Revolutionizing Radiology with Tokenized and De-Identified Imaging Data


Radiology, or diagnostic imaging, consists of procedures that take and process images of different parts of the body. Radiology, in its various processes, offers the means to visualize internal organs. Imaging modalities employed in healthcare include X-rays, MRIs, ultrasounds, CT scans, mammography, nuclear medicine, fluoroscopy, bone mineral densitometry, and PET scans.

Radiology plays an important role in the management of diseases, as it provides a variety of methods for the detection, staging, treatment planning, and monitoring of diseases. Radiology helps in the early detection of cancer, neurodegenerative, and cardiovascular diseases. And early detection further gives way to planning for early intervention and better outcomes. Imaging is also critical for planning surgery, radiation therapy, and other treatments since it gives accurate images of the targeted area.Additionally, radiology enables the monitoring of patients' health and journey. 


Monitoring the course or remission of diseases and determining the effectiveness of treatment, e.g., tumor regression in cancer treatment, has become possible because of imaging. Also, radiological results supply information for research studies, which aids in examining the efficacy of treatments and comparative studies. Imaging also aids in comprehending the natural history of disease, and formulating new diagnostic and therapeutic approaches.


Since large amounts of valuable imaging data are continually being produced, ensuring that it is interoperable is highly important for future R&D. Imaging data is commonly used for research and oftentimes carries sensitive Protected Health Information (PHI) and Personal Identifiable Information (PII). As such, it falls under legal regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). 


In the context of both regulatory and interoperability requirements, there needs to be a system. That system must be of a kind where secure retrieval and sharing of imaging data is possible. Tokenization fills this gap by substituting sensitive data with nonsensitive identifiers, or "tokens", protecting data while preserving its utility.

The need for tokenization and de-identification in medical imaging

Tokenization is used in databases or internal systems to replace sensitive data while keeping the actual data secure. But what exactly are tokens? 

Tokens are values that are unrelated to each other, yet they preserve certain characteristics of the original data, typically length or format, ensuring continuity in business processes. The original sensitive data is stored securely outside of the organization’s internal systems.

Tokenized data is non-reversible and non-decipherable. There is no mathematical relationship between the token and the original data. This means the token cannot be reversed to reveal the original information unless additional information is provided. 

Tokenization of Real-World Imaging Data (RWiD)

Imaging has now become a standard aspect of healthcare for diagnosing various diseases. It contributes to processes such as diagnosis, prognosis, therapy monitoring, and surveillance. And due to advancements in technology, acquiring, processing, and analyzing imaging datasets has become simpler. Without the analysis of RWiD, there will be limitations in the RWE and other types of studies. RWiD can assist in various stages of understanding diseases and drug development, whether used independently or combined with other dataset types (through tokenization).


RWiD aids in the discovery and examination of biomarkers while providing insights into the natural progression of diseases. During the trial phase, it can facilitate trial design and optimization, help with patient recruitment, and serve as an external control group. RWiD also enhances and generates more precise evidence following drug approvals.


‍Tokenization of RWiD is of greater importance than with respect to other categories of datasets, such as claims data. In contrast to typical datasets, RWiD often contains metadata that may contain PHI (Protected Health Information). Tokenizing such metadata is, thus, essential to maintain patient confidentiality and meet regulatory needs. Doing so makes it possible for organizations to exchange imaging data in a more efficient, secure, and compliant manner with various stakeholders. Also, tokenization facilitates interoperability, along with helping integrate different types of clinical data with imaging data.

How the process of tokenization works


  1. Data transformation
    ‍    The process starts with sending sensitive information, which includes personally identifiable information and personal health information, to an external tokenization service. The service translates the sensitive data into non-sensitive substitutes called tokens.

  1. Removal from internal systems
    ‍    Upon tokenization completion, sensitive information is thoroughly removed from the organization's internal systems and replaced with tokens. It ensures that no sensitive data exists in the organization’s environment, thereby enhancing the security of the data.
    ‍
  1. Operational use of tokens
    ‍    The tokens are assigned to the organization for operational purposes and have no mathematical relationship to the original data. In this manner, tokens assist in operations by facilitating continuity without accessing the sensitive data itself.

  1. Secure external storage
    ‍    The original sensitive data is stored securely by a third-party tokenization issuer outside the organization's systems.


Benefits of utilizing tokenized imaging data

Tokenized imaging data provides some important advantages in medicine, especially in the context of interoperability and privacy issues:

  1. ‍Multi-modal data integration
    Tokenization allows multiple datasets from different sources—such as imaging data, clinical records, and insurance claims—to be linked securely. This integration supports richer, more holistic insights by enabling researchers and clinicians to analyze information from various modalities without exposing sensitive patient identifiers. As a result, healthcare organizations can leverage a broader range of data to improve diagnosis, treatment planning, and population health strategies while maintaining strict privacy safeguards.
    ‍
  2. Strengthened privacy protection
    The tokenization process substitutes sensitive patient identifiers with tokens that are unique, so the data cannot be linked back to an individual. This increases patient privacy and minimizes the likelihood of data breaches. Tokenization also facilitates compliance with privacy laws like HIPAA and GDPR because it guarantees that sensitive patient data does not get exposed when transferring or using the data.
    ‍‍
  3. Enhanced interoperability
    Tokenization enables the application of standardized tokens among various stakeholders in the healthcare sector, allowing the exchange and sharing of imaging data across platforms. Healthcare professionals and researchers are able to share this tokenized imaging data securely without infringing on privacy.
    ‍
  4. ‍Reduced risk of data breach
    Since tokens replace sensitive information, even if the data is intercepted, it would be useless without access to the tokenization system. This significantly reduces the potential threat of data breach.
    ‍
  5. ‍Streamlined data management
    Tokenized data can be centrally stored, making it easier to manage and track. Tokenization simplifies access control, allowing for more efficient and organized data management.
    By relying on tokens instead of full datasets, healthcare organizations can reduce redundancy and improve the overall efficiency of data storage and retrieval.
    ‍
  6. ‍Facilitating research and data analysis
    Scientists can access tokenized imaging data without violating the privacy of patients. This way, worthwhile data can be utilized for insights and innovation while strictly preserving data privacy protections. Tokenized data can be connected between different datasets without disclosing any personally identifiable information, enabling more robust data analysis without privacy issues.
    ‍
  7. Scalability
    Tokenized imaging data is usable in a range of applications, from clinical diagnosis to research and healthcare models based on artificial intelligence. Such flexibility guarantees that healthcare systems can expand their use of data without fear of privacy and interoperability challenges.

The Segmed advantage

Tokenization is an important process that transforms the landscape of radiology imaging data, ensuring its optimum utilization. The tokenization of imaging offers a better understanding of the patient journey as it enables its integration with other clinical data. This accelerates the research process and innovation, and drives advanced analytics enabled through artificial intelligence.

At Segmed, we provide fit-for-purpose, regulation-grade imaging datasets for pharmaceutical, medtech, and AI research and development.

Connect with us to find out how our high-quality, diverse, tokenized imaging datasets, when integrated with other tokenized datasets, can support a myriad of research and development for healthcare innovation.

Comments

Post a Comment

Popular posts from this blog

Role of Computer Vision & Synthetic Data in Transforming Medical Imaging

At Segmed, we are excited to announce the release of our latest review at the Radiology Journal , exploring cutting-edge advancements in computer vision for medical imaging. This project was led by the Segmed team in collaboration with our friends from University of Washington Seattle, Harvard University, Stanford University, Microsoft, and University of San Francisco! Over the past decade, artificial intelligence (AI) has played a pivotal role in transforming healthcare, particularly in medical imaging, by enhancing techniques like image classification and segmentation. Our review highlights how the next frontier for AI, with large-scale generative models and synthetic data, is addressing key challenges in medical data sharing and accelerating innovation. In this blog article, we’ll dive into the core findings from our publication and explain why synthetic data could be the game-changer for the development of AI models in healthcare. ‍1. How Synthetic Data Can Fuel AI Model Developmen...
Read more

Unlocking AI Insights: Federated Learning Explained

Image
Although federated learning (FL) is becoming an increasingly popular form of machine learning, it is still considered a new domain. FL was only recently coined in 2016 when it was introduced in a paper published by Google AI called “Communication-Efficient Learning of Deep Networks from Decentralized Data.” As the name suggests, federated learning’s advantage point over other forms of machine learning is its ability to work using decentralized data. It is extremely important for decentralized data to be used—especially in the field of healthcare, where patient medical data is sensitive—in order to avoid privacy breaches or violations. Additionally, federated learning can be used to train data with rare cases, as it is a result of the large amounts of varying medical data from different data sources. When there is enough of this diverse data, the algorithms are able to identify more cases and give more accurate predictions and/or conclusions. Federated learning works by using a...
Read more

Introducing Segmed’s LLM-based Data De-identification Playground

Here at Segmed, we’re simplifying access to real-world imaging data for AI research and collaboration. Due to this, we regularly process massive quantities of data - both radiology reports and associated DICOMs. To date, we have accumulated 60M+ patient records sourced from 1500+ sites across 5 continents (and this number continues to grow!). Medical data has incredible potential - researchers and developers in medical AI benefit from high-quality, standardized data to train, test and validate their algorithms. However, prior to sharing and collaborating on this data, patient privacy and confidentiality must be maintained. For this, said data must be effectively de-identified. What is data de-identification? Organizations that use patient data for research must prevent the exposure of protected health information (PHI). HIPAA (Health Insurance Portability and Accountability Act) mandates the removal of any information that could potentially lead to the re-identification of a patient....
Read more