301 research outputs found
AI-based medical image analysis and interpretation: from feature extraction to decision support
No full textNegli ultimi anni, abbiamo assistito a un'enorme diffusione di modelli di Intelligenza Artificiale (IA) ad elevate prestazioni che affrontano diverse sfide nel campo della visione artificiale in ambito biomedico. Tuttavia, l'integrazione clinica di queste tecnologie è ancora limitata a causa di sfide come la scarsità di dati e la necessità di risultati interpretabili. La tesi propone la creazione di pipeline automatizzate per l'analisi di immagini cliniche utilizzando l'IA, con tre casi studio in ambito oncologico, cardiologico e neurologico. Le pipeline mirano a performance elevate, garantendo allo stesso tempo riproducibilità, interpretabilità e facilità di generalizzazione. I primi due casi riguardano sistemi per migliorare l'efficienza diagnostica nello screening di microcalcificazioni mammarie maligne e malattie coronariche, rispettivamente. Nel terzo caso, viene presentato un workflow per la predizione della prognosi e l’identificazione di nuovi biomarcatori utilizzando sequenze di risonanza magnetica di pazienti con ictus ischemico acuto. Infine, viene approfondito il tema dell'impiego di modelli generalisti o “fondativi" che sta gradualmente cambiando il panorama dell'IA. Nel dominio medico, questo approccio promette di superare limitazioni comuni, in particolare quelle legate alla quantità e qualità dei dati. Viene presentato uno studio di validazione disegnato per testare l'adattamento di un algoritmo di segmentazione progettato per un uso generale su un set di dati reale di pazienti con ictus emorragico. Le alte prestazioni ottenute da tale sistema mostrano come questo nuovo approccio sia facile da implementare e possa quindi accelerare il processo di segmentazione manuale dell'ematoma dalla TAC di pronto soccorso. Se, da un lato, questo dimostra il grande potenziale dell'approccio generalista, è anche innegabile che diverse sfide, in particolare legate alla sfera medico-legale ed etica, devono essere affrontate tempestivamente per garantire la sicurezza di tali software al fine di arrivare a migliorare l'accessibilità, l'equità e l'inclusività nell'assistenza sanitaria.Over the past few years, we have witnessed an explosion of highly performing Artificial Intelligence (AI) models addressing diverse tasks in computer vision within the healthcare domain. However, their integration into everyday clinical practice remains limited. The field of AI-based medical image analysis faces multiple challenges, including a scarcity of data, variable image quality, and the imperative for interpretable and generalizable results. Conversely, the potential benefits of employing such technology in routine clinical practice are extensive. These include the possibility of seamlessly incorporating fully automated decision support systems at different stages of the clinical routine, ranging from early diagnosis to prognosis prediction. This thesis aims to delineate a comprehensive workflow for building fully automated and easy to customize AI-based medical image analysis pipelines. Three distinct case studies, designed and analyzed in collaboration with highly specialized European centers, are presented. Each case pertains to a specific medical domain - oncological, cardiological, or neurological - presenting unique challenges from both clinical and technical perspectives. The proposed pipelines are crafted to meet specific criteria: high performance, reproducibility, ease of generalization, and interpretability by the final clinical user, who must view the system as trustworthy, even without expertise in the technical implementation. Additionally, the applications have been meticulously designed to demand limited computational resources while maintaining optimal performance. The first two case studies present fully automated systems designed to enhance the efficiency and diagnostic accuracy during screening programs. The first system accurately identifies malignant microcalcifications from mammograms during breast screening programs to mitigate the high false positive rate. In the second case, a quick and accurate automated system is introduced to rule out patients requiring further clinical investigations during coronary artery disease screenings, based on the degree of occlusion of the three main coronary arteries visible from cardiac CT angiography. These pipelines are specifically crafted to alleviate time-consuming and operator-dependent tasks. In the last case study, an easily generalizable workflow for prognosis prediction and biomarkers discovery is discussed. The presented pipeline is capable of identifying novel imaging biomarkers from follow-up MRI sequences with the objective of predicting poor long-term functional outcomes in acute ischemic stroke patients. This kind of system has the potential to fully exploit the information content in routinely acquired clinical images, providing insights into the pathophysiological mechanisms of the disease and predicting its possible evolution. This goes beyond qualitative biomarkers or simple lesion measurements, which are often the only indicators used to guide the best clinical intervention. Finally, the recent emergence of generalist foundation models that are gradually shifting the landscape of AI is deeply discussed. In the medical domain, this approach holds significant promise in overcoming common limitations, particularly those related to data quantity and quality. An evaluation study designed to test the adaptation of a general-purpose segmentation algorithm on a real dataset of patients with hemorrhagic ictus is presented. The high performance of the implemented system showcases a novel and easy-to-implement approach for expediting the manual hematoma delineation process from CT scans acquired in emergency rooms. If, on the one hand, this demonstrates the great potential of the generalist approach, it is also undeniable that various concerns, particularly from legal and ethical perspectives, must be promptly addressed to ensure the safety of the final supporting tools improving healthcare accessibility, fairness, and inclusivity
Economic factors affecting obesity: an application in Italy
No full textThe World Health Organization has stated that obesity is spreading around the world like a “global epidemic”. In 2004 the percentage of obese people in the Italian population was 9%, but the trend s increasing in recent years. Focusing on this country, the purpose of the paper is to analyze the socio-economic variables affecting obesity by means of a survey conducted in a consumer sample. Our analysis is based on a survey conducted in Italy, and the sample was composed of 999 consumers. We used a binary logit model and the dependent variable is body mass index (BMI), expressed in a dichotomic way (seriously overweight and obese, value 1, and normal weight, value 0). The results show that the condition of the seriously overweight and obese increases with age, especially in people over 65 of age. Also gender is correlated with the pathology: being seriously overweight and obese is far more likely for men than for women. An inverse relation was shown between obesity and education, and between obesity and the level of food knowledge. The results highlight that disadvantaged social categories are more susceptible to the problem of overweight and obesity. A policy implication of the analysis, to limit the spread of obesity, could lie in programs aimed at improving health and food awareness and focused on these minority groups.economics of obesity, BMI and consumer, logit model, Food Consumption/Nutrition/Food Safety, Health Economics and Policy,
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No full textDetecting pathogens and mounting immune responses upon infection is crucial for animal health. However, these responses come at a high metabolic price (McKean and Lazzaro, 2011, Kominsky et al., 2010), and avoiding pathogens before infection may be advantageous. The bacterial endotoxins lipopolysaccharides (LPS) are important immune system infection cues (Abbas et al., 2014), but it remains unknown whether animals possess sensory mechanisms to detect them prior to infection. Here we show that Drosophila melanogaster display strong aversive responses to LPS and that gustatory neurons expressing Gr66a bitter receptors mediate avoidance of LPS in feeding and egg laying assays. We found the expression of the chemosensory cation channel dTRPA1 in these cells to be necessary and sufficient for LPS avoidance. Furthermore, LPS stimulates Drosophila neurons in a TRPA1-dependent manner and activates exogenous dTRPA1 channels in human cells. Our findings demonstrate that flies detect bacterial endotoxins via a gustatory pathway through TRPA1 activation as conserved molecular mechanism.sponsorship: Vlaams Instituut voor Biotechnologie Alessia Soldano Luis Franco Guangda Liu Natalia Mora Emre Yaksi Bassem A Hassanr Fonds Wetenschappelijk Onderzoek G.0702.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0077.15 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0680.10 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0681.10 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0503.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0654.15 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0761.10N Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0596.12 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar Fonds Wetenschappelijk Onderzoek G.0565.07 Alessia Soldano Yeranddy A Alpizar Brett Boonen Alejandro Lopez-Requena Natalia Mora Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar KU Leuven GOA/14/011 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar European Commission IUAP P7/13 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekensr KU Leuven OT/12/091 Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talaverar KU Leuven PF-TRPLe Alessia Soldano Yeranddy A Alpizar Brett Boonen Luis Franco Alejandro Lopez-Requena Guangda Liu Natalia Mora Emre Yaksi Thomas Voets Rudi Vennekens Bassem A Hassan Karel Talavera (Vlaams Instituut voor Biotechnologie, Fonds Wetenschappelijk Onderzoek|G.0702.12, Fonds Wetenschappelijk Onderzoek|G.0077.15, Fonds Wetenschappelijk Onderzoek|G.0680.10, Fonds Wetenschappelijk Onderzoek|G.0681.10, Fonds Wetenschappelijk Onderzoek|G.0503.12, Fonds Wetenschappelijk Onderzoek|G.0654.15, Fonds Wetenschappelijk Onderzoek|G.0761.10N, Fonds Wetenschappelijk Onderzoek|G.0596.12, KU Leuven|GOA/14/011, KU Leuven|OT/12/091, European Commission|IUAP P7/13, KU Leuven PF-TRPLe)status: Publishe
Machine Learning-Based Approach towards Identification of Pharmaceutical Suspensions Exploiting Speckle Pattern Images
No full textParenteral artificial nutrition (PAN) is a lifesaving medical treatment for many patients worldwide. Administration of the wrong PAN drug can lead to severe consequences on patients’ health, including death in the worst cases. Thus, their correct identification, just before injection, is of crucial importance. Since most of these drugs appear as turbid liquids, they cannot be easily discriminated simply by means of basic optical analyses. To overcome this limitation, in this work, we demonstrate that the combination of speckle pattern (SP) imaging and artificial intelligence can provide precise classifications of commercial pharmaceutical suspensions for PAN. Towards this aim, we acquired SP images of each sample and extracted several statistical parameters from them. By training two machine learning algorithms (a Random Forest and a Multi-Layer Perceptron Network), we were able to identify the drugs with accurate performances. The novelty of this work lies in the smart combination of SP imaging and machine learning for realizing an optical sensing platform. For the first time, to our knowledge, this approach is exploited to identify PAN drugs
Adapting foundation models for rapid clinical response: intracerebral hemorrhage segmentation in emergency settings
No full textIntracerebral hemorrhage (ICH) is a medical emergency that demands rapid and accurate diagnosis for optimal patient management. Hemorrhagic lesions’ segmentation on CT scans is a necessary first step for acquiring quantitative imaging data that are becoming increasingly useful in the clinical setting. However, traditional manual segmentation is time-consuming and prone to inter-rater variability, creating a need for automated solutions. This study introduces a novel approach combining advanced deep learning models to segment extensive and morphologically variable ICH lesions in non-contrast CT scans. We propose a two-step methodology that begins with a user-defined loose bounding box around the lesion, followed by a fine-tuned YOLOv8-S object detection model to generate precise, slice-specific bounding boxes. These bounding boxes are then used to prompt the Medical Segment Anything Model for accurate lesion segmentation. Our pipeline achieves high segmentation accuracy with minimal supervision, demonstrating strong potential as a practical alternative to task-specific models. We evaluated the model on a dataset of 252 CT scans demonstrating high performance in segmentation accuracy and robustness. Finally, the resulting segmentation tool is integrated into a user-friendly web application prototype, offering clinicians a simple interface for lesion identification and radiomic quantification
Social Network to analyse the relationship between ‘victim-author’ and ‘motivation’ of violence against women in Italy.
No full textThe paper aims to analyse the phenomenon of Violence against women in the Italian context during 2020. It proposes to study the relationship between ‘victim-author’ and ‘motivation’ in femicides committed in domestic environment. By means of the properties of the Social Network Analysis on bimodal data, the study detected main actors and motivations that generated the homicides with female victims. At the same time, the structural relationships allowed to investigate the existence of motivations that better characterized the action of the various actors. The bipartite graph visualization and centrality scores calculated have demonstrated the effectiveness of the methodology for the pursued objectives
DeepMiCa: Automatic segmentation and classification of breast MIcroCAlcifications from mammograms
No full textBackground and objective: Breast cancer is the world's most prevalent form of cancer. The survival rates have increased in the last years mainly due to factors such as screening programs for early detection, new insights on the disease mechanisms as well as personalised treatments. Microcalcifications are the only first detectable sign of breast cancer and diagnosis timing is strongly related to the chances of survival. Nevertheless microcalcifications detection and classification as benign or malignant lesions is still a challenging clinical task and their malignancy can only be proven after a biopsy procedure. We propose DeepMiCa , a fully automated and visually explainable deep-learning based pipeline for the analysis of raw mammograms with microcalcifications. Our aim is to propose a reliable decision support system able to guide the diagnosis and help the clinicians to better inspect borderline difficult cases. Methods: DeepMiCa is composed by three main steps: (1) Preprocessing of the raw scans (2) Automatic patch-based Semantic Segmentation using a UNet based network with a custom loss function appositely designed to deal with extremely small lesions (3) Classification of the detected lesions with a deep transfer-learning approach. Finally, state-of-the-art explainable AI methods are used to produce maps for a visual interpretation of the classification results. Each step of DeepMiCa is designed to address the main limitations of the previous proposed works resulting in a novel automated and accurate pipeline easily customisable to meet radiologists' needs. Results: The proposed segmentation and classification algorithms achieve an area under the ROC curve of 0 . 95 and 0 . 89 respectively. Compared to previously proposed works, this method does not require high performance computational resources and provides a visual explanation of the final classification results.Conclusion: To conclude, we designed a novel fully automated pipeline for detection and classification of breast microcalcifications. We believe that the proposed system has the potential to provide a second opinion in the diagnosis process giving the clinicians the opportunity to quickly visualise and inspect relevant imaging characteristics. In the clinical practice the proposed decision support system could help reduce the rate of misclassified lesions and consequently the number of unnecessary biopsies. (c) 2023 Elsevier B.V. All rights reserved
Louis-Philippe Dalembert, «vagabond jusqu’au bout de la fatigue»
No full textThe Haitian novelist and poet Louis-Philippe Dalembert (Port-au-Prince, 1962) has developed in his works of fiction the concept of vagabondage as a literary projection of his biographical wandering through multiple spaces. The aim of this essay is to study the presence of vagabondage and its distinctive features in those novels written by Dalembert that reflect the writer’s perpetual motion: Le Crayon du bon Dieu n’a pas de gomme (1996), L’Autre face de la mer (1998), L’Île du bout des rêves (2003), Les dieux voyagent la nuit (2006). The main characters are constantly moving, they are cosmopolitan wanderers who belong to many places at the same time, just like Dalembert himself. By analyzing the representation of movement in these fictions, we will show that the notion of vagabondage is depicted by the author as a positive and meaningful opportunity for the vagabond who travels across countries, languages and cultures
Una ridiscussione dei concetti di home e identity nell’Asia globalizzata: il caso di These Foolish Things (2004) e How to Get Filthy Rich in Rising Asia (2013)
No full textThe evolution of postcolonial studies in the last thirty years and the development of a transnational approach in literary studies (Jay 2010) have led to a renewed interest towards the subaltern voices, especially in relation to the phenomena of migration and diaspora and their global effects. In this light, the idea of 'home' is characterized by a sort of porosity and by a new geographical and emotional conceptualization which inevitably influences the personal and collective identities of migrant communities.
The aim of the paper is to analyze these topics from a cultural and literary standpoint through the examination of the two different kinds of migrant flows and postcolonial scenarios depicted in These Foolish Things (2004) by the English author Deborah Moggach, and How to Get Filthy Rich in Rising Asia (2013) by the Pakistan novelist Mohsin Hamid. In these novels, the chaotic Indian framework – the former margin of the British empire – is torn between its colonial past and the current effects of the permeability of its borders. It is, therefore, a perfect global context, wherein the experiences and the feelings of the modern Indian identities are reinterpreted by the two authors
Sulle tracce della fortuna dei Carmina di Ennodio tra Tardoantico e Medioevo
No full textKnowledge of the poems of Magnus Felix Ennodius in Late Antiquity and the Middle Ages is a field of research that has not yet been explored. The essay provides some food for thought on the possible fortune of the Late Antique author starting from Columbanus up to Radulfus Tortarius, identifying in the greatest medieval poets (Aldhelm, Paul the Deacon, Sedulius Scotus) expressions, verbal sequences and original clauses of the poet of Ticinum. The last section is dedicated to the epigraphic field and above all to the Fortleben of notable iuncturae minted by Ennodius in some inscriptions of the 8th-9th century
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