“3rd AIxIA Workshop on Artificial Intelligence for Healthcare” and “5th Data4SmartHealth Workshop”

Program Schedule

Wednesday, 27 November 2024

10:30: Opening – Room D1.03

• The opening ceremony

10:35: Keynote – Room D1.03

• Sara Montagna: Bridging AI and Healthcare: Trustworthy and Transparent Diagnostic Solutions with Informed Machine Learning

11:30: Medical Imaging and Anomaly Detection (MIAD) – Room D1.03

• L. Marconi, E. Pirovano and F. Cabitza: CLARITY AI: A Comprehensive Checklist Integrating Established Frameworks for Enhanced Research Quality in Medical AI Studies

• A. Marzullo and M. B. M. Ranzini: Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?

• P. Bruno, A. Cirella, E. Di Cesare, G. Greco, A. Guzzo, P. Palumbo, S. J. Santini, G. Sinatti, P. Vittorini, C. Balsano and F. Calimeri: To Heart via Liver: a Study on Prognostic Stratification of Heart Disease in MASLD Patients using Machine Learning Models

• C. Dodaro, G. Galatà, M. Maratea, C. Marte and M. Mochi: Nuclear Medicine Rescheduling Problem: A Logic-based Approach

15:00: Working Group Meeting – Room D1.03

• Annual meeting of the AI and Healthcare Working Group of AIxIA

16:00: Minute Madness – Lightning Talks: short papers – Room D1.03

• A. Monaldini, A. Vozna and S. Costantini: Blueprint Personas in Digital Health Transformation

• E. De Rose, C. Adornetto, F. Calimeri and G. Greco: Features selection throught autoencoder filtering and DeepShap: an iterative algorithm

• L. Sanna, S. Magnolini, P. Bellan, S. Ghanbari Haez, M. Segala, M. Consolandi and M. Dragoni: Doctor, is it normal? Enabling medical chatbots to provide certified replies to normalcy questions

• J. Bolt, A. Berghuis, A. Hommersom, M. Lombaers, J. Pijnenborg and S. Renooij: Bayesian Networks in Medicine: Presenting Query Response Uncertainty for Decision Support

• L. Portinale, G. Leonardi and A. Santomauro: Similarity-based positional encoding for enhanced classification in medical images

• F. Angeli: An Educational Approach for Neurodiverse Children Using AI: A Case Study on DSA, Autism, and Dyscalculia

• M. Macrì, P. Bruno and C. Dodaro: Deep Learning Approaches for Segmentation and Classification of Breast Ultrasound Images

• P. Ribino, M. Mannone, C. Di Napoli, G. Paragliola, D. Chicco and F. Gasparini: Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering

• A. Basile, F. Calefato, F. Lanubile, G. Logroscino, G. Mallardi and B. Tafuri: A Preliminary Study on Augmenting Neuroimaging data using a Diffusion Model

16:15: Minute Madness – Lightning Talks: non-original dissemination and discussion papers – Room D1.03

• B. Salvatori, S. Wegener, G. Kotzaeridi, A. Herding, F. Eppel, I. Dressler-Steinbach, W. Hernich, A. Piersanti, M. Morettini, A. Tura and C. Göbl: Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

• B. Yousefi, F. Melograna, G. Galazzo, N. van Best, M. Mommers, J. Penders, B. Schwikowski and K. Van Steen: Capturing the dynamics of microbial interactions through individual-specific networks

• R. Khatun, S. Chatterjee, B. Christoph, M. Wadepohl, O. J. Ott, S. Semrau, R. Fietkau, A. Nürnberger, U. Gaipl and B. Frey: Complex-valued neural networks to speed-up MR Thermometry during Hyperthermia using Fourier PD and PDUNet

• Z. Li, F. Melograna, H. Hoskens, D. Duroux, M. Marazita, S. Walsh, S. Weinberg, M. Shriver, B. Müller-Myhsok, P. Claes and K. Van Steen: netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

• P. Bruno, F. Calimeri, F. Filice, C. Marte and S. Perri: IDADA: A Blended Inductive-Deductive Approach for Data Augmentation

• C. Radhakrishna, K. V. Chintalapati, S. C. H. Ram Kumar, R. Sutrave, H. Mattern, O. Speck, A. Nürnberger and S. Chatterjee: SPOCKMIP: Segmentation of Vessels in MRAs with Enhanced Continuity using Maximum Intensity Projection as Loss

• C. Adornetto, P. Bruno, F. Calimeri, E. De Rose, G. Greco and A. Quarta: AI-Driven Innovations in Healthcare: Bridging Imaging and Genomics for Advanced Disease Insights

• A. Zanga, A. Bernasconi, P. J. F. Lucas, H. Pijnenborg, C. Reijnen, M. Scutari and A. C. Constantinou: Federated Causal Discovery with Missing Data in a Multicentric Study on Endometrial Cancer

• G. H. Bahre, H. Hamidi, A. Sellergren, L. A. Celi, F. Calimeri and L. Seyyed-Kalantari: Fairness Of AI Models in vector embedded Chest X-ray representations

16:30: Poster showcase and discussion – Room D1.03

All attendees are encouraged to directly and freely engage with the speakers, and open discussions and networking are explicitly fostered among the whole community.

Thursday, 28 November 2024

10:30: Data4SmartHealth – Room D1.03 – See https://www.data4smarthealth.it for details.

• F. Boschello: AI based predicting tool for suicide attempts 

• D. Crazzolara: Can AI support the management of physical activity in people with type 1 diabetes?

• M. Farsad: Integrating AI in PET imaging for brain tumors

• M. Santarelli: AI-driven diagnosis and prediction: potential orthopedic clinical applications

13:30: Prognostic Models and Risk Assessment (PMRA) – part 1 – Room D1.03

• A. Calzoni, M. Savardi and A. Signoroni: Bimodal ECG-PCG Cardiovascular Disease Detection: a Close Look at Transfer Learning and Data Collection Issues

• A. Bernasconi, A. Balordi, A. Zanga and R. Cabañas: On Counterfactual Explainations of Cardiovascular Risk in Adolescents and Young Adults Breast Cancer Survivors

• G. Aguzzi, M. Magnini, S. Ferretti, G. P. Salcuni and S. Montagna: Applying Retrieval-Augmented Generation on Open LLMs for a Medical Chatbot Supporting Hypertensive Patients

14:15: Prognostic Models and Risk Assessment (PMRA) – part 2 – Room D1.03

• N. Rocchi, A. Bernasconi, A. Zanga, A. Gronchi, D. Callegaro, P. G. Casali, S. Provenzano, R. Miceli, A. Trama and F. Stella: A Causal Discovery Workflow for Rare Cancers: Case Study on Soft Tissue Sarcoma

• S. Ndlalane, O. Otegbeye and A. Ezugwu: Strategic Optimization of Blood Allocation in Blood Banks for Enhanced Resource Utilization

• G. Tinè and R. Miceli: A-MALA: A New Adaptive Version of the Metropolis Adjusted Langevin Algorithm for Survival Prediction in a High-Dimensional Framework

15:00: Techniques and Model Optimization (TMO) – Part 1 – Room D1.03

• V. V. N. K. C. Atkuri: Balancing Accuracy and Safety in AI: A Novel Adversarial Training Approach

• M. Locatelli, R. C. Cerioli, D. Besozzi, A. Hommersom and F. Stella: Reinforcement Learning and Fuzzy Logic Modelling for Personalized Dynamic Treatment

16:00: Techniques and Model Optimization (TMO) – Part 2 – Room D1.03

• T. Kolajo and O. Daramola: Predicting COVID-19 Post-vaccination Mortality in Persons with Cardiovascular Disease Risk Factors Using Explainable AI

• M. Serenelli, M. Quadrini, M. Óskarsdóttir and M. Loreti: Encoding Methods Comparison for Stress Detection

16:30: Clinical Decision Support and Digital Health (CDSDH) – part 1 – Room D1.03

• M. Dragoni, G. Apriceno and T. Bailoni: Validating a Functional Status Knowledge Graph in a Large-scale Living Lab

• S. Fazio, P. Ribino, F. Gasparini, N. Marwan, P. Fazio, M. Gherardi and M. Mannone: A physics-based view of brain-network alteration in neurological disease

• P. V. de Campos Souza and M. Dragoni: Artificial Intelligence in Emergency Care: Implementing Machine Learning for Triage Optimization in Italian Hospitals

17:15: Clinical Decision Support and Digital Health (CDSDH) – part 1 – Room D1.03

• M. Hosseinipour, L. Bergamin, H. Kotler, G. Gennaro and F. Aiolli: Addressing Challenges in Image Translation for Contrast-Enhanced Mammography using Generative Adversarial Networks

• F. de Arriba-Pérez and S. García-Méndez: Detecting anxiety and depression in dialogues: a multi-label and explainable approach

• M. Consolandi, S. Magnolini and M. Dragoni: Risk Communication in Healthcare: The Management of Misunderstandings

18:00: Awards Ceremony – Room D1.03

• Best Paper Award and Best Dissemination Award

18:10: Closing – Room D1.03

The closing ceremony.