About the client

In this business case, our customer was a UK-based startup focused on AI and ML tools for processing DICOM images (MRI, CT, X-ray, and PET) and non-imaging data. It provides R&D professionals across life science, academic, and other organizations with advanced cloud-based data analytics tools.

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Business requirements

The customer's goal is to provide R&D professionals across life science, academic, and other organizations with advanced cloud-based data analytics tools integrated with various imaging modalities and data sources (PACS, EHR, etc.). The solution we worked on is used for early diagnosing neurological disorders, such as Alzheimer’s disease and multiple sclerosis, as well as for predicting the velocity of memory and cognitive deterioration. The platform leverages AI-enabled multi-modal analysis of data acquired from different sources: medical scans, EHRs, wearable devices, genomic repositories, and more. Andersen assisted the startup team with Front-end and Back-end development to roll out an MVP with a user-friendly interface, integrated with a sophisticated AI-rich back-end part. With this solution, scientists from all over the world can now upload DICOM scans of brain tissue alongside non-imaging data for further processing with unique AI/ML algorithms, effectively identifying the earliest signs of some of our society’s most devastating diseases.

duration7 months





Additional services:

QA manual, DevOps, UX/UI, PM, BA

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Page with recent clinical projects

Project details

Andersen's team has contributed to the development of an AI-focused, scalable, and customized clinical decision support platform aimed at diagnosing and predicting progressive degenerative brain diseases. The solution uses multi-modal (MRI, PET, CT, X-ray) imaging and non-imaging data.

Application functionality

  • A user-friendly and task-driven interface for clinicians and medical researchers, which doesn't require ML knowledge or skills in ML programming;
  • Dynamic image analysis and the possibility to conduct own research projects by using several clinical variables;
  • Automatic and reliable DICOM file de-identification for research purposes;
  • A fast and secure DICOM uploader with unique algorithms for further analysis;
  • The possibility to review the analysis progress and production of visually comprehensible results;
  • Further integration with hospital PACS and EMRs.

Project results

  • A new system is developed following SOLID, REP, and CCR Architectural principles, as well as Cloud technologies;
  • Easy aggregation, management, standardization, and sharing of all medical imaging are ensured in order to significantly contribute to an increased number of research-based users;
  • The platform has a user-friendly interface to smartly process the uploaded images by unique algorithms, review analysis progress, and visualize its results.

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