INPECO: diagnostic AI fine tuning and cloud platform

Collaboration with INPECO continues: fine tuning the diagnostic prediction model and cloud engineering the data pipeline.

AIDigital HealthR&D INPECODeep LearningCloudDiagnosticsData Pipeline

Direct collaboration with INPECO

The direct collaboration with INPECO, the group specialising in clinical laboratory automation, continues. The work focuses on two main tracks.

AI model fine tuning

The diagnostic prediction model based on deep learning, developed within the ALA project with the BioRobotics Institute of Scuola Superiore Sant’Anna, enters a targeted fine tuning phase. The goal is to improve the model’s precision and reliability on real clinical datasets, optimising sensitivity and specificity metrics for laboratory use.

Cloud engineering of the data platform

In parallel, noze works on the cloud engineering of the clinical data management platform. The architecture covers the entire pipeline:

  • Data collecting: structured acquisition of data from laboratory devices and systems
  • Pre-processing: normalisation, validation and preparation of data for AI inference
  • Post-processing: processing of model results, aggregation and correlation
  • Delivery: distribution of results to downstream systems and user interfaces

The infrastructure is designed to operate in both cloud and on-premise environments, with Docker containerisation and service orchestration.

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