To develop an integrated approach based on a datalakehouse infrastructure. The project proposal is based on the need for Data oriented IRCCS to equip themselves with technological platforms suitable for supporting the monitoring processes and planning support both in terms of clinical research and in terms of organizational research.
In particular, the project will focus on the development and testing of new IT/information tools to support clinical and research activity capable of standardizing data and providing forecasting and management algorithms with a particular focus on the of Datalakehouse for assistance and research data analysis, analytical ecosystem preparation and standard definition for the implementation of Advanced Analytics algorithms.
Sample size: Patients admitted in Emergency Department and as Inpatients to the IRCCS University Hospital of Bologna Policlinico di Sant'Orsola in occasion of epidemics spread or extreme climate conditions especially considering patients with frailty conditions.
This pilot project aims to improve fall risk assessment in elderly individuals by focusing on muscle power and motor control degradation, which are believed to be more predictive of falls than muscle strength alone.
The study will use a combination of experimental methods—including isometric and isokinetic dynamometry, gait analysis, and wearable sensors—and in silico tools such as digital twins and musculoskeletal modeling.
The goal is to develop and validate a clinically applicable protocol that quantifies muscle power, strength, and motor control. This approach will help identify older adults at high risk of falling, particularly those with or without knee osteoarthritis.
Data from experimental tests and imaging (e.g., MRI) will be collected and integrated with simulation models to inform clinical decision-making. The study is ambispective and observational, involving follow-ups over 24 months.
Expected outcomes include improved predictive accuracy of fall risk, a robust data collection framework, and a secure, open-access data platform. The project aligns with Italy’s National Recovery and Resilience Plan (PNRR), targeting advancements in preventive and personalized medicine.
To identify novel patterns of risk factors associated with non-communicable diseases among the elderly:
Sample size: between 500,000 and 700,000 elderly population in Europe.
The pilot project FALLSPREDICT aims to develop a tool capable of estimating the risk of falls in older adults, both frail and non-frail, following hospital discharge as well as in the general population.
The tool will be fed with demographic and clinical data, as well as data collected from wearable sensors that monitor mobility, sleep, and heart rate, providing risk estimates on an annual and semi-annual basis.
FALLSPREDICT, either on its own or in combination with other tools used to estimate fracture risk following a fall, can support healthcare professionals in optimally allocating limited resources for fall prevention and mitigation. It will also provide valuable insights into key functional domains (mobility, sleep, and heart rate) in the daily lives of older adults.
The pilot project includes a main study (DARE-FALLSPREDICT study) and satellite studies (DARE-FALLSPREDICT GP and LOOKING-GLASS studies) to explore various aspects of the practical and technical feasibility of procedures involving the integrated use of multiple wearable sensors.
Osteoarthritis afflicts a considerable percentage of the population and, over time, disables the affected individual in common motor activities. In patients with degenerative processes still in the early stages, knee osteotomy is intended to realign the joint so as to slow the progression of osteoarthritis and, thus, delay or even eliminate the need for knee replacement. Standard preoperative planning of osteotomies is generally based on simple radiographs. With innovative three-dimensional analysis, advanced medical imaging techniques, and biomechanical modelling, it is now possible to perform such planning in a more accurate and personalised manner. The main goal of this pilot project is to arrange an accessible platform for “smart” planning of personalised knee osteotomies. Relevant patient-specific information, such as imaging, anatomical modelling, and device design, will be integrated into a single tool, the operability of which will be tested by a select group of Rizzoli Orthopaedic Institute surgeons scattered throughout the country. Last but not least, it is expected that the use of such a digital platform can be extended to other types of surgeries, including those of other anatomical compartments, supporting the use of three-dimensional printing in the customisation of orthopaedic treatments.
The project aims to develop methodologies providing insights about the progression of two major conditions affecting the knee joint. Specifically, the project focuses on studying: the progression of osteoarthritis, 1) assessing whether its advancement can be monitored since the early stages, 2) and joint prosthesis failure, representing the final treatment option in cases of end-stage osteoarthritis, but still with sub-optimal performance.
To investigate such relevant clinical issues, in silico models will be developed based on clinical imaging and key features of the prosthesis. Depending on the specific condition, the models will be driven either by evidence obtained through targeted motor tasks or by international standards. An in vitro study performed on knee tissues collected from patients undergoing total knee arthroplasty will further explore the impact of osteoarthritis on the functionality of these tissues, with particular focus on articular cartilage.
Neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease, are preceded by a long prodromal phase, which represents the best therapeutic window but whose diagnosis is difficult. In this pilot we will focus on prodromal patients with a high conversion rate to overt neurodegenerative diseases (patients with subjective cognitive decline, mild cognitive impairment, Down syndrome, REM behaviour sleep disorder).
We plan to develop multimodal markers to predict the risk and time to conversion from prodromal conditions to overt neurodegenerative diseases.
These markers will include clinical data (instrumental measurements, clinical scales), neuropsychological data (neuropsychological scales, caregiver questionnaires), imaging data, biological data (blood test results, biomarkers of neurodegeneration measured in peripheral tissues and biological fluids, omics).
Overall, this project will promote the development of non-invasive and low-cost screening strategies for the early diagnosis of neurodegenerative diseases.
Diabetes mellitus (DM) affects approximately 530 million people worldwide. The foot and locomotor system can be severely affected (in about 20% of these patients), and the associated treatment costs for healthcare systems are substantial. Due to the risk of ulceration, it is crucial to identify all potential predisposing factors so that both patients and healthcare professionals can immediately adopt protective measures.
Modern multi-instrumental assessments—such as plantar pressure analysis, joint kinematics, and weight-bearing 3D radiographic scans—can now more effectively support prevention as well as early and personalised care, particularly through the identification of new biomechanical-based biomarkers.
The study evaluates two distinct populations of patients with type 2 DM:
I) those without any ulcerative lesions, and
II) those who have already developed such lesions.
Clinical and metabolic data will be collected at Sant’Orsola Hospital in Bologna, biological and biochemical data at Maria Cecilia Hospital in Cotignola, and biomechanical and functional data at the IRCCS Rizzoli Orthopaedic Institute in Bologna.
The primary objective is to correlate all collected data with the risk of foot ulceration. The secondary objective is to create a reference database on the metabolic conditions and structural and dynamic alterations of the diabetic foot in relation to ulceration risks.
This will allow for a detailed and quantitative monitoring of disease progression and enable early identification of potential foot complications.
The pilot study “Accessible Measurements of Mobility and Deformity as Biomarkers for Orthopaedic Treatments (MAMBO)" is being conducted at the IRCCS Istituto Ortopedico Rizzoli in Bologna. The project is coordinated by the research teams of the "Movement Analysis and Functional Evaluation of Prostheses" laboratory and of the "Rare Skeletal Diseases" unit. The primary objective of the study is the functional and morphological characterisation of Multiple Osteochondromas, a rare musculoskeletal disorder marked by the development of benign osseocartilaginous tumours on the bone surface. These bony lumps may result in functional and postural impairments of the musculoskeletal system.
The characterization of Multiple Osteochondromas is performed using state-of-the art sensors such as: IMU sensors, to measure joint mobility and quantify motor deficits; Pressure platforms, to assess plantar pressure distribution; Full-body 3D scanners, to assess the skeletal and postural alterations. In addition, the pilot also aims at evaluating experimental protocols, based on operator-independent instrumentation, for patient monitoring in community and outpatient settings.
Adopting a holistic approach, MAMBO integrates morphological and postural evaluations, offering clinicians a more comprehensive understanding of the patient’s overall condition and the progression of the disease over time. Furthermore, the study defines functional parameters that can serve as prognostic and predictive factors for the clinical and surgical management of patients with this rare disease, as well as others with similar pathogenic mechanisms.
This pilot project aims to apply and validate an in-silico trial technology called BoneStrength to assess the effectiveness of various hip fracture prevention strategies in the elderly population. Hip fractures, often caused by falls and bone fragility, represent a significant public health issue, with high mortality rates and substantial healthcare costs. Although increasing bone mineral density (BMD) has traditionally been the primary goal in fracture prevention, recent evidence suggests that preventing falls may be a more effective approach for certain populations. The BoneStrength model simulates hip fracture risk by integrating virtual cohorts with individual femur geometries, BMD data, and environmental or lifestyle-related factors. The study will simulate a 10-year follow-up on over 1,000 virtual subjects to compare different preventive strategies—such as physical exercise or environmental modifications—predicting their impact on fracture incidence. Model validation will be based on data available in the scientific literature.
To develop an integrated approach based on a datalakehouse infrastructure. The project proposal is based on the need for Data oriented IRCCS to equip themselves with technological platforms suitable for supporting the monitoring processes and planning support both in terms of clinical research and in terms of organizational research.
In particular, the project will focus on the development and testing of new IT/information tools to support clinical and research activity capable of standardizing data and providing forecasting and management algorithms with a particular focus on the of Datalakehouse for assistance and research data analysis, analytical ecosystem preparation and standard definition for the implementation of Advanced Analytics algorithms.
Sample size: Patients admitted in Emergency Department and as Inpatients to the IRCCS University Hospital of Bologna Policlinico di Sant'Orsola in occasion of epidemics spread or extreme climate conditions especially considering patients with frailty conditions.
This pilot project aims to improve fall risk assessment in elderly individuals by focusing on muscle power and motor control degradation, which are believed to be more predictive of falls than muscle strength alone.
The study will use a combination of experimental methods—including isometric and isokinetic dynamometry, gait analysis, and wearable sensors—and in silico tools such as digital twins and musculoskeletal modeling.
The goal is to develop and validate a clinically applicable protocol that quantifies muscle power, strength, and motor control. This approach will help identify older adults at high risk of falling, particularly those with or without knee osteoarthritis.
Data from experimental tests and imaging (e.g., MRI) will be collected and integrated with simulation models to inform clinical decision-making. The study is ambispective and observational, involving follow-ups over 24 months.
Expected outcomes include improved predictive accuracy of fall risk, a robust data collection framework, and a secure, open-access data platform. The project aligns with Italy’s National Recovery and Resilience Plan (PNRR), targeting advancements in preventive and personalized medicine.
To identify novel patterns of risk factors associated with non-communicable diseases among the elderly:
Sample size: between 500,000 and 700,000 elderly population in Europe.
The pilot project FALLSPREDICT aims to develop a tool capable of estimating the risk of falls in older adults, both frail and non-frail, following hospital discharge as well as in the general population.
The tool will be fed with demographic and clinical data, as well as data collected from wearable sensors that monitor mobility, sleep, and heart rate, providing risk estimates on an annual and semi-annual basis.
FALLSPREDICT, either on its own or in combination with other tools used to estimate fracture risk following a fall, can support healthcare professionals in optimally allocating limited resources for fall prevention and mitigation. It will also provide valuable insights into key functional domains (mobility, sleep, and heart rate) in the daily lives of older adults.
The pilot project includes a main study (DARE-FALLSPREDICT study) and satellite studies (DARE-FALLSPREDICT GP and LOOKING-GLASS studies) to explore various aspects of the practical and technical feasibility of procedures involving the integrated use of multiple wearable sensors.
Osteoarthritis afflicts a considerable percentage of the population and, over time, disables the affected individual in common motor activities. In patients with degenerative processes still in the early stages, knee osteotomy is intended to realign the joint so as to slow the progression of osteoarthritis and, thus, delay or even eliminate the need for knee replacement. Standard preoperative planning of osteotomies is generally based on simple radiographs. With innovative three-dimensional analysis, advanced medical imaging techniques, and biomechanical modelling, it is now possible to perform such planning in a more accurate and personalised manner. The main goal of this pilot project is to arrange an accessible platform for “smart” planning of personalised knee osteotomies. Relevant patient-specific information, such as imaging, anatomical modelling, and device design, will be integrated into a single tool, the operability of which will be tested by a select group of Rizzoli Orthopaedic Institute surgeons scattered throughout the country. Last but not least, it is expected that the use of such a digital platform can be extended to other types of surgeries, including those of other anatomical compartments, supporting the use of three-dimensional printing in the customisation of orthopaedic treatments.
The project aims to develop methodologies providing insights about the progression of two major conditions affecting the knee joint. Specifically, the project focuses on studying: the progression of osteoarthritis, 1) assessing whether its advancement can be monitored since the early stages, 2) and joint prosthesis failure, representing the final treatment option in cases of end-stage osteoarthritis, but still with sub-optimal performance.
To investigate such relevant clinical issues, in silico models will be developed based on clinical imaging and key features of the prosthesis. Depending on the specific condition, the models will be driven either by evidence obtained through targeted motor tasks or by international standards. An in vitro study performed on knee tissues collected from patients undergoing total knee arthroplasty will further explore the impact of osteoarthritis on the functionality of these tissues, with particular focus on articular cartilage.
Neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease, are preceded by a long prodromal phase, which represents the best therapeutic window but whose diagnosis is difficult. In this pilot we will focus on prodromal patients with a high conversion rate to overt neurodegenerative diseases (patients with subjective cognitive decline, mild cognitive impairment, Down syndrome, REM behaviour sleep disorder).
We plan to develop multimodal markers to predict the risk and time to conversion from prodromal conditions to overt neurodegenerative diseases.
These markers will include clinical data (instrumental measurements, clinical scales), neuropsychological data (neuropsychological scales, caregiver questionnaires), imaging data, biological data (blood test results, biomarkers of neurodegeneration measured in peripheral tissues and biological fluids, omics).
Overall, this project will promote the development of non-invasive and low-cost screening strategies for the early diagnosis of neurodegenerative diseases.
Diabetes mellitus (DM) affects approximately 530 million people worldwide. The foot and locomotor system can be severely affected (in about 20% of these patients), and the associated treatment costs for healthcare systems are substantial. Due to the risk of ulceration, it is crucial to identify all potential predisposing factors so that both patients and healthcare professionals can immediately adopt protective measures.
Modern multi-instrumental assessments—such as plantar pressure analysis, joint kinematics, and weight-bearing 3D radiographic scans—can now more effectively support prevention as well as early and personalised care, particularly through the identification of new biomechanical-based biomarkers.
The study evaluates two distinct populations of patients with type 2 DM:
I) those without any ulcerative lesions, and
II) those who have already developed such lesions.
Clinical and metabolic data will be collected at Sant’Orsola Hospital in Bologna, biological and biochemical data at Maria Cecilia Hospital in Cotignola, and biomechanical and functional data at the IRCCS Rizzoli Orthopaedic Institute in Bologna.
The primary objective is to correlate all collected data with the risk of foot ulceration. The secondary objective is to create a reference database on the metabolic conditions and structural and dynamic alterations of the diabetic foot in relation to ulceration risks.
This will allow for a detailed and quantitative monitoring of disease progression and enable early identification of potential foot complications.
The pilot study “Accessible Measurements of Mobility and Deformity as Biomarkers for Orthopaedic Treatments (MAMBO)" is being conducted at the IRCCS Istituto Ortopedico Rizzoli in Bologna. The project is coordinated by the research teams of the "Movement Analysis and Functional Evaluation of Prostheses" laboratory and of the "Rare Skeletal Diseases" unit. The primary objective of the study is the functional and morphological characterisation of Multiple Osteochondromas, a rare musculoskeletal disorder marked by the development of benign osseocartilaginous tumours on the bone surface. These bony lumps may result in functional and postural impairments of the musculoskeletal system.
The characterization of Multiple Osteochondromas is performed using state-of-the art sensors such as: IMU sensors, to measure joint mobility and quantify motor deficits; Pressure platforms, to assess plantar pressure distribution; Full-body 3D scanners, to assess the skeletal and postural alterations. In addition, the pilot also aims at evaluating experimental protocols, based on operator-independent instrumentation, for patient monitoring in community and outpatient settings.
Adopting a holistic approach, MAMBO integrates morphological and postural evaluations, offering clinicians a more comprehensive understanding of the patient’s overall condition and the progression of the disease over time. Furthermore, the study defines functional parameters that can serve as prognostic and predictive factors for the clinical and surgical management of patients with this rare disease, as well as others with similar pathogenic mechanisms.