1,720,961 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
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
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
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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