During the first reporting period, AutoPiX laid the scientific and technical groundwork needed to develop and validate AI-based imaging biomarkers for arthritis across different imaging methods and clinical settings.
A key achievement was the establishment of a secure data infrastructure that complies with GDPR and supports large-scale imaging research. This included clear rules for data governance, procedures to protect patient privacy through anonymisation and pseudonymisation, and the creation of a data lake that allows imaging and clinical data from multiple centres to be stored, annotated, and analysed in a structured way. Together, these elements form the technical foundation for training and validating AI models using large, multi-centre datasets.
From a methodological standpoint, AutoPiX developed shared standards and workflows for annotating imaging data. This included an Imaging Charter that defines how images should be acquired, who is qualified to read them, and how quality control and annotations are performed. These common standards ensure consistency and reproducibility across partners and across different imaging modalities.
On the AI development side, existing models within the consortium were systematically identified and reviewed, resulting in an inventory of nine AI and machine-learning models relevant to arthritis imaging. Building on this baseline, automated image-analysis pipelines were further developed, particularly for conventional radiographs. These pipelines combine automated identification of joints with visual explanations of the results, and early internal testing showed strong agreement with expert assessments. This supports their readiness for larger clinical validation studies.
At the same time, the project progressed the technical preparation of clinical validation studies focused on early disease detection, disease monitoring, and assessment of joint damage. Study protocols were prepared, data-collection workflows were defined, and technical systems for image acquisition, storage, and integration were put in place. In addition, remote and point-of-care imaging technologies, such as mobile self-imaging, thermography, and automated ultrasound systems, were technically integrated and aligned with relevant quality and safety standards to enable future prospective studies.
Overall, during the first reporting period, the consortium initiated the development of the key scientific methods, technical platforms, and early validated tools needed to support comprehensive, multi-modal validation of imaging biomarkers in the next phases of the AutoPiX project.
