Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™–aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

Unveiling healthcare data archiving: Exploring the role of artificial intelligence in medical image analysis

Gli archivi sanitari digitali possono essere considerati dei moderni database progettati per immagazzinare e gestire ingenti quantità di informazioni mediche, dalle cartelle cliniche dei pazienti, a studi clinici fino alle immagini mediche e a dati genomici. I dati strutturati e non strutturati che compongono gli archivi sanitari sono oggetto di scrupolose e rigorose procedure di validazione per garantire accuratezza, affidabilità e standardizzazione a fini clinici e di ricerca. Nel contesto di un settore sanitario in continua e rapida evoluzione, l’intelligenza artificiale (IA) si propone come una forza trasformativa, capace di riformare gli archivi sanitari digitali migliorando la gestione, l’analisi e il recupero di vasti set di dati clinici, al fine di ottenere decisioni cliniche più informate e ripetibili, interventi tempestivi e risultati migliorati per i pazienti. Tra i diversi dati archiviati, la gestione e l’analisi delle immagini mediche in archivi digitali presentano numerose sfide dovute all’eterogeneità dei dati, alla variabilità della qualità delle immagini, nonché alla mancanza di annotazioni. L’impiego di soluzioni basate sull’IA può aiutare a risolvere efficacemente queste problematiche, migliorando l’accuratezza dell’analisi delle immagini, standardizzando la qualità dei dati e facilitando la generazione di annotazioni dettagliate. Questa tesi ha lo scopo di utilizzare algoritmi di IA per l’analisi di immagini mediche depositate in archivi sanitari digitali. Il presente lavoro propone di indagare varie tecniche di imaging medico, ognuna delle quali è caratterizzata da uno specifico dominio di applicazione e presenta quindi un insieme unico di sfide, requisiti e potenziali esiti. In particolare, in questo lavoro di tesi sarà oggetto di approfondimento l’assistenza diagnostica degli algoritmi di IA per tre diverse tecniche di imaging, in specifici scenari clinici: i) Immagini endoscopiche ottenute durante esami di laringoscopia; ciò include un’esplorazione approfondita di tecniche come la detection di keypoints per la stima della motilità delle corde vocali e la segmentazione di tumori del tratto aerodigestivo superiore; ii) Immagini di risonanza magnetica per la segmentazione dei dischi intervertebrali, per la diagnosi e il trattamento di malattie spinali, così come per lo svolgimento di interventi chirurgici guidati da immagini; iii) Immagini ecografiche in ambito reumatologico, per la valutazione della sindrome del tunnel carpale attraverso la segmentazione del nervo mediano. Le metodologie esposte in questo lavoro evidenziano l’efficacia degli algoritmi di IA nell’analizzare immagini mediche archiviate. I progressi metodologici ottenuti sottolineano il notevole potenziale dell’IA nel rivelare informazioni implicitamente presenti negli archivi sanitari digitali

Novel Approaches to the Representation and Analysis of 3D Segmented Anatomical Districts

Nowadays, image processing and 3D shape analysis are an integral part of clinical practice and have the potentiality to support clinicians with advanced analysis and visualization techniques. Both approaches provide visual and quantitative information to medical practitioners, even if from different points of view. Indeed, shape analysis is aimed at studying the morphology of anatomical structures, while image processing is focused more on the tissue or functional information provided by the pixels/voxels intensities levels. Despite the progress obtained by research in both fields, a junction between these two complementary worlds is missing. When working with 3D models analyzing shape features, the information of the volume surrounding the structure is lost, since a segmentation process is needed to obtain the 3D shape model; however, the 3D nature of the anatomical structure is represented explicitly. With volume images, instead, the tissue information related to the imaged volume is the core of the analysis, while the shape and morphology of the structure are just implicitly represented, thus not clear enough. The aim of this Thesis work is the integration of these two approaches in order to increase the amount of information available for physicians, allowing a more accurate analysis of each patient. An augmented visualization tool able to provide information on both the anatomical structure shape and the surrounding volume through a hybrid representation, could reduce the gap between the two approaches and provide a more complete anatomical rendering of the subject. To this end, given a segmented anatomical district, we propose a novel mapping of volumetric data onto the segmented surface. The grey-levels of the image voxels are mapped through a volume-surface correspondence map, which defines a grey-level texture on the segmented surface. The resulting texture mapping is coherent to the local morphology of the segmented anatomical structure and provides an enhanced visual representation of the anatomical district. The integration of volume-based and surface-based information in a unique 3D representation also supports the identification and characterization of morphological landmarks and pathology evaluations. The main research contributions of the Ph.D. activities and Thesis are: \u2022 the development of a novel integration algorithm that combines surface-based (segmented 3D anatomical structure meshes) and volume-based (MRI volumes) information. The integration supports different criteria for the grey-levels mapping onto the segmented surface; \u2022 the development of methodological approaches for using the grey-levels mapping together with morphological analysis. The final goal is to solve problems in real clinical tasks, such as the identification of (patient-specific) ligament insertion sites on bones from segmented MR images, the characterization of the local morphology of bones/tissues, the early diagnosis, classification, and monitoring of muscle-skeletal pathologies; \u2022 the analysis of segmentation procedures, with a focus on the tissue classification process, in order to reduce operator dependency and to overcome the absence of a real gold standard for the evaluation of automatic segmentations; \u2022 the evaluation and comparison of (unsupervised) segmentation methods, finalized to define a novel segmentation method for low-field MR images, and for the local correction/improvement of a given segmentation. The proposed method is simple but effectively integrates information derived from medical image analysis and 3D shape analysis. Moreover, the algorithm is general enough to be applied to different anatomical districts independently of the segmentation method, imaging techniques (such as CT), or image resolution. The volume information can be integrated easily in different shape analysis applications, taking into consideration not only the morphology of the input shape but also the real context in which it is inserted, to solve clinical tasks. The results obtained by this combined analysis have been evaluated through statistical analysis

Ultrasound Detection of Subquadricipital Recess Distension

Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy. Ultrasound imaging has recently emerged as an effective tool to diagnose joint recess distension caused by joint bleeding. However, no computer-aided diagnosis tool exists to support the practitioner in the diagnosis process. This paper addresses the problem of automatically detecting the recess and assessing whether it is distended in knee ultrasound images collected in patients with hemophilia. After framing the problem, we propose two different approaches: the first one adopts a one-stage object detection algorithm, while the second one is a multi-task approach with a classification and a detection branch. The experimental evaluation, conducted with $483$ annotated images, shows that the solution based on object detection alone has a balanced accuracy score of $0.74$ with a mean IoU value of $0.66$, while the multi-task approach has a higher balanced accuracy value ($0.78$) at the cost of a slightly lower mean IoU value

Usability testing of JIActiv, a social media-based program promoting engagement in physical activity among young people living with juvenile idiopathic arthritis

L'arthrite juvénile idiopathique (AJI) est une maladie chronique infantile d'origine inconnue caractérisée par de la douleur chronique, des enflures articulaires et de la fatigue. Malgré les effets positifs de l'activité physique (AP) sur les symptômes reliés à l’arthrite et la santé générale, les jeunes atteints d'AJI adoptent souvent un mode de vie sédentaire. Par conséquent, ils sont plus à risque de développer d’autres maladies chroniques telles les maladies cardio-vasculaires. Cela nécessite l’accès à un programme efficace pour inciter ces personnes à faire de l'AP. En réponse à ceci, notre équipe a développé le programme ActiJI livré sur Instagram promouvant l’engagement à l’AP auprès des jeunes personnes vivant avec l’AJI. La présente étude évalue l’utilisabilité d’ActiJI en ciblant la satisfaction et la performance d’utilisation parmi les jeunes atteints d'AJI. Une étude qualitative descriptive a été utilisée. Des adolescents (âgés de 13 à 17 ans) et des jeunes adultes (âgés de 18 à 25 ans) atteints d'AJI ont été recrutés via des associations patients, des centres hospitaliers et de réadaptation. Au total, 28 participants (âge moyen = 18,69 ans) ont complété des entretiens semi-dirigés sur deux cycles itératifs via Zoom (Enterprise Version 5.0.2). Les verbatims ont été transcrits, puis triés, organisés et codés avec MAXQDA 11 selon les recommandations de Huberman et al.. Le processus de codage s'est appuyé sur six thèmes ancrés dans les principes théoriques de l’utilisabilité et définis par les équipes de recherche, ceux-ci comprenaient la confidentialité et la sécurité, l'esthétique du design, les fonctionnalités, l'organisation, la connexion sociale et le contenu de la page. Nos résultats démontrent que le programme ActiJI est vu comme étant sécuritaire, convivial, et est apprécié pour ses activités de groupe et les interactions entre pairs. En particulier, le soutien éventuel offert par les professionnels de santé et les pairs motiveraient les jeunes atteints d'AJI à s'engager davantage dans l'AP. Les participants rapportent que le programme ActiJI est facilement utilisable, et que la page Instagram peut être naviguée efficacement. Les recommandations des participants ont été intégrées au programme ActiJI. Une prochaine étude visera à évaluer la faisabilité d’ActiJI.Juvenile idiopathic arthritis (JIA) is the most common childhood chronic rheumatic condition of unknown origin and is characterized by chronic pain, joint inflammation and fatigue. Despite the benefits of physical activity (PA) in mitigating arthritis symptoms and for general health, young people with JIA have a greater tendency to adopt a sedentary lifestyle rather than engage in PA. Consequently, these young people are at greater risk for other chronic health conditions such as cardiovascular disease. Access to innovative and attractive means of promoting PA among these young people is sorely needed. In response to this need, our team developed JIActiv an Instagrambased program promoting physical activity among young people living with JIA. The current study aimed to assess the usability of the JIActiv program in terms of user performance and the level of satisfaction among adolescents and young adults living with JIA. We used a descriptive qualitative study design. Adolescents (ages 13 to 17 years) and young adults (aged 18 to 25 years) living with JIA were recruited from rheumatology clinics in rehabilitation and hospital centers, as well as through patient organizations. A total of 28 young people (mean age = 18.69, SD=± 2.28 years) completed semi-structured interviews over two iterative cycles using Zoom (Enterprise Version 5.0.2). The audio recordings of the interviews were transcribed word by word, then sorted, organized, and coded using MAXQDA 11 software following recommendations by Huberman et al.. The coding process was based on six themes anchored within the theoretical principals of usability testing and were specified by the research teams, which included privacy and safety, design aesthetics, functionalities, organization, social connection, and content of the page . Our findings have shown that the JIActiv program is viewed as secure and user-friendly. Participants appreciated the group activities and peer interactions. Notably, the potential support offered by healthcare professionals and peers may motivate those living with JIA to engage more in PA. Study participants reported that the JIActiv program was easy to use, and they navigated the Instagram page effectively. Participant recommendations were integrated within the JIActiv program. A subsequent study will assess the feasibility of JIActiv

Expression of chemokines CXCL4 and CXCL7 by synovial macrophages defines an early stage of rheumatoid athritis

BACKGROUND AND OBJECTIVES: For our understanding of the pathogenesis of rheumatoid arthritis (RA), it is important to elucidate the mechanisms underlying early stages of synovitis. Here, synovial cytokine production was investigated in patients with very early arthritis. METHODS: Synovial biopsies were obtained from patients with at least one clinically swollen joint within 12 weeks of symptom onset. At an 18-month follow-up visit, patients who went on to develop RA, or whose arthritis spontaneously resolved, were identified. Biopsies were also obtained from patients with RA with longer symptom duration (>12 weeks) and individuals with no clinically apparent inflammation. Synovial mRNA expression of 117 cytokines was quantified using PCR techniques and analysed using standard and novel methods of data analysis. Synovial tissue sections were stained for CXCL4, CXCL7, CD41, CD68 and von Willebrand factor. RESULTS: A machine learning approach identified expression of mRNA for CXCL4 and CXCL7 as potentially important in the classification of early RA versus resolving arthritis. mRNA levels for these chemokines were significantly elevated in patients with early RA compared with uninflamed controls. Significantly increased CXCL4 and CXCL7 protein expression was observed in patients with early RA compared with those with resolving arthritis or longer established disease. CXCL4 and CXCL7 co-localised with blood vessels, platelets and CD68(+) macrophages. Extravascular CXCL7 expression was significantly higher in patients with very early RA compared with longer duration RA or resolving arthritis CONCLUSIONS: Taken together, these observations suggest a transient increase in synovial CXCL4 and CXCL7 levels in early RA