Related services

• AI Imaging Lab: Development & Validation of Segmentation and Detection Models

  Karolinska Institutet (KI)

  ## Overview This service supports SMEs and researchers in developing, training, and validating AI models for medical imaging applications. Hosted at SMAILE, Karolinska Institutet, it covers segmentation, detection, and classification tasks using CT, MRI, nuclear images, multimodal images, ultrasound, histology, and microscopic images. The pipeline includes data curation, evaluation of labeling strategy, model selection and training, evaluation metric selection, and model performance benchmarking. We provide expertise and support in: - AI model training and optimization for medical imaging - Validation using clinical datasets and standard metrics (using publicly available datasets, datasets available through data agreements, and internal datasets at KI, depending on the case) - Clinical Relevance and Comparison with the state of the art in research and clinical practice - Imaging biomarkers studies for diagnosis, prognosis, and prediction applications ### How can the service help you? The service ensures your imaging AI model performs reliably and is aligned with clinical expectations. Whether you’re entering the pre-clinical testing phase or seeking validation to secure investment or regulatory approval, this service equips you with a rigorous evaluation and feedback report. ### How the service will be delivered? Available both virtually and physically. Imaging data can be reviewed remotely through a secure data transfer process. On-site collaboration is also possible for sensitive datasets or model development, evaluation, and validation. A typical project takes 4–6 weeks. --- ## Additional information ### Provider description SMAILE is the digital health core facility at Karolinska Institute. It offers interdisciplinary support in AI for medical imaging, data analytics, and system validation, partnering with leading institutions under the Swedish TEF-Health node. ### Technical description The imaging model pipeline is extensively validated by using a curated benchmark set and standard evaluation frameworks such as well-established quantification metrics for object detection, image segmentation, and classification tasks.. Annotation quality is reviewed, and model performance is benchmarked against open or reference models. Standard medical image processing tools such as PyDicom, ANTs, and ITK, as well as community-driven open-sourced frameworks such as MONAI, are the core components of our designed pipelines. ### Service customization The service can focus on either model development, evaluation, or both. Datasets can be anonymized and securely shared, or analysis can be conducted in a local sandboxed environment.

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• Anatomic pathology: human prepared slides, data, analysis, expertise

  Centre Hospitalier Universitaire De Rennes (CHU RENNES)

  Anatomic pathology slides: preparation +/- associated patient clinical data +/- analysis +/- software evaluation +/- medical expertise; cancer, lesion, sample, biopsy, tissue, organ; histology, diagnosis, genetic; microscope, scanner

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• DNA & RNA analysis

  Univerzita Komenskeho V Bratislave (UK BA)

  (1) The laboratory provides microsatellite instability (MSI) in FFPE cancer tissue in low throughput screening methods, including PCR and fragment analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in GeneMapper v.6 software. (2) Next, the laboratory provides DNA methylation analysis in promoter regions of genes in low and medium throughput screening methods, including pyrosequencing and/or MS-MLPA and fragment analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in Cofalyser.net softvare. (3) The laboratory also provides comparative genomic hybridization (CGH), as well as single nucleotide polymorphism (SNP) detection by microarray that enables identification of aneuploidies, microdeletions, microduplications, as well as other types of chromosomal aberrations across the genome. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing. (4) The laboratory provides detection of single nucleotide variation from various types of samples by real-time PCR or Sanger sequencing. The method is also usefull for verification of whole genome sequencing results. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing. (5) The laboratory also provides targeted sequencing of selected parts of the genome (e.g. TruSight Oncology 500 panel), followed by somatic or germline analysis. The service includes insight into technology, sample preparation, and partial or full bioinformatic data processing in Pierian software with clinical interpretation.

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• EUCAIM Platform

  Eucaim

  Cancer Image Europe provides a robust, trustworthy platform for researchers, clinicians, and innovators to access diverse cancer images, enabling the benchmarking, testing, and piloting of AI-driven technologies. EUCAIM Platform integrates a dashboard, a catalogue and a federated searching environment to discover Medical Imaging data related to cancer from a distributed federation of data holders. EUCAIM provides in some nodes processing capacity to securely access the data. EUCAIM is mainly intended for Data scientists to develop, validate or improve AI models or image postprocessing tools. The platform is accessible in https://dashboard.eucaim.cancerimage.eu/

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• Flow cytometry

  Univerzita Komenskeho V Bratislave (UK BA)

  Laboratory infrastructure includes flow cytometer/sorter FACS Aria II c, fluorescence microscope Olympus iX72, light microscope, BSL-2 laminar hoods, cell incubators, and electrophoretic equipment for Western blot. The laboratory develops and prepares unique in vitro models (3D and 2D cancer cell lines) derived from human tumor tissue.

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• Identification and enablement of existing health data for AI solutions

  Karolinska Institutet (KI)

  ## Overview Identifying and acquiring existing data sets—full-service data management. This service identifies data sets and makes them available to SME’s for TEF-project purposes, such as validation and testing of AI systems. Data can be collected from routine health records and local, regional, and national databases in collaboration with healthcare providers in Sweden. We provide expertise and technical support in the following areas: - Project design & management - Ethical application support - Data assembly - Data cleaning, annotation, anonymisation ### How can the service help you? Access to existing health data can help you validate your AI solution without needing to generate new data. We provide access to high quality datasets for validation and testing of your AI system to facilitate placing it on the market, and supporting to increase its market readiness level. Validation and testing data can be used for independent performance evaluation of your AI system in a real-world or simulated environment. ### How the service will be delivered? The service will be delivered according to established ethical agreements and guidelines, in collaboration with the SME and researchers from the Swedish TEF-Health node. Data usage is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. --- ## Additional information ### Provider description The Swedish TEF-Health node is a collaboration between Karolinska Institutet, SciLifeLab and RISE, and is led by Karolinska Institutet. Together, we offer world-leading services with our unique collection of core facilities. We can grant services in expert consulting, virtual- and physical testing in the range of in vivo imaging, ex vivo OMICS, pharmaceutical development, simulated healthcare environments, AI-system validation and development, advanced data analysis and other data-driven life science. ### Technical description Data access and usage are facilitated through a secure environment managed by Karolinska Institutet. Datasets will be identified and made available to suit your AI system, to support with testing and validating your solution. The data will be tailored to your specific request to ensure it is compatible with your AI system, with full support from Karolinska Insitutet to ensure compliance with all relevant regulations. Upon completion of this service, you will receive an evaluation of the performance of your AI system, which can support the placing of your solution on the EU market. ### Service customization The service can be customised according to your specific needs, taking into account which type of data you require. --- ## Use case example ### Context A biotech SME is developing an AI-based system designed to improve cancer diagnosis, treatment planning, and prognosis prediction. To gain regulatory approval and to facilitate market entry, the SME requires access to diverse, high-quality datasets, including cancer imaging, electronic healthcare records (EHR), and multi-omics data, for independent performance evaluation. ### Objective The goal is to validate the AI system's ability to analyze complex datasets and deliver accurate, reliable outputs in simulated and real-world scenarios. ### Solution The identification and enablement of existing health data for AI solutions service, provided by the Swedish TEF-Health node in collaboration with Karolinska Institutet, offers ethically sourced datasets tailored to the SME’s needs. ### Implementation #### Ethical Agreement The SME enters into an ethical agreement with researchers from the Swedish TEF-Health node, ensuring all data collection and usage comply with GDPR and national Swedish regulations. #### Data Collection - Cancer Imaging Data: Retrospective datasets from imaging modalities like CT, MRI, and PET scans, annotated for various cancer types and stages. - EHR Data: Pseudonymised clinical data, including patient histories, treatment outcomes, and longitudinal follow-up records. - Omics Data: Retrospective genomic, transcriptomic, and proteomic datasets linked to cancer cases. #### Secure Access Usage of the collected data is facilitated through a secure virtual environment managed by Karolinska Institutet, ensuring the highest standards of data protection. #### Validation and Testing Through a collaboration between Karolinska Institutet and the SME, the dataset is used to test the AI system's ability to: - Detect cancer accurately across imaging modalities. - Predict treatment responses and outcomes using combined EHR and omics data. - Identify biomarkers associated with different cancer subtypes and prognostic outcomes. ### Outcome Following the validation and testing, the AI system can be shown for its robustness, accuracy, and reliability, supporting regulatory approval and enhancing confidence for healthcare providers and stakeholders. ### Benefits - **Comprehensive Dataset**: Integration of imaging, EHR, and omics data ensures the AI system is tested across real-world complexities. - **Ethical and Secure**: Compliance with ethical guidelines builds trust and supports regulatory approval. - **Accelerated Innovation**: Access to retrospective data saves time, allowing the SME to focus on model optimization and deployment. ### Impact The validated AI system enhances early cancer detection and personalized treatment planning, improving patient outcomes while streamlining healthcare workflows.

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