Decentralized, Privacy‑Preserving Cervical Cancer Screening

TL;DR

• What: Ratio1.ai proposes a mobile-first, privacy-preserving pipeline for cervical-screening support (TZ type and lesion classification) built on decentralized storage (R1FS), federated learning, and homomorphic-encryption based training and inference (EDIL framework), with edge nodes serving models locally even offline when moved to remote areas. 

• Why it matters: Cervical cancer remains a major global burden; scalable early-screening support tools can help close access gaps, especially in low-resource settings. 

• Data stays private: Training occurs without centralizing patient images; nodes exchange encrypted/encoded updates, not raw data and thus reducing exposure risk vs. conventional centralized pipelines. 

• Auditable & trustworthy: Permissioned participation plus on-chain traceability provide tamper-evident logs of dataset and training operations for regulatory and clinical audit. 

• Grounded in open data & OSS: Pipeline development leverages open datasets and open-source models, enabling reproducible research and spin-off apps. 

• Call to action: Clinicians, researchers, and developers can collaborate on extending the pipeline (e.g., new tasks, locales) while preserving data sovereignty and enabling verifiable deployment.

Introduction

At Ratio1, we continually build new templates, frameworks, and ready-to-deploy white-label applications to help companies, developers, researchers, and content creators bring end-to-end products to market lightning-fast. Following the alpha release of the RedMesh framework and the preview of AI-for-Everyone, we’re turning our attention to an extremely sensitive domain - one where our core ethos, Your AI, your Data, is paramount: healthcare. Our latest initiative in this space is CerviSafe, a decentralized, privacy-preserving AI framework for cervical cancer screening. The proof-of-concept (PoC) for CerviSafe has officially been added to our September roadmap (R1-445), marking a key milestone in demonstrating how privacy-first, edge-enabled AI can transform global health access.

Cervical cancer remains a preventable yet deadly disease, especially in low-resource regions. Early detection through screening can save lives. In practice, one key step is identifying the cervix’s transformation zone (TZ) type from a visual exam - a task requiring expertise. Women with a “Type 1” cervix (fully visible TZ) can usually be cleared with routine procedures, whereas “Type 2” or “Type 3” cervixes need more intensive follow-up. Unfortunately, many clinics in underserved areas lack the time or specialists to make these classifications, resulting in missed opportunities for life-saving intervention. Similarly, visually detecting precancerous lesions during screening is challenging and subjective, contributing to variability in care.

Artificial intelligence (AI) offers a promising assistive tool here. Modern AI vision models can learn to classify TZ types as well as detect (i.e. classify) suspicious lesions on cervical images, potentially standardizing care and expanding access. In fact, research shows that AI algorithms can outperform traditional HPV tests and pap smears in identifying cervical precancers from images. To accelerate such innovations, organizations like IARC are curating large cervical image datasets - e.g. an ongoing image bank of over 56,000 cervix images open to researchers worldwide.

However, deploying AI for cervical screening at scale also raises practical and ethical challenges. How do we bring powerful models to remote clinics and mobile devices? How do we ensure patient data privacy when aggregating sensitive medical images for model training? And can we prove trustworthiness of AI recommendations in a clinical setting? At Ratio1.ai, we have built a solution addressing these challenges: a decentralized AI training pipeline for cervical cancer prevention that keeps “Your AI, your Data” at its core. In this post, we outline how our approach - combining homomorphic encryption, decentralized federated learning, and private decentralized storage - enables mobile-friendly AI for classifying transformation zone types and lesions, all while preserving privacy and trust through blockchain technology.

The Challenges of Cervical Cancer Screening in Remote Areas

“...democratize AI for cervical cancer prevention - giving clinics, clinicians, medical personnel and also non-medical people a tool to classify TZ type and detect lesions on the spot and more importantly on the edge…”

Implementing cervical cancer screening in low-resource settings faces several hurdles:

• Limited Specialist Access: Trained colposcopists or cytologists are scarce in many regions. Visual screening methods like VIA (visual inspection with acetic acid) have subjective results and variable accuracy, especially when providers lack experience. Transformation zone classification and lesion recognition can be inconsistent, leading to missed lesions or unnecessary referrals.

• Data and Privacy Concerns: AI models improve with data, but medical images are sensitive. Clinics and hospitals are often reluctant to share patient data with third-party AI services or central cloud servers due to privacy regulations (e.g. HIPAA, GDPR) and patient trust issues. A traditional approach of uploading thousands of cervical images to a cloud for AI training or inference is risky for privacy and may be infeasible in places with poor connectivity.

• Infrastructure Gaps: Rural clinics may not have reliable internet or advanced hardware. Solutions must run on low-powered devices (even phones or small edge computers) and handle intermittent connectivity. Moreover, many projects remain pilot-stage because scaling out an AI system to numerous dispersed sites is complex if it relies on centralized computing.

Our mission was to democratize AI for cervical cancer prevention - giving clinics, clinicians, medical personnel and also non-medical people a tool to classify TZ type and detect lesions on the spot and more importantly on the edge, without surrendering control of their data. To do this, we leveraged Ratio1’s decentralized AI platform principles: “Your AI, Your Data” ownership via R1FS, an Encrypted Decentralized Inference and Learning (EDIL) framework for training on encrypted data, a network of edge nodes for local processing, and blockchain-based orchestration for trust and transparency. All components are built with open-source models and standards, so researchers and developers can freely build upon or integrate them into real-world solutions.

“Your AI, Your Data” under the hood

A cornerstone of our approach is ensuring that clinics and users retain full ownership of their data. Ratio1’s answer is R1FS, our private, IPFS-based file storage system integrated with the Ratio1 network. R1FS allows data to be stored and shared in a fully decentralized manner, with no single server holding all the information. All files are encrypted with keys owned by the user or node, so no central entity ever sees raw patient images - enabling a truly “Your AI, your Data” paradigm.

In the context of our cervical screening pipeline, each participating clinic or individual or node keeps its own cervix images (and any annotations) in its local R1FS storage. When the AI model needs to train or update, the raw images never leave the premises. Instead, what gets shared are securely derived data artifacts (more on this below) or model parameters - and even those are exchanged through R1FS in an encrypted form. R1FS acts as a peer-to-peer data backbone for our federated learning: multiple nodes can publish and retrieve shared model updates or results, with content addressing (via R1FS/IPFS CIDs) ensuring the right data finds the right nodes. Crucially, this happens without exposing any private images or requiring a central database. As our developers describe it, “Multiple nodes can securely exchange partial model weights or aggregated learning statistics via R1FS, ensuring each node’s raw data remains private. This opens doors for collaborative machine learning projects across different organizations without compromising individual data sovereignty”. In short, R1FS gives us a decentralized data layer where clinics collaborate on AI while each retains control over its sensitive data.

“...no central entity ever sees raw patient images  enabling a truly “Your AI, your Data” paradigm…”

With EDIL, all training computations occur on encrypted or encoded data, so that nodes (and even the coordinating server, if one existed) never see each other’s raw inputs. Our Ratio1 EDIL framework leverages advances in homomorphic encryption and secure distributed computing to keep data encrypted end-to-end during training. “Our Ratio1 EDIL framework enables homomorphic encrypted learning and serving, allowing computations on encrypted data without ever exposing the raw inputs,” as noted in our whitepaper. This means even if multiple hospitals are training a model together, each image remains encrypted (or transformed in a one-way fashion resulting in a lower dimensionality image embedding) when it’s being processed by the AI. Traditional methods would require decrypting data at some point for model training - a vulnerable step. By contrast, our approach uses DFHE (Decentralized/Fully Homomorphic Encryption) mechanics to keep data encrypted throughout the entire training process, greatly reducing exposure risk. This is achieved via the “Domain Auto-Encoder” strategy within EDIL. Before data leaves a node (for aggregation or for another node’s computation), it’s passed through a trained (few shot or pre-trained for similar domains) auto-encoder that compresses and scrambles essential features of the image. This encoder is specific to the problem domain (in this case, cervix images) and acts as a one-way transformation - much like a specialized cipher for images. The Ratio1 training coordinator can then use these encoded features to assemble a global model update, without ever needing to decrypt them back into the original image pixels. Each node’s contribution is effectively an encrypted puzzle piece**: useful for model training when combined, but meaningless if intercepted in isolation. This strategy strikes a balance between secrecy and practicality, reducing the heavy computational overhead of purely homomorphic operations by working on lower-dimensional encoded data.

Secure On-Spot Screening

A big advantage of Ratio1’s decentralized design is that AI doesn’t live in a cloud data center - it lives on the “edge” nodes. An edge node in Ratio1 can be any machine from a cloud VM to a hospital’s local server, a laptop, or even a mobile device (within a specific set of compatible devices). This is particularly important for bringing AI assistance to remote and resource-limited settings. Our Ratio1 Edge Node (REN) software can turn heterogeneous hardware into part of the AI network: “Ratio1 Edge Nodes (REN) transform heterogeneous devices - ranging from desktops and servers to mobile and virtual instances - into secure, remotely managed compute units.” In practical terms, a rural health post could run a Ratio1 edge node on a modest PC or an arm64 device with or without an attached mobile colposcope.

Trustless Security and Blockchain Traceability

In healthcare, trust and accountability are paramount. Both clinicians and patients need confidence that the AI system is secure, has not been tampered with, and operates under agreed policies. Here, Ratio1’s use of blockchain provides a unique advantage. All our training and inference workflows are underpinned by a permissioned, yet trustless network architecture.

“...only permissioned nodes (e.g. legitimate clinics, operators, individuals, etc) participate, every action is verified in a trustless manner, and the history of model training and usage is tamper-proof with immutable trace…”

Permissioned nodes

To ensure only authorized participants (e.g. certified clinics, vetted research partners) can join the training network, Ratio1 uses a Node Deed licensing mechanism. A Node Deed is a non-fungible token (NFT) that grants a node permission to participate. Only nodes presenting a valid deed can contribute data or receive jobs. This keeps bad actors out and enforces a level of governance - essentially a membership system for the decentralized network. The Node Deeds are tied to real-world identity (through a KYC’d or KYB’d wallet) and a specific node instance, providing clear traceability of which organization operated which node.

Trustless technology

Even with permissioned entry, we don’t want to have to trust any single node or even a central authority. Ratio1 employs an adaptive Byzantine-fault-tolerant consensus (OracleSync and ChainDist) across special oracle nodes to validate every important action in the network. The oracle network gathers job execution and even overall node behaviour information and reaches consensus (using a aPBFT algorithm) to confirm the results before accepting them. This means the system can detect malicious or malfunctioning nodes (e.g. sending bad model updates, serving invalid inferences, etc) and ignore them, without a centralized moderator. All node states and outputs are verified in a decentralized, tamper-evident way - truly trustless in the sense that participants don’t need to blindly trust each other or a cloud service; the network rules and cryptography enforce honesty.

Immutable audit trail

Every training round, model aggregation, and even dataset usage can be recorded as a transaction on the Ratio1 blockchain (or an internal ledger). The platform logs each task (job) assignment, start, and completion immutably on-chain, providing full traceability and auditability. In practice, this means we have a verifiable log of how a particular cervical screening model was trained, even which data sources contributed (in an anonymized way), what parameters changed, and whether a mission had a specific record or number or patients. If a regulatory body or hospital review board wants to audit the AI’s development, we can point to an immutable record of the training operations. Even for individual predictions, blockchain logging can trace which node handled an inference and confirm it ran the approved model version, etc. This level of transparency, secured through blockchain, helps build trust in the AI system’s integrity. It also aligns with medical data governance - any access or computation on sensitive data is tracked in a ledger that cannot be altered retrospectively, reducing the risk of undisclosed data tampering.

In summary, blockchain integration adds a layer of security and trust on top of our decentralized AI: only permissioned nodes (e.g. legitimate clinics, operators, individuals, etc) participate, every action is verified in a trustless manner, and the history of model training and usage is tamper-proof with immutable traces. This gives peace of mind to stakeholders that the AI was built and is operating under robust oversight, despite being decentralized.

Open-Source Collaboration and Future Prospects

True to Ratio1’s philosophy, our cervical screening AI pipeline is built with open-source tools, models and pipelines. Making our pipeline open source means anyone can spin up their own instance - commercial or otherwise - or contribute to its improvement. A university research group could fork our code to experiment with a new model for lesion detection. A healthcare startup in another country could build on our pipeline to develop a localized cervical screening app, plugging in their own data while benefiting from our encrypted FL and deployment framework. Open sourcing this work encourages peer review, transparency, and faster innovation. It aligns with our belief that no single entity should monopolize AI solutions for global health problems. Instead, by sharing knowledge and code, we can collectively raise the bar for what AI can do in cervical cancer prevention.

“...anyone can spin up their own instance - commercial or otherwise - or contribute to its improvement…”

The Ratio1 platform itself is designed to be developer-friendly (with SDKs and templates) and community-driven. We invite developers and domain experts to join us in improving the AI models and expanding the capabilities of this pipeline. For instance, one could integrate an HPV testing module, or extend the system to other screening tasks (like detecting other pathologies from medical images) - all within the same decentralized, privacy-preserving framework. By keeping the system modular and open, we lower the barrier for others to create production-grade applications on top of our work, whether for research or real deployments.

Conclusion

With our decentralized, encrypted learning pipeline, AI-powered cervical cancer screening can be delivered anywhere - from a cutting-edge hospital to the most remote village - without compromising privacy or requiring heavy infrastructure. Each clinic or practitioner retains ownership of their data and control of the AI, embodying the Ratio1 ethos: Your AI, your Data. Homomorphic encryption and federated learning (EDIL) ensure that even as the AI learns from thousands of patients, no sensitive information is exposed. A network of lightweight edge nodes means that a nurse can get instant AI guidance during a screening, even offline, by leveraging computing power next door or a few villages over. And the blockchain backbone instills confidence that every step, from model training to prediction, is transparent and verifiable in a trustless ecosystem.

“...we welcome collaboration from all corners - technologists, healthcare workers, researchers - to further refine and (much more importantly) “Deeploy” this solution…”

This approach isn’t just theoretical - it’s being built now. We have demonstrated classification of transformation zone types and detection of cervical lesions using our pipeline on distributed data from multiple sources, all while keeping the data encrypted and decentralized. The results are encouraging: the model’s performance is on par with training on centralized data, but with far greater privacy and scalability. More importantly, the real-world impact could be immense. By deploying AI in a decentralized way, a network of small clinics could collectively achieve diagnostic accuracy that rivals big urban hospitals, helping to close the healthcare gap. Each additional node (clinic) that joins the network not only benefits from the global model but also contributes to making it better - a virtuous cycle for continual improvement in cervical cancer prevention.

We believe this is a paradigm shift in how critical healthcare AI solutions can be delivered. Decentralization, privacy, and openness form the trifecta that can empower doctors and patients everywhere. A future where no woman is denied a life-saving screening due to her location or privacy concerns is within reach, if we harness technology in a thoughtful, human-centric way. At Ratio1.ai, we’re committed to making that future a reality, and we welcome collaboration from all corners - technologists, healthcare workers, researchers - to further refine and (much more importantly) “Deeploy” this solution.