Digital Health

Digital Health

Thesis

RAN-IO: Radiomics as biomArker imaging for NSCLC patients treated with ImmunOtherapy

SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors

Dr. Arsela Prelaj (arsela.prelaj@polimi.it)

Aldo Marzullo  (aldo.marzullo@polimi.it)

Collaborations 
 
Description

Aim:

Predicting Immunotherapy (IO) efficacy in Non-Small Cell Lung Cancer (NSCLC) is a crucial unmet clinical need. Beyond Programmed Death Ligand-1 (PD-L1) no other biomarkers are approved. However, PD-L1 is quite Imperfect. Radiomics was recently used as imaging biomarker in NSCLC patients treated with IO. This thesis aim is to find a predictive radiomic signature for NSCLC patients treated with IO

Project phases:

  • Literature review
  • Development of  predictive radiomic signature

Requirements:

  • Knowledge of Matlab
  • Knowledge of Python (scikit-learn, TensorFlow/Keras)
  • Basic knowledge of Git and Linux is welcome

ThesisCURE-T790M: NSCLC EGFR MUTATED patients pREdicting T790M+ USING AI
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors

Dr. Arsela Prelaj (arsela.prelaj@polimi.it)

Collaborations 
 
Description

Aim:

Osimertinib (OSI), a third-generation tyrosine kinase inhibitor (TKI) is now become the standard of care and is given as first line therapy in NSCLC patients with EGFR mutation (mut) since it demonstrated superiority compare to 1st and 2nd generation (G) TKI. Previously, OSI was received in 2nd line  after disease progression upon 1st or 2nd  generation TKI in around 50% of patients who acquired an exon20 resistance mutation called T790M. Recent data demonstrated that combination therapy with 1st G TKI in first Line obtained the same PFS compare to OSI. Thus predicting the onset of T790M can prolong survival of around 12 months in around 50% using combo therapy in first line and OSI in second line as sequence therapy. The aim of this study is to predict T790M using AI techniques by integrating all data of NSCLC patients with EGFR mut (Fig in the right).

Project phases:

  • Literature review
  • Development of  AI software to predict T790M

Requirements:

  • Knowledge of Matlab
  • Knowledge of Python (scikit-learn, TensorFlow/Keras)
  • Basic knowledge of Git and Linux is welcome

Thesis

Multimodaldata integration using explainable AI to predict the efficacy of immunotherapy in lung cancer patients

SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors

PhD Vanja Miskovic (vanja.miskovic@polimi.it)

 

Collaborations

Istituto Nazionale dei Tumori Milano (INT), Arsela Prelaj MD

 
Description

Aim:

Lung cancer is the leading cause of cancer-related death globally. In the last years Immunotherapy (IO), changed the therapeutic and prognostic process. The big challenge is to select the patients that would benefit from the IO treatment. Machine and Deep Learning (ML and DL) methodologies are able to analyze complex behaviors, from different types of data and increase the accuracy of prediction biomarkers leading to the selection of patients who can benefit from IO. In this project, we aim to develop an explainable AI model that uses multi-omics and real-world data to predict the efficacy of IO in Non-small-cell-lung-cancer patients (NSCLC).

Project phases:

  • Literature review on current ML and DL methodologies used in oncology
  • Data curation (in collaboration with medical oncologists from INT)
  • Exploratory data analysis (EDA)
  • Building an explainable ML/DL model for IO efficacy prediction

Requirements:

  • Good knowledge of Python
  • Basic knowledge of Machine and/or Deep Learning
ThesisPrendiciting Immuno-therapy response using AI in NSCLC patients integrating multiomics data
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors

Dr. Arsela Prelaj (arsela.prelaj@polimi.it)

Collaborations 
 
Description

Aim:

Predicting Immunotherapy (IO) efficacy oin NSCLC is a crucial unmet clinical need. Beyond Programmed Death Ligand-1 (PD-L1) no other biomarkers are approved. However, PD-L1 is quite Imperfect. Thus, AI which is able to integrate high dimensional real world and multiomics data could be a unique approach able To find an algorithm able to predict IO in NSCLC patients. The later will be this thesis aim.

Project phases:

  • Literature review
  • Development of  AI software to predict IO inNSCLC

Requirements:

  • Knowledge of Matlab
  • Knowledge of Python (scikit-learn, TensorFlow/Keras)
  • Basic knowledge of Git and Linux is welcome

Thesis

Development of an application to promote upper limb exosuit user engagement and monitor motor performance

SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Prof. Emilia Ambrosini
Co-Supervisors

Chiara Piazzalunga
Elena Bardi

Collaborations 
  
Description

Aim:
In the context of rehabilitation and assistance, soft exoskeletons (exosuits) represent a promising technology. Nevertheless, the acceptance of assistive devices is a challenge to be targeted. The aim of the thesis is to develop an application to provide daily feedback on the use of an upper limb soft robotic suit to promote user engagement and to monitor the motor ability of the user on a daily basis. The metrics are extracted from a set of wireless inertial measurement units (IMUs) placed on the user’s thorax and arm.

Project phases:

  • Literature Review on user engagement promotion and motor performance indexes
  • Familiarization with IMUs
  • Development of an application that downloads data from the exosuit control unit and displays the daily feedback and long-term progression of motor performance
  • Usability study

Requirements:

  • Good knowledge in programming (Python, Matlab)
Thesis

Development of a multiplayer videogame to promote the inclusion of disabled children

SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisors

Chiara Piazzalunga(chiara.piazzalunga@polimi.it)

Collaborations

ActivE3 project (Politecnico di Milano, Lecco Campus), TecnoBody (Dalmine, BG)

  
Description

Aim:
Physical activity is a crucial factor in the development of children, contributing to physical, emotional, and relational well-being. Because of this, governments all around the world call for its regular monitoring through Physical Education lessons. However, these can be a source of discomfort and exclusion for children who have a disability, as the activities are not always designed towards accessibility and inclusion. The ActivE3 project aims at exploiting technological tools to make psychomotricity activities at school more inclusive. The aim of this work is to create a multiplayer videogame with multiple input systems to cater to the needs of disabled children.

Project phases: 

  • Literature review
  • Functional requirements definition and videogame co-design
  • Identification of the best tools for input and data collection
  • Development of the videogame
  • Testing of the game
  • Data analysis

Requirements

  • Interest in videogames
  • Familiarity with coding (C#, Unity)
Thesis

Serious games for Specific Learning Disorders prevention and training

SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisors

Chiara Piazzalunga(chiara.piazzalunga@polimi.it)

Collaborations

Essence project, Indipote(dn)s project, Prof. Cristiano Termine (Università dell’Insubria, Fondazione Macchi)

  
Description

Aim: 
Specific Learning Disabilities (SLDs) first screening starts from direct teachers’ observation. If it is not possible, e.g., in distance learning during COVID-19 lockdown, they exacerbate and prevent children from proper learning. The aim of this work is to provide a platform based on serious games and teachers’ observations for the training of learning difficulties in primary school children.

Project phases: 

  • Literature review
  • Development of serious games
  • Serious game testing
  • Data analysis

Requirements

  • Interest in videogames
  • Familiarity with programming (C#, Unity)
Thesis

Speech emotion recognition and longitudinal monitoring of cognitive decline through mobile app

SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-Supervisors

Chiara Giangregorio (chiara.giangregorio@polimi.it)

Collaborations

 

  
Description

Aim: 
Automatic voice analysis is being widely studied and employed since it contains important information about the cognitive status and allows ecological and continuous monitoring. Within the ESSENCE European project, a mobile app was developed to compute voice parameters on the fly during phone conversations for the identification of cognitive decline. This work aims at exploiting the functionality of the app, for the longitudinal monitoring of cognitive decline and emotions of frail subjects.

Project phases:

  • Literature Review
  • Protocol definition
  • Data acquisition
  • Development of machine learning models

Requirements:

  • Basic knowledge of Matlab and Python
  • Basic machine learning knowledge
Thesis

Detection and analysis of cough in lung cancer patients

SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-Supervisors

Chiara Giangregorio (chiara.giangregorio@polimi.it)

Collaborations

Istituto Nazionale dei Tumori Milano (INT), Arsela Prelaj MD

  
Description

Aim: 

It has been proved that 65% of people with lung cancer have a chronic cough by the time they’re diagnosed (up to 80% or higher for those with advanced disease). In the context of I3Lung, a European project which “ensures access to innovative, sustainable and high-quality health care”, an app has been developed for the automatic and ecological monitoring of cough in lung cancer patients. This thesis aims to develop a machine (or deep) learning algorithm to analyze cough to ensure therapy’s effectiveness.

Project phases:

  • Literature Review
  • Development of an automatic algorithm for features extraction
  • Design of a machine learning model for the detection of cough

Requirements:

  • Basic knowledge of Matlab
  • Basic knowledge of Machine Learning and Python
ThesisMachine Learning for premature babies parenteral nutrition
SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsMarco Frontini (Link Up s.r.l.)
Valentina Bozzetti (Ospedale San Gerardo, Monza)
  
Description

Aim:

Premature babies need a special nutrition program to overcome their frailty condition. It must balance different nutrients apport, to achieve a target growth. However, the real effect of different nutrition programs is still unclear. The aim of this work is the creation of a machine learning model on babies’ response to nutritional programs and the impact on their global health.

Project phases:

  • Literature review
  • Machine Learning models implementation

Requirements:​

  • Interest in machine learning
ThesisMonitoring of patients with Parkinson’s disease during walking in free-living and challenging conditions
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsMilad Malavolti (milad.malavolti@polimi.it)
Monica Parati (monica.parati@polimi.it)
CollaborationsIRCCS Istituti Clinici Scientifici Maugeri, Milano
  
Description

Aim:

Gait impairments, including bradykinesia and freezing of gait (FoG), are the most common and disabling symptoms in Parkinson’s disease patients. Quantifying gait impairments under free-living and challenging condition (e.g. FoG-provoking test) using sensing technologies is a promising avenue to assess and monitor disease severity. The aim of this work is to use sensing technologies to quantify gait impairment in Parkinson’s disease, with the final aim of defining tailored interventions.

Project phases:

  • Literature review​
  • Set-up and protocol refinement
  • Pilot testing on patients with Parkinson’s disease
  • Data analysis

Requirements:​

  • Interest in working with patients
  • Basic knowledge of Matlab and programming languages
Thesis

Handwriting Analysis in Primary School Children with an IoT Smart Ink Pen

SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsSimone Toffoli
Collaborations

Università degli Studi dell’Insubria

 
Description

Aim:

10 to 30% of pupils in primary school show difficulties in mastering handwriting movement skills, a condition known as dysgraphia. Such difficulties have a negative impact on many aspects, like the child’ self-esteem, personal relationship, academic and employment achievements. The diagnosis is performed during grade 3 and is based on the qualitative observation of the handwritten product. National guidelines suggest the development of quantitative approaches for the analysis of children’s handwriting performance (process analysis) in order to: i) create normative data for Italian children of primary school; ii) compare normally developing pupils and children classified as dysgraphic by current clinical protocols. The thesis aim is the collection of handwriting data from pupils with a smart ink pen developed at Nearlab, followed by data  analysis.

Project phases:

  • Literature Review
  • Data collection
  • Data analysis

Requirements:

  • Knowledge of Matlab and Python; Interest in data acquisition in schools
Thesis

IoT smart ink pen for early detection and monitoring of patients with mild cognitive impairments and dementia

SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
Simone Toffoli (simone.toffoli@polimi.it)
Collaborations

IRCCS Fondazione Don Carlo Gnocchi ONLUS

 
Description

Aim:
Dementia affects millions of people worldwide. Unfortunately, it cannot be cured, but an early diagnosis can help to better manage the disease evolution. Handwriting results from a complex network made up of cognitive, kinesthetic, and perceptual-motor abilities and it is one of the daily’s activities affected in patients with dementia. 

The aim of this work is to use an IoT smart ink pen for the characterization of patients with dementia and mild cognitive impairment with the final goal of supporting early diagnosis. 

Project phases:

  • Literature Review
  • Data analysis and algorithm development

Requirements:

  • Knowledge of Matlab and Python
ThesisIoT smart ink pen for early detection and monitoring of patients with Parkinson’s disease
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
Monica Parati (monica.parati@polimi.it)
CollaborationsIRCCS Istituti Clinici Scientifici Maugeri, Milano
 
Description

Aim:

The diagnosis of clinically probable Parkinson’s Disease (PD) in the early stages relies primarily on clinical assessment by a neurologist. When motor symptoms affect the dominant hand, patients may report worsening of

handwriting as of the initial symptoms. The aim of this work is to use an IoT smart ink pen for the characterization of PD patients handwriting, with the final goal of supporting PD patients’ diagnosis and remote monitoring

Project phases:

  • Literature review​
  • Data collection
  • Data analysis and algorithm development

Requirements:​

  • Basic knowledge of Matlab
  • Basic knowledge of Python
ThesisCompanion interface for an IoT smart ink pen
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsMilad Malavolti (milad.malavolti@polimi.it)
Collaborations 
 
Description

Aim:

Handwriting is a high-value task entailing a unique blend of cognitive, perceptual, and fine motor skills and for this reason its assessment is leveraged in a number of health-related applications,  One of the aim of the European ESSENCE project is to leverage a novel IoT smart in pen to monitor user’s handwriting. 

The aim of this thesis work is to develop a companion interface to manage – via Bluetooth – communication, data recording, data download and important  functionalities related to the pen use.

Project phases:

  • Research for identification of solutions​
  • Companion Interface design of automated algorithms
  • Companion interfacedevelopment
  • Companion Interface testing

Requirements:

  • KnowledgeAndroid programming or otherlanguages (Python, Javascript…)
  • Interest in programming and problem solving
ThesisThe DYSPA System: a novel neuro-motor assessment to quantify dystonia and spasticity in children with movement disorders
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsDott. Giovanna Zorzi, Dott. Davide Rossi, Fondazione IRRCS Istituto Neurologico Carlo Besta
 
Description

Aim:

Selecting and evaluating appropriate treatment for children with hypertonic movement disorders is nontrivial. One challenge is the ability of quantifying the presence and importance of motor impairments, especially when more than one coexist. This is the case, for instance, of mixed hypertonia with components of spasticity and dystonia. Against this background, the DYSPA System aims at achieving quantitative assessment, encompassing kinematic and electromyographic measures, that quantifies neuro-motor performance during functional tasks and measures the presence and extent of motor impairments through specific dystonia and spasticity indices.

Project phases:

  • Literature review
  • Data collection on children with movement disorders and age-matched controls
  • Data analysis and statistics

Requirements:

  • Knowledge of Matlab
  • Interest in learning other analysis softwares (e.g., BTS Smart Analyzer, R)
  • Interest in pediatric movement disorders
  • Interest in working in close contact with patients
ThesisComputer Vision and IoT to detect grasping difficulties
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Prof. Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsUniversità dell’Insubria/Fondazione Macchi (prof Cristiano Termine)
Provveditorato di Varese (Luigi Macchi, Simonetta Bralia)
 
Description

Aim:

An inefficient grasping position causes difficulties in handwriting and an early correction can avoid persistent problems. In distance learning, teachers’ or experts’ direct observation was not possible.
The aim of this work is to leverage computer vision to detect the grasping strategy of children, through video recordings of handwriting production, and to relate it to pen movements collected by an IoT smart ink pen, with the final goal of understanding grasping problems from IoT sensors only.

Project phases:

  • Literature review
  • Data collection
  • Computer vision algorithms implementation
  • IoT sensors’ data analysis

Requirements:

  • Basic knowledge of computer vision and deep learning
  • Availability to reach schools for data collection (province of Varese)
ThesisVoice analysis to diagnose neurodegenerative diseases
SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
CollaborationsDott. Andrea Arighi, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico
 
Description

Aim:

Voice signals provide important information to measure human behaviors and cognitive functions. Various types of neurodegenerative dementia (Alzheimer’s disease, frontotemporal dementia, primary progressive aphasia, Lewy body dementia) affect human speech in different manners. Disorders or irregularities in language domain, evaluated in terms of temporal features, such as pauses, speech rate, pitch, etc, could be strong predictors of different neurodegenerative pathologies affecting the brain. This thesis aims at the development and validation of a software tool able to automatically extract a set of language features sensitive and to classify neurodegenerative disorders from voice signals.

Project phases:

Starting from a Matlab-algorithm to automatically extract voice features from recordings and a dataset of recordings on healthy elderly subjects:

– Literature review on vocal indicators of cognitive decline
– Creation of a database of normal and pathological voice samples from elderly (control subjects, patients with Alzheimer’s disease, frontotemporal dementia, primary progressive aphasia and Lewy body dementia)
– Optimization of the algorithm to extract voice features
– Development of a classification algorithm to support the diagnosis of neurodegenerative diseases based on voice features and neurodegenerative biomarkers (magnetic resonance atrophy, cerebrospinal fluid proteins concentration)

Requirements:

  • Knowledge of Matlab
  • Knowledge (or intention to know) Python
  • Availability to participate to data collection
ThesisEye tracking for action observation treatment in neurological patients
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorFrancesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsDr. Davide Sebastiano RossiFondazione Istituto Neurologico C. Besta, Milano
Prof. Giovanni BuccinoUniversità Vita-Salute San Raffaele and Division of Neuroscience
 
Description

Aim:

Studies show that the use of systematic observation of meaningful actions followed by their execution (action observation treatment [AOT]) may become a rehabilitative strategy to accelerate the process of functional recovery in patients with motor impairment.

This work aims at developing a home-based system for neurological patients that leverages eye tracking technology during the observation of video clips showing appropriate actions, with the aim of evaluating compliance to the required task. 

Project phases:

– Literature review
-Design of technological solution with definition of requirements and specifications
Integration of eye tracking with tablet/laptop
Data acquisition and analysis