Projects

Current Projects

UnderXAI
UnderXAI
Understanding Ovarian Cancer initiation and progression through Explainable Artificial Intelligence.
We develop an explainable AI-driven decision-support system for assisting the clinicians in making well-informed diagnosis and therapy decisions, ultimately leading to improved treatment outcomes for patients affected by ovarian cancer.
UnderXAI
CAL.HUB.BRIA
CAL.HUB.BRIA
The project “CAL.HUB.RIA” (CALabria HUB for Innovative and Advanced Research), included in the Health Operational Plan, focuses on two main activities: The development of a platform for collecting and harmonizing data on rare diseases; The implementation of machine learning models to support clinical decision-making in patients with advanced-stage brain gliomas (glioblastoma).
CAL.HUB.BRIA
TIMECARE
TIMECARE
The objective of the TIMECARE project is to develop artificial intelligence (AI) algorithms that can analyse biosignals to predict cardiovascular events and facilitate early recognition. The project will also develop AI algorithms to analyse videos to detect out-of-hospital cardiac arrest (OHCA) and augmented reality (AR) methods to deliver patient-specific cardiopulmonary resuscitation (CPR) and defibrillation. The ultimate goal is to reduce mortality and disability.

TIMECARE
Redit
REDIT
Robot-assisted Remote Echography for Diagnosis and Treatment
The REDIT project aims to empower ultrasound specialists to perform remote diagnostic exams, addressing challenges in isolated or mobile settings lacking on-site specialists. The core objectives encompass:
1) the development of a shared-autonomous robotic ultrasound scanning system, with embedded AI-enabled techniques to guide the transducer and avoid missing suspicious regions
2) the refinement of augmented interfaces, i.e., Augmented Reality (AR), to enhance the ergonomics and efficacy of remote ultrasound-guided diagnosis and interventions
3) a rigorous health technology assessment, emphasizing system transparency and usability through extensive validation on phantom models.
Redit
PREDICT
Latest Project
PREDICT
Funded by the Fondazione Regionale per la Ricerca Biomedica — aims to advance ovarian cancer care through the integration of advanced generative and predictive artificial intelligence. By leveraging diagnostic computed tomography (CT) scans and clinical data, the project develops AI models capable of forecasting tumor progression and chemotherapy response. This approach seeks to reduce unnecessary surgeries, optimize treatment planning, and improve both patient outcomes and healthcare efficiency.
PREDICT
UNCAN
Latest Project
UNCAN
Funded by the EU under Horizon Europe. It builds a federated cancer research data hub following the FAIR principles (Findable, Accessible, Interoperable, Reusable), integrating clinical, biological, and lifestyle information across diverse cancer patients in Europe. The goal is to accelerate prevention, early diagnosis, and treatment research, advancing precision medicine and improving patient outcomes
UNCAN
Cariplo
Latest Project
CARIPLO
Our primary objective is to advance the generalizability and inclusiveness of clinical trials through the integration of artificial intelligence and in-silico modeling.
To achieve this, we will develop advanced data processing and machine learning tools capable of identifying subject categories within trials, normalizing heterogeneous datasets from multiple clinical institutions, and managing missing data through modern statistical and data science methods. The project will further employ generative models to include under-represented populations, creating in-silico cohorts that simulate how diverse groups might respond to interventions.
Our project particularly focuses on improving trust, diversity, and representativeness in evidence-based medicine by enabling the automatic assessment of a trial’s generalizability to specific patients and by estimating how results translate to real-world scenarios. The project’s outcomes will directly inform future guidelines, supporting the development of robust, equitable, and generalizable clinical research practices.

Cariplo
LyNAR
Latest Project
LyNAR
We are developing an augmented reality–based surgical guidance system designed to improve intra-operative localization of metastatic lymph nodes in robotic-assisted radical prostatectomy. By fusing pre-operative PET/CT 3D reconstructions with live endoscopic views, the platform delivers real-time anatomical insights directly into the surgeon’s field of view—helping to reduce missed nodes and enhance oncological results. 
LyNAR
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UnderXAI
CAL.HUB.BRIA
TIMECARE
Redit
PREDICT
UNCAN
Cariplo
LyNAR
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Past Projects


ARTERY is a radiation-free approach based on shared-autonomy robotic catheters, with increased user engagement and easy interaction.

The fusion of the information yielded by echocardiography, optical and electromagnetic sensing techniques will provide a superior view upon the cardiovascular space.

Fluidic actuation paired with artificial intelligence will be the pillars motors of the next generation of robotic catheters that autonomously find their way towards the target site.

Through a fully immersive augmented reality interface, the operator will monitor the intravascular route of the catheter with no need for radiation-based imaging

Partners

Sant’Anna University (Pisa, IT) – KU Leuven (Leuven, BE) – IRCCS San Raffaele Hospital (Milano, IT) – SwissVortex (Zürich, SW) – Artiness (Milano, IT) – FBGs (Jena, GE)


ATLAS develops smart flexible robots that autonomously propel through complex deformable tubular structures. This calls for seamless integration of sensors, actuators, modelling and control.

By engaging in this ambitious research topic, participants will be exposed to all aspects of robotics. While contributing to the state of the art, they will become proficient in building, modelling, testing, interfacing in short in integrating basic building blocks into systems that display sophisticated behavior.

Partners

KU Leuven (Leuven, BE) – Sant’Anna School of Advanced Studies (Pisa, IT) – University Of Verona (Verona, IT) – University of Strasbourg (Strasbourg, FR) – Delft University of Technology (Delft, NE) – Polytechnic University of Catalonia (Barcelona, SP)


DIH-HERO’s primary objective is to accelerate innovation in robotics for healthcare.

To connect innovators, providers, businesses, users and politicians, DIH-HERO will establish an open online portal offering multiple services facilitating collaboration on various innovations, emphasizing the sharing of best practice and enhancing the delivery of innovation throughout the value chain.

DIH-HERO especially focuses on supporting small and medium-sized enterprises in maximizing their impact and reducing time-to-market


Find Us

NEARLab is located inside the Leonardo Robotics Labs space at Politecnico di Milano, piazza Leonardo da Vinci 32, Building 7, 20133, Milano, Italy
and at Campus Colombo in Via Giuseppe Colombo, 40, 20133 Milano MI

Hours

Monday to Friday: 8.00 A.M. – 20.00 P.M.


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Website Maintainers
Benjamin Fortuno, Matteo Di Mauro, Alessandra Maria Trapani

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