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arXiv 2022年

作者： Bandini, Andrea Dousty, Mehdy Hitzig, Sander L. Craven, B. Catharine Kalsi-Ryan, Sukhvinder Zariffa, José KITE Research Institute Toronto Rehabilitation Institute University Health Network Canada The BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Institute of Biomedical Engineering University of Toronto TorontoON Canada Rehabilitation Sciences Institute Temerty Faculty of Medicine University of Toronto TorontoON Canada St. John's Rehab Research Program Sunnybrook Research Institute Sunnybrook Health Sciences Centre TorontoON Canada Department of Occupational Science & Occupational Therapy Temerty Faculty of Medicine University of Toronto TorontoON Canada Brain and Spinal Cord Rehabilitation Program Toronto Rehabilitation Institute University Health Network TorontoON Canada Division of Physical Medicine and Rehabilitation Department of Medicine Temerty Faculty of Medicine University of Toronto TorontoON Canada Department of Physical Therapy University of Toronto TorontoON Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoON Canada

Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 hours of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc);the average duration of individual interactions (Dur);the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord Independent Measure - SCIM). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use. © 2022, CC BY-NC-ND.

关键词： Video recording

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCai FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (Cai) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test Cai algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCai2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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How can we learn (more) from challenges? A statistical approach to driving future algorithm development

arXiv

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arXiv 2021年

作者： Roß, Tobias Bruno, Pierangela Reinke, Annika Wiesenfarth, Manuel Koeppel, Lisa Full, Peter M. Pekdemir, Bünyamin Godau, Patrick Trofimova, Darya Isensee, Fabian Moccia, Sara Calimeri, Francesco Müller-Stich, Beat P. Kopp-Schneider, Annette Maier-Hein, Lena Heidelberg Germany Medical Faculty Heidelberg University Heidelberg Germany Heidelberg Germany Department of Mathematics and Computer Science University of Calabria Rende Italy Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany Section Clinical Tropical Medicine Heidelberg University Heidelberg Germany Heidelberg Germany The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Heidelberg Germany

Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner. While the validation on identical data sets was a great step forward, results analysis is often restricted to pure ranking tables, leaving relevant questions unanswered. Specifically, little effort has been put into the systematic investigation on what characterizes images in which state-of-the-art algorithms fail. To address this gap in the literature, we (1) present a statistical framework for learning from challenges and (2) instantiate it for the specific task of instrument instance segmentation in laparoscopic videos. Our framework relies on the semantic meta data annotation of images, which serves as foundation for a General Linear Mixed Models (GLMM) analysis. Based on 51,542 meta data annotations performed on 2,728 images, we applied our approach to the results of the Robust Medical Instrument Segmentation Challenge (ROBUST-MIS) challenge 2019 and revealed underexposure, motion and occlusion of instruments as well as the presence of smoke or other objects in the background as major sources of algorithm failure. Our subsequent method development, tailored to the specific remaining issues, yielded a deep learning model with state-of-the-art overall performance and specific strengths in the processing of images in which previous methods tended to fail. Due to the objectivity and generic applicability of our approach, it could become a valuable tool for validation in the field of medical image analysis and beyond. and segmentation of small, crossing, moving and transparent instrument(s) (parts). © 2021, CC BY-NC-ND.

关键词： Deep learning

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Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces

arXiv

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arXiv 2020年

作者： Schillaci, Guido Villalpando, Antonio Pico Hafner, Verena V. Hanappe, Peter Colliaux, David Wintz, Timothee BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy Adaptive Systems Group Humboldt-Universität zu Berlin Germany Sony Computer Science Laboratories Paris France

This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of lowdimensional features from images, and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals, and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Correction to: Unveiling social distancing mechanisms via a fish-robot hybrid interaction

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Biological cybernetics 2022年第3期116卷 387页

作者： Donato Romano Cesare Stefanini The BioRobotics Institute Sant'Anna School of Advanced Studies Viale Rinaldo Piaggio 34 56025 Pontedera Pisa Italy. donato.romano@santannapisa.it. Department of Excellence in Robotics and AI Sant'Anna School of Advanced Studies 56127 Pisa Italy. donato.romano@santannapisa.it. The BioRobotics Institute Sant'Anna School of Advanced Studies Viale Rinaldo Piaggio 34 56025 Pontedera Pisa Italy. Department of Excellence in Robotics and AI Sant'Anna School of Advanced Studies 56127 Pisa Italy. Healthcare Engineering Innovation Center (HEIC) Khalifa University Abu Dhabi UAE.

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P079 Low-intensity Pulsed Ultrasound reflects the status of mucosal injury and repair through stage-specific miRNAs in experimental model of Ulcerative Colitis

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Journal of Crohn's and Colitis 2024年第Supplement_1期18卷 i348–i348页

作者： Pineda Chavez, S E Carriles Linares, A Rizzo, G Wozny, M Cafarelli, A Mori, G Sorriento, A Loy, L Dal Buono, A Gabbiadini, R Bezzio, C Lucchetti, D Sgambato, A Ricotti, L Repici, A Armuzzi, A Vetrano, S Humanitas University Biomedical Sciences Pieve Emanuele- Milan Italy IRCCS Humanitas Research Hospital Laboratory of Gastrointestinal Immunopathology- Department of Gastroenterology Rozzano- Milan Italy Scuola Superiore Sant’Anna The BioRobotics Institute Pisa Italy Scuola Superiore Sant’Anna Department of Excellence in Robotics & AI Pisa Italy Humanitas Clinical and Research Center - IRCCS - IBD Unit- Department of Gastroenterology Rozzano- Milan Italy Università Cattolica del Sacro Cuore Institute of General Pathology Rome Italy Humanitas Clinical and Research Center-IRCCS Endoscopy Unit Rozzano- Milan Italy

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Soft yet Effective Robots via Holistic Co-Design

arXiv

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arXiv 2025年

作者： Stölzle, Maximilian Pagliarani, Niccolò Stella, Francesco Hughes, Josie Laschi, Cecilia Rus, Daniela Cianchetti, Matteo Santina, Cosimo Della Zardini, Gioele Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Cognitive Robotics Delft University of Technology Mekelweg 2 CD Delft2628 Netherlands The BioRobotics Institute Scuola Superiore Sant’Anna Pisa56025 Italy Embodied AI AG Lausanne1015 Switzerland CREATE Lab EPFL Lausanne1015 Switzerland Advanced Robotics Centre Department of Mechanical Engineering National University of Singapore 117575 Singapore Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

Soft robots promise inherent safety via their material compliance for seamless interactions with humans or delicate environments. Yet, their development is challenging because it requires integrating materials, geometry, actuation, and autonomy into complex mechatronic systems. Despite progress, the field struggles to balance task-specific performance with broader factors like durability and manufacturability—a difficulty that we find is compounded by traditional sequential design processes with their lack of feedback loops. In this perspective, we review emerging co-design approaches that simultaneously optimize the body and brain, enabling the discovery of unconventional designs highly tailored to the given tasks. We then identify three key shortcomings that limit the broader adoption of such co-design methods within the soft robotics domain. First, many rely on simulation-based evaluations focusing on a single metric, while real-world designs must satisfy diverse criteria. Second, current methods emphasize computational modeling without ensuring feasible realization, risking sim-to-real performance gaps. Third, high computational demands limit the exploration of the complete design space. Finally, we propose a holistic co-design framework that addresses these challenges by incorporating a broader range of design values, integrating real-world prototyping to refine evaluations, and boosting efficiency through surrogate metrics and model-based control strategies. This holistic framework, by simultaneously optimizing functionality, durability, and manufacturability, has the potential to enhance reliability and foster broader acceptance of soft robotics, transforming human-robot interactions. © 2025, CC BY.

关键词： Intellectual property core

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A Test Bench to Assess Systems for Respiratory Monitoring of Workers

A Test Bench to Assess Systems for Respiratory Monitoring of...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Martina Zaltieri Joshua Di Tocco Daniela Lo Presti Carlo Massaroni Domenico Formica Emiliano Schena Giacomo D'Alesio Mariangela Filosa Jessica D'Abbraccio Ilaria Cesini Luca Massari Calogero Maria Oddo Marco Di Rienzo Maurizio Ferrarin Michele Arturo Caponero Maria Sabrina Sarto School of Engineering Università Campus Bio-Medico di Roma Rome Italy The Biorobotics Institute Sant' Anna School of Advanced Studies Pisa Italy Department of Excellence in Robotics and AI The Biorobotics Institute Sant' Anna School of Advanced Studies Pisa Italy IRCSS Fondazione Don Carlo Gnocchi Milan Italy Photonics Micro and Nanostructures Laboratory ENEA Research Center of Frascati Rome Italy School of Engineering La Sapienza Università di Roma Rome Italy

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Safety at work is driving the development of new technological solutions in the era of Industry 4.0. Among others, systems able to monitor physiological parameters are gaining high relevance since such devices have the potential to improve the workers' safety. In this perspective, the respiratory rate can provide useful information since it is sensitive to many environmental stressors. In this study, we focused on the development of a test bench to assess the performance of a wearable system for respiratory monitoring. The presented system achieves data collection and synchronization from FBG sensors, a reference flowmeter and, an M-IMU unit. In addition, a GUI has been developed in order to guide the volunteer in the execution of selected movements during the experimental session. The proposed system could further facilitate the design and development of solutions for assessing wearables for workers' activities monitoring.

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Sustainable hydrogels from slaughterhouse waste: Vitreous humor for tissue engineering applications

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International journal of biological macromolecules 2025年 318卷 144897页

作者： Peter R Corridon Makayla J Fahmy Marie Arjemandi Hamda Alkaabi Sara Khaled Alameri Shiv Dutt Purohit Diego Trucco Leonardo Ricotti Department of Biomedical Engineering and Biotechnology College of Medicine and Health Sciences Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Healthcare Engineering Innovation Group Khalifa University Abu Dhabi United Arab Emirates Center for Biotechnology Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The BioRobotics Institute Scuola Superiore Sant'Anna 56127 Pisa Italy. Electronic address: peter.corridon@ku.ac.ae. Department of Immunology and Inflammation Imperial College London United Kingdom. Department of Biomedical Engineering and Biotechnology College of Medicine and Health Sciences Khalifa University of Science and Technology Abu Dhabi United Arab Emirates. The BioRobotics Institute Scuola Superiore Sant'Anna 56127 Pisa Italy Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna 56127 Pisa Italy.

Bioprocessing of slaughterhouse waste has paved the way for sustainable tissue engineering approaches that align with circular economy principles. Recent research demonstrates that efficient waste management across the entire food system can repurpose discarded tissues to extract bioactive molecules, tissue/organ templates, and biphasic materials. Hydrogels are notable examples of such materials, consisting of a unique porous network of biological macromolecules and liquid components. This architecture allows them to retain water while preserving their structural integrity and mechanical properties. The vitreous humor (VH) represents a compelling case within this paradigm. This perspective review explores the potential of animal-derived VH as a biomaterial for therapeutic applications by outlining its key structural and functional characteristics and examining its upcycling into functional hydrogels. The review then evaluates the biophysical properties of this gel obtained from both conventional (bovine, ovine, porcine, and caprine) and exotic (camelid) animal sources. Insights from the literature suggest that VH-based hydrogels show promise for various applications in additive manufacturing and regenerative medicine. Finally, the review identifies critical gaps in the research and challenges that must be addressed to facilitate the clinical translation of VH-derived biomaterials.

关键词： Biovalorization Circular economy Hydrogels Slaughterhouse waste Sustainable tissue engineering Vitreous humor

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A machine learning model to predict progression from subjective cognitive decline to mild cognitive impairment and dementia: a 12-year follow-up study

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Alzheimer's & Dementia 2023年第S24期19卷

作者： Salvatore Mazzeo Michael Lassi Sonia Padiglioni Valentina Moschini Alberto Arturo Vergani Maenia Scarpino Giulia Giacomucci Rachele Buralli Carmen Morinelli Carlo Fabbiani Juri Balestrini Giulia Galdo Silvia Bagnoli Filippo Emiliani Assunta Ingannato Benedetta Nacmias Sandro Sorbi Antonello Grippo Alberto Mazzoni Valentina Bessi Research and Innovation Centre for Dementia-CRIDEM AOU Careggi Florence Florence Italy University of Florence Florence Florence Italy SalvatoreMazzeo Research and Innovation Centre for Dementia-CRIDEM AOU Careggi Florence Florence Italy. The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Pisa Italy Research and Innovation Centre for Dementia-CRIDEM AOU Careggi Pisa Pisa Italy IRCCS Fondazione Don Carlo Gnocchi Florence Florence Italy

Background Subjective cognitive decline (SCD) represents a target population to be screened for dementia. Previous studies identified demographic, cognitive and generic features associated with higher risk of progression to mild cognitive impairment (MCI) and Alzheimer’s disease (AD). We aim to test the accuracy of a machine learning model, trained on features derived from non-invasive and easily accessible techniques, in predicting conversion from SCD to MCI and AD. Method We included 150 SCD patients who underwent neuropsychological examination, assessment of cognitive complaint, mood disorders and cognitive reserve, APOE genotyping at baseline. All the patients underwent clinical-neuropsychological follow-up every 12-24 months for a mean time of 12 years. Result During follow-up, 63 patients were classified as progressive SCD (p-SCD, 42.0%[95% C.I. = 34.1:49.9], including 41 MCI (27.3% [95%C.I. = 20.2:34.5]) and 22 AD (14.7% [95%C.I. = 9.0:20.3]). 87 patients were classified as non-progressive SCD (np-SCD, 58.0% [95%C.I. = 50.1:65.9]). We split the whole sample into a training (80%) and a test group (20%). A gradient boosted trees algorithm was trained on 15 selected features including: age, APOE, education, test assessing for global cognition, immediate and delayed verbal, ecological memory and working memory, language, visuospatial abilities, phonemic fluency and cognitive reserve (Fig.1). This model showed a good accuracy (0.83, AUC = 0.78) in distinguishing p-SCD and np-SCD in the test group (Fig.2 and Fig.3). Conclusion Our machine learning models might represent a reliable, cost-effective and globally scalable tool for a first step screening of SCD patients before confirmation of AD pathology via more invasive and expensive tests.

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