Some people might actually improve their mental and physical health by adopting a more mature mindset.

Mind Age, as well as other aging clocks developed by Deep Longevity, have been published in peer-reviewed academic journals .

Mind Age is not a diagnostic tool. However, it may be able to assist healthcare professionals for the purposes of initial assessment of psychological state and further progress by monitoring the response of patients to our psychological questionnaire in guided therapies . Some itFuturSelfrations of Mind Age, such as FuturSelf, may also be used as a self-improvement tool without professional oversight.

Our recommendation engine provides personalized tips self-improvement based on an individual’s current mental state assessed with Mind Age.

Our Mind Age project is an ensemble of deep learning models trained to predict one’s chronological age, mental health risks, and future well-being.

By processing thousands of psychological profiles obtained from public sources such as MIDUS and NHANES with deep learning algorithms, we have created these tools that may be used to gain useful insights on users’ mental health. Making these models available through projects like futurself.ai has allowed us to collect tens of thousands of psychological profiles that we have used to fine-tune Mind Age models.

Currently, the Mind Age project consists of deep neural networks, self-organizing maps, and clustering algorithms that exchange their outputs to parse aging-related trends in human psychology and determine the best course of action for each users based on their background.

Self-organizing maps are the foundation of our Mind Age recommendation engine. It is a method of unsupervised learning that groups people on a 2D plane based on the similarity of their psychological profiles. The regions on this map are scored based on the associated risk of mental illness, personal preference, and level of mental well-being. Then, the adjacent high scoring region is picked as the destination point for a user. Once the itinerary for a new user is composed, a curated database of psychological advice is sought for entries that can promote the desired shift in the psychological profile.

Mind Age offers the tools that may be used for self-improvement with no professional guidance. However, we believe that AI-tools cannot replace the nuanced attention a human expert can provide to their patient through one-on-one sessions.

Mind Age Summary reports can be generated within minutes after completing the survey. Our team is always there if you run into any problems.

Deep Longevity may use anonymized patient data for further research and fine-tuning of its deep learning models.

We use best-in-class security practices and encryption to secure patient data. All the information-handing is compliant with international and local laws.

The Mind Age aging clock, as well as all the patient data, is stored on Amazon AWS servers.

As of December 2022, Mind Age can be accessed by our commercial partners. Mind Age is currently being integrated into our B2B portal SenoClock.AI and is expected to be released as an extension to it in Feb 2023.

For further inquiries on the commercial availability of Mind Age, reach out to us at support@deeplongevity.com

Yes. We can provide a trial account for hospitals and other businesses that may want to implement Mind Age for their patients, customers, or employees.

Please write to us at support@deeplongevity.com. Our team will connect you with an expert as soon as possible.

Yes. Mind Age can be accessed via API to enable the development of third-party tools and plug-ins for existing software.

Mind Age’s throughput is practically uncapped as it is hosted on the cloud. If you operate at a large scale, please contact us for a special arrangement.

Access is available through secure APIs and Deep Longevity’s SenoClock SaaS portal, offering convenient online usage hosted within your specific region.

The platform is tailored to offer personalized health insights for non-pregnant individuals aged 20 to 65 years.

Yes, maintaining the privacy and confidentiality of user data is a top priority. The platform adheres to strict security measures to safeguard personal health information.

The platform typically accepts standard blood reports with our listed parameters. Ensure that the uploaded report is recent and accurately reflects your current health status.

The Blood Age clock needs 45 Blood Parameters to predict your biological age, while the Biometrics Age clock only needs 17 Blood Parameters and 4 Biometrics.

Chronological age represents the number of years a person has lived. Epigenetic Age, however, reflects the functional and molecular condition of the body.
An individual’s Epigenetic Age may be lower or higher than their chronological age, indicating slower biological aging or accelerated aging, respectively.

Epigenetic Age is calculated using scientifically validated machine-learning models trained on large population and longitudinal datasets. 
These models analyze DNA methylation data from biological samples and convert complex molecular patterns into an age estimate that correlates with health outcomes, disease risk, and mortality. 

Epigenetic Age analysis requires a DNA-based biological sample using blood sample. 
The specific sample type depends on laboratory workflows, regulatory standards, and regional requirements.

Epigenetic Age is among the most scientifically validated biomarkers of biological aging.
DNA methylation–based aging clocks demonstrate a high level of accuracy, with a typical prediction error of approximately 2.77 years, placing them among the most precise epigenetic aging models currently available.

Epigenetic Age is designed as a decision-support and wellness analytics tool.
It is not a diagnostic test and should always be interpreted alongside clinical evaluations by qualified healthcare professionals.

Artificial intelligence and machine-learning models identify complex, non-linear relationships between epigenetic markers and aging outcomes.
These models continuously improve as new population-level and longitudinal data are incorporated, enhancing prediction accuracy and real-world relevance.

Scientific evidence suggests that epigenetic patterns are dynamic and can respond to positive lifestyle changes.
Factors such as improved nutrition, regular physical activity, stress reduction, quality sleep, and healthier environments may slow or partially reverse epigenetic age acceleration over time.

Epigenetic Age is typically measured at intervals of several months.
This timeframe allows sufficient biological change to occur following lifestyle or therapeutic interventions, making trend analysis more meaningful.

Once laboratory processing and data ingestion are complete, Epigenetic Age reports are generated automatically by the Deep Longevity platform.
Turnaround time depends on laboratory workflows and system integration, but is generally short.

All epigenetic data are processed using secure, encrypted infrastructure that complies with global data-protection standards.
Personal identifiers are removed and anonymized data is processed along with strict privacy controls, access management, and regulatory-compliant data-handling policies.

Yes. Epigenetic Age insights are available through secure APIs, enabling seamless integration with healthcare platforms, wellness applications, research systems, and enterprise longevity solutions. 

Epigenetic Age solutions are designed for: 

• Healthcare providers 

• Research institutions 

• Wellness and digital health platforms 

• Insurers and employers 

• Longevity-focused organizations 

They support data-driven strategies for healthier aging and preventive care.

Epigenetic Age is influenced by DNA methylation patterns shaped by lifestyle and environmental exposures.
Key factors include stress, physical activity, sleep quality, nutrition, smoking, alcohol use, and environmental toxins.
These influences can accelerate or slow biological aging at the cellular level.

A genetic DNA test analyzes inherited DNA sequences that remain largely unchanged throughout life.
An Epigenetic Age test, by contrast, measures chemical modifications on DNA that regulate gene activity and change over time in response to lifestyle and environment.

Epigenetic clocks provide a dynamic view of how the body is aging biologically—beyond genetic predisposition alone.

Epigenetic Age testing is based on decades of research in molecular biology, epigenetics, and population health.
DNA methylation patterns have been shown to change predictably with age and to correlate strongly with health outcomes, disease risk, and longevity.
By combining these biomarkers with AI-driven models, Epigenetic Age provides a robust, science-backed measure of biological aging.

AI skin analysis is a technology that uses artificial intelligence and computer vision to assess visible skin characteristics from a facial image. It evaluates features such as oiliness, dryness, redness, pigmentation, texture irregularities, fine lines, and other surface patterns that reflect skin condition and visible aging.

By analyzing these features objectively and consistently, AI skin analysis helps identify key skin-related factors influencing appearance and aging. The insights can then be used to guide personalized skincare strategies, wellness interventions, and product recommendations, supporting improved and measurable skin outcomes over time.

Chronological age measures the number of years lived, while Face Age reflects how aging appears on the face. Face Age may be higher or lower than chronological age, indicating accelerated or slower visible aging influenced by skin condition, lifestyle, and environmental exposure.

The Face Age Clock uses computer vision and proprietary AI-based facial analysis trained on large, demographically diverse datasets. The models examine facial structure and detailed skin features, translating visible aging patterns into a biological age estimate that reflects an individual’s facial aging progression.

In addition to facial structure, the Face Age model evaluates multiple skin-related parameters, including: 

• Skin Quality  

• Wrinkles 

• Pores 

• Oiliness 

• Dryness 

• Elasticity 

• Hydration 

• Skin Appearance 

• Dullness 

• Sagging 

• Radiance 

• Texture Irregularities 

• Fine Lines 

• Eyes   

• Dark Circles 

• Bags Under Eyes 

• Blemishes  

• Acne 

• Black/Dark Spots 

• Dark Spots 

• Redness 

• Uneven Skin Tone 

• Line on Forehead 

• Facial Hair 

These features contribute directly to the Face Age estimation and aging profile. We will keep adding additional features over time.

AI models analyze high-resolution visual patterns, contrast variations, and structural cues within the skin. This allows the detection of subtle features—such as oil imbalance, redness, or pigmentation—that may not be consistently identified through manual observation.

AI ensures accurate face analysis by using advanced computer vision and deep-learning models trained on large, diverse, and demographically representative datasets. Automated quality checks verify image clarity, lighting, alignment, and facial positioning before analysis. 

The models then evaluate multiple facial and skin features simultaneously, identifying consistent patterns linked to visible aging and skin condition. Continuous validation, performance monitoring, and model refinement help maintain reliability and consistency across different populations and imaging conditions. 

By identifying specific skin-related aging drivers, Face Age insights can be used to design targeted skincare, cosmetic, or wellness strategies. These outputs support more precise and personalized interventions aimed at improving visible skin health over time.

Yes. Face Age highlights skin features that contribute to accelerated visible aging, many of which are modifiable through appropriate skincare routines, lifestyle changes, or professional interventions.

Yes. Face Age is dynamic and may change as skin condition and facial features respond to improved care, reduced stress, better nutrition, or environmental changes. Repeated assessments enable longitudinal tracking of visible aging trends. 

Face Age can be measured regularly due to its non-invasive nature. However, noticeable changes in facial aging and skin patterns typically emerge over weeks or months rather than days. 

The accuracy of results also depends on image quality – clear, well-lit images with proper face positioning enable more precise analysis and more reliable outcomes. The clock will reject images of poor quality.

A single facial image captured under defined quality guidelines—proper lighting, neutral expression, and frontal orientation—is required. No biological samples or invasive procedures are involved.

Face Age models are developed and validated using large-scale datasets and rigorous statistical methods. While the insights are scientifically grounded, Face Age is intended for wellness, research, and decision-support applications, not medical diagnosis.

All facial data is processed using secure, encrypted infrastructure and handled in compliance with global data-protection and privacy standards. Consent is duly recorded.

Yes. Face Age is available through secure APIs, enabling integration with wellness platforms, skincare solutions, research tools, and enterprise longevity systems.

Unlike cosmetic skin analyzers, the Face Age Clock is grounded in longevity and proprietary AI facial analysis to estimate how your face is aging compared to your chronological age. It integrates skin features as biological aging signals rather than aesthetic metrics, enabling consistent tracking, interpretability, and long-term aging assessment.

Traditional skin assessment methods rely on subjective visual checks, generalized skin types, or trend-based assumptions. These approaches fail to account for individual skin variability, leading to trial-and-error decisions and inconsistent results.

Face Age replaces subjective judgment with AI-driven analysis of facial and skin features, enabling more precise and repeatable insights.

Face Age identifies specific skin-related aging drivers, such as oil imbalance, dryness, pigmentation, redness, and texture irregularities—rather than relying on generic skin categories.
By understanding why certain skin features appear, users and brands can make targeted, data-informed decisions instead of experimenting with unsuitable products or routines.

AI enables Face Age to analyze multiple facial and skin parameters simultaneously and objectively. This allows personalization to be based on measurable skin features and visible aging signals, rather than assumptions, preferences, or popularity-driven trends.

Face Age enables brands to: 

• Provide targeted products for proper skin care 

• Improve product-to-skin matching accuracy 

• Increase customer trust and satisfaction 

This transforms personalization into a measurable, repeatable process. 

Unlike cosmetic tools or self-reported questionnaires, Face Age is: 

• AI-driven and data-based 

• Focused on biological and visible aging signals 

• Designed for consistency, validation, and longitudinal tracking 

It delivers actionable insights rather than superficial categorizations. 

Yes. Face Age is purpose-built for online and digital-first experiences. It delivers accurate facial aging insights using a single facial image, making it ideal for web platforms, mobile applications, and enterprise-scale personalization where fast, scalable, and remote analysis is essential.

Face Age is ideal for: 

• Skincare and cosmetic brands 

• Wellness and longevity platforms 

• Digital health, beauty and e-commerce businesses 

• Research and innovation teams 

Deep longevity’s APIs can be accessed using JWT or API key.

Yes, APIs are secure and necessary security measures are already in place. Here are the few,

APIs are accessible via HTTPS only which ensures encryption for secure communication over the network.

Standard authentication mechanisms are implemented to access APIs like JWT & API Key. Validity for API key can be controlled during the API key creation.

APIs are 100% REST endpoints, and all possible vulnerabilities are managed including SQL injection.

Proper input validations are in place.

Permissions and authorization implemented.

Information about available endpoints is accessible with a valid customer only. To know more about the APIs please contact us at support@deeplongevity.com

Available APIs are 100% REST APIs and request/response body contains JSON data only.

Rate limits varies customer to customer based on the agreed contract and plan.

In case of rate limits are exceeded the requests will be waiting in a queue and executed in FIFO (first in first out).

Errors are communicated by the API through HTTP status codes.

Here are some following status codes and error messages,

400 Bad Request: The server cannot process the request due to a client error (e.g., malformed request).

401 Unauthorized: Authentication is required and has failed or has not been provided.

403 Forbidden: The server understood the request but refuses to authorize it.

404 Not Found: The requested resource could not be found.

500 Internal Server Error: A generic error message returned when an unexpected condition was encountered.

Developers will be provided with necessary documentation and validation details during onboarding.

Yes, APIs are documented using Swagger (Open API 3.0 specification). Along with this postman collection is available to test and understand all available APIs.

Once successfully onboarded a developer account will be created in our test server and shared with the developer/team.

Most of the APIs return quick response. For asynchronous processes APIs are available to check current process status.

Users will not be blocked by the asynchronous events. After initiating the event API will return some process/tracking id and using the id user check the status/results.

In API development, data formatting in requests and responses is crucial for effective communication between clients and servers. All requests and responses are in JSON format.

Yes, versioning is supported, and user can specify the version during APIs calls. Without versioning APIs will always return latest version.

No, it’s not required. All communication will happen using REST API.

Only requirement is that all requests should be a REST API call.

Currently monitoring is managed by our internal team only. Developers will not have access to this. A monthly report will be shared based on the usages.

In case of any performance or API issues, developers can ask support to check by sending email to support@deeplongevity.com.

We don’t need any PII to process your requests. So, we will not be accepting or storing any shared PII data. Developers are required to send deidentified data only.

Scalability of API is available based on the requirement of the user and agreed terms.

It’s recommended to maintain the agreed max number of concurrent requests to get the best performance.

Developers can reach out to our support team at support@deeplongevity.com. Our team will get back with possible causes and solutions.