Publications

Objectives: Aging people with HIV are increasingly affected by multimorbidity and polypharmacy, which heighten the risk of drug–drug interactions (DDIs) and potentially inappropriate medications (PIMs). This study evaluated a multidisciplinary, AI-supported quality improvement intervention designed to optimize polypharmacy management in older people with HIV. Methods: People with HIV aged ≥50 years attending the Modena HIV Metabolic Clinic (MHMC) were invited to submit photos of their medications via WhatsApp. Images were processed by AI for optical character recognition and automatically reconciled with the electronic patient chart (EPC). AI recognition accuracy was 94% when validated against manual review. Pharmacists reviewed AI-generated reports from the NavFarma® decision support system, generated alerts for PIM, defined according to Beers and the STOPP/START criteria, DDIs, anticholinergic burden (ACB), and risks of QTc prolongation and nephrotoxicity. Primary outcome was agreement between patient-reported and EPC-recorded medications. Secondary outcomes included pill burden, total prescribed drugs and actionable alerts. Results: Of 181 participants (median age 63 years; 72% male), 111 (61.3%) showed complete agreement between EPC and patient lists, while 70 (38.7%) had discrepancies. Pharmacist evaluation identified major DDIs in 70.4% of cases, ACB in 26.5%, QTc-prolonging drugs in 81.6% and nephrotoxic agents in 95.9%. Participants with ≥10 total prescribed drugs had higher frailty, pill burden and PIM. Conclusions: AI-assisted medication reconciliation combined with pharmacist review improved the identification of PIM and medication-related risks, supporting safer prescribing in people with HIV. This model aligns with international calls to improve prescribing safety and offers a scalable framework for integrating digital tools into multidisciplinary HIV care.

Male infertility is a significant yet under-addressed global health condition, and testicular ultrasound (US) plays a central role in its diagnostic evaluation. We introduce and publicly release TesticulUS-Real, the first multicenter testicular US segmentation dataset, addressing the absence of annotated public benchmarks for this anatomy. The dataset comprises 1,053 real ultrasound images acquired from two independent clinical institutions, with expert segmentation masks obtained through a standardized annotation and consensus review protocol. Leveraging this resource, we define an open-organ segmentation protocol to evaluate how models trained on existing multi-organ US data transfer to a previously unseen anatomical target. Beyond the dataset release, we conduct a broad cross-organ segmentation generalization study on ultrasound data. Using the UUSIC benchmark, we evaluate generalization across five anatomical regions and independent acquisition centers, comparing task-specific segmentation models, generalization-oriented ultrasound methods, and SAM-based foundation models under fully automatic inference. Alongside this benchmark, we introduce Cond-UNet, a lightweight conditional U-Net that combines Feature-wise Linear Modulation (FiLM) with our newly proposed shared attention conditioning (SAC) to obtain adaptive organ-aware representations. Experiments show that Cond-UNet achieves the best average cross-organ generalization performance across the UUSIC organs while using fewer parameters and lower computational cost than foundation-model alternatives. In the open-organ setting, the proposed testicular dataset enables a direct analysis of how different model families behave when facing an unseen ultrasound anatomy, highlighting the role of large-scale pretraining for foundation models and the robustness of organ-aware conditioning in lightweight architectures. The code is publicly released at https://github.com/AImageLab-zip/US_Cond-UNet, and the dataset at https://ditto.ing.unimore.it/testiculus/.

Intraoral photographs (IOPs) provide a low-cost record of tooth appearance, alignment, soft tissue, and occlusal relationships. We present IOP-Compass, a dataset, benchmark, and annotation resource built on 1,000 patients from the Bite2Text collection. It comprises 5,000 standardized clinical photographs, five views per patient, with view labels and expert-verified tooth-instance masks carrying FDI numbers, together with frozen patient-disjoint splits. The dataset was produced through a browser-based human-in-the-loop annotation platform that we also released. Using IOP-Compass, we benchmark a pipeline representative of the current literature for view classification, region-of-interest extraction, and FDI-aware tooth instance segmentation, providing reference results and ablations across alternative pipeline components. View classification is near-saturated on the benchmark, while FDI-aware instance segmentation remains the main challenge. Both the dataset and code are publicly released.

In this work, we target Handwritten Text Recognition (HTR) in low-resource scenarios, which arise from underrepresented languages, rare scripts, and degraded visual conditions typical of historical documents. We introduce SCAM (Sahidic Coptic Ancient Manuscripts), a new line-level dataset built from digitized ancient manuscripts written in the extinct Sahidic Coptic dialect. The dataset reflects a realistic and challenging setting, as it combines heterogeneous acquisition conditions across libraries with typical manuscript degradations such as ink fading, bleed-through, and material deterioration. In addition to visual complexity, SCAM poses significant linguistic challenges due to the scarcity of resources for Sahidic Coptic, its uncommon alphabet, and dialect-specific diacritics. To support research in low-resource HTR, we benchmark several state-of-the-art approaches based on different paradigms, highlighting their limitations and strengths in this setting. Our results underline the gap between current HTR performance on well-resourced modern scripts and historically grounded, low-resource scenarios, thus providing a reference point for future developments.

The complete workflow of the RI-PIENO framework is presented, a system for refueling itinerary optimization that extends the original PIENO design. While prior work introduced the conceptual modules of RI-PIENO, their operational pipeline was not described in detail. This study makes the workflow explicit, covering the end-to-end process from CAN Bus data acquisition and stop detection to the construction of daily trip graphs, refueling optimization, and mileage prediction. By clarifying the sequence of operations, the contribution provides a reproducible and extensible foundation for future research and development.

Con la crescente diffusione delle tecnologie di intelligenza artificiale, i moderni sistemi di apprendimento operano in ambienti sempre più dinamici, in cui distribuzioni dei dati, task e obiettivi evolvono nel tempo. I paradigmi statici tradizionali faticano a tenere il passo con tali mutamenti, con conseguente degrado delle prestazioni, perdita di conoscenze acquisite o riaddestramenti poco efficienti. Affrontare tali sfide richiede meccanismi capaci di trasferire conoscenza attraverso dimensioni temporali e strutturali, dai dati sequenziali ai flussi di task, fino al riuso e alla combinazione di interi modelli preesistenti. Questa tesi analizza come i sistemi di apprendimento possano evolvere insieme ai propri ambienti, sfruttando informazioni strutturate e conoscenze pregresse. Il lavoro si articola in tre direttrici principali: la comprensione dei dati temporali, l'apprendimento continuo e la composizione di modelli, con l'obiettivo di comprendere come le informazioni apprese in un contesto possano essere riutilizzate o adattate in un altro. La prima parte è dedicata all'apprendimento temporale da dati visivi, considerando i flussi video come serie temporali strutturate. Viene proposta una formulazione basata sulla coerenza temporale per la localizzazione di anomalie (CSL-TAL) in assenza di annotazioni a livello di frame; seguono modelli probabilistici e rappresentazioni basate sul flusso per il tracciamento multi-oggetto (TrackFlow), e un approccio per la stima della distanza degli oggetti da visione monoculare (DistFormer), che integra un ragionamento centrato sull'oggetto nei processi temporali. Nel loro insieme, questi studi mostrano come l'informazione temporale possa essere sfruttata per ottenere rappresentazioni visive più generali e interpretabili. La seconda parte affronta l'apprendimento continuo, in cui i dati si presentano come flusso. CHARON propone un framework efficiente per il riconoscimento di azioni basato su scheletri, che combina mascheramento e compressione per ottimizzare memoria e stabilità. CGIL introduce invece un metodo di adattamento continuo per modelli visione-linguaggio di grandi dimensioni mediante generative latent replay, mantenendo le capacità zero-shot e consentendo l'apprendimento incrementale dei prompt. Questi contributi reinterpretano l'apprendimento continuo come una progressione temporale strutturata, intesa come una sequenza di task in evoluzione. La parte finale esplora il trasferimento di conoscenza tra modelli attraverso fusione e aritmetica dei modelli. Invece di adattare un singolo modello nel tempo, l'obiettivo è combinare modelli pre-addestrati per generare nuove capacità. Il framework PASTA mostra come componenti specializzati possano essere composti nello spazio dei parametri per generalizzare tra domini. Successivamente, vengono analizzate tecniche a basso rango (MoDER e Core Space) e basate sul gradiente (GradFix) per fondere modelli, consentendo la creazione di nuove reti tramite operazioni dirette sui parametri. Questi approcci permettono di sintetizzare reti specifiche per task, rappresentando una nuova forma di evoluzione nello spazio dei modelli anziché in quello dei dati. Nel complesso, la tesi offre una prospettiva unificata sul trasferimento di conoscenza nei sistemi in evoluzione. Collegando apprendimento temporale, adattamento continuo e composizione di modelli, reinterpreta l'analisi delle serie temporali come principio generale di trasferimento tra rappresentazioni che cambiano nel tempo. Il quadro che emerge evidenzia il ruolo di struttura, modularità e riuso nella costruzione di sistemi di apprendimento scalabili, adattivi e resilienti, capaci non solo di interpretare un mondo in trasformazione, ma anche di trasformarsi in risposta a esso.