DPLaBs
Digital Pathology Laboratory System
Scheda del progetto
The DPLaBs project aims to improve pathological anatomy (AP) through the development of innovative technology for pathologists and healthcare facilities.
Aequip will lead the project, developing products STAINS e ASSIST products, based on a proprietary mixed approach call MDI - Matchematical driven Intelligence that combines artificial intelligence with memetic-statistical methods to support pathologists in their daily routine.
Aethia will integrate the system into a Laboratory Information System (LIS) using High Performance Computing solutions.
The Genomics Laboratory of Fondazione Edo ed Elvo Tempia will analyze genetic markers for more accurate diagnosis.
The units of Pathological Anatomy di Torino e Biella will acquire imaged for Digital Pathology through scanners and will implement the developed solutions.
Contact:
Massimo Salvi
The objectives of the project are: 1) to create a digital platform for transition; 2) to provide end users with innovative tools for clinical practice; 3) to integrate genomic data to assess cancer risk and predict recurrence or tranformation.
Within this initiative, digital solutions will be designed and validated to support histological analysis, aiming to enhance and to accelerate diagnostic procedures for disease through both macroscopic and microscopic examinations.
The project aims to digitize two hospital in Piedmont, improving the interpretation of morphological data in Pathology through cutting-edge technologies.
The project includes the digitization of slides and the implementation of AI tools for assisted diagnosis. Solutions like STANS and ASSIST aim to reduce variability in prostate cancer diagnosis, providing robust results. Cloud architecture simplifies adoption, while genomic data integration aims to predict aggressive tumor behavior. The project, innovative on an international level, starts from different levels of technology maturity, with the goal of demonstrating its effectiveness in real-world situations.
The project will contribute to scientific, tehnological, economic, social and environmental impacts, focusing on 4P medicine: preventive, predictive, personalized, and participatory. The goal is to reduce the subjectivity of the diagnosis by reducing intra- and inter-operator variability, accelerate the prognosis and improve hospital efficiency. In this way, the patient can receive a more accurate diagnosis in a shorte time frame.