Advancing clinical evaluation and diagnostics with artificial intelligence technologies

Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadThe nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT images distributions was developed for the quantitative characterization of soft tissue changes according to the lower extremity function of elderly subjects. In this regard, the NTRA method defines 11 subject-specific soft tissue parameters and has illustrated high sensitivity to changes in skeletal muscle form and function. The present work further explores the use of these 11 NTRA parameters in the construction of a machine learning (ML) system to predict body mass index and isometric leg strength using tree-based regression algorithms. Results obtained from these models demonstrate that when using an ML approach, these soft tissue features have a significant predictive value for these physiological parameters. These results further support the use of NTRA-based ML predictive assessment and support the future investigation of other physiological parameters and comorbidities. Keywords: Computed Tomography; Machine learning; body mass index; isometric leg strength; soft tissue

Geographical differences in the perspective of osteoarthritis care management: A cross-sectional study in Italy, Sweden and Russia

Background: This study aimed to explore the awareness, experiences, and beliefs of individuals with osteoarthritis (OA) regarding their healthcare management, along with assessing their overall satisfaction levels. Methods: A cross-sectional online survey was conducted in Italy, Sweden, and Russia, rigorously developed based on OA international guidelines in collaboration with healthcare professionals and individuals with OA. Participants over 40 years of age with self-reported hip and/or knee OA were eligible. The analytical framework included descriptive analysis (assessment of awareness levels for 'recommended', 'optional', and 'not recommended' treatments), analysis of suggested treatments and taken treatments, exploration of beliefs, barriers and satisfaction analysis (0-100 scale). Results: A total of 401 participants (mean age: 59.7, 78.3% female, 28% Italian, 49% Swedish, 23% Russian) contributed to the study. In Sweden, 57%-72% accurately identified recommended treatments, while in Russia, the range was 34%-91%, and in Italy, it was 35%-73%. The predominant suggested and taken treatments were oral anti-inflammatory drugs in Italy (87/81%) and Russia (97/97%) and specific exercise in Sweden (84/79%). Notably, only Sweden reached a consensus on the effectiveness of exercise for everyone, while Russia and Italy insisted on radiographic findings as a prerequisite for exercise. Mean satisfaction levels were 59.7 (Italy), 47.4 (Sweden), and 35.2 (Russia). Conclusions: This study uncovered variations in awareness, treatment preferences, and beliefs among the three countries, underscoring the necessity for tailored education on OA management that accounts for regional differences across Europe

Treatment utilisation and satisfaction with management in individuals with osteoarthritis and metabolic multimorbidity: A cross‐sectional multi‐country study

Purpose: To compare treatment utilisation for osteoarthritis (OA) and satisfaction with OA management between individuals with and without comorbid metabolic conditions (e.g., diabetes, obesity, dyslipidaemia, hypertension). Methods: Secondary analysis of a cross‐sectional international survey study (Italy, Russia, Sweden) on people ≥ 40 years old with knee/hip OA. Metabolic comorbidity was self‐reported. We used direct standardisation with prevalence ratios and mixed‐effect models to estimate the associations between comorbidity with treatment utilisation and satisfaction (score 0–100). Results: We analysed 401 individuals (48% Sweden, 28% Italy, 24% Russia; 53% with ≥ 1 metabolic condition). Those with and without comorbid metabolic conditions showed similar prevalence for first‐line interventions (exercise, education, and weight management). Metabolically unhealthy individuals showed higher use of opioids (prevalence ratio [95% CI] 1.9 [1.3–2.4]), antidepressants (1.8 [1.1–2.5]), corticosteroid injections (1.4 [1.0−1.8]), and homoeopathic products (2.1 [1.2–3.0]). Satisfaction with care (adjusted difference: −3.9 [95% CI: −8.5 to 2.4]) and information received about treatments (−4.0 [−9.7 to 1.7]) were similar. Conclusions: While first‐line OA interventions were similarly used, those with metabolic conditions relied more on second‐line and non‐recommended treatments, showing comparable satisfaction. More effort is needed to increase the adoption of lifestyle‐focused treatments in OA and to minimise the use of less recommended options among individuals with metabolic comorbidities

Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadThe nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals

Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes : BioVRSea paradigm

Funding Information: This work has been funded by Landspitali Research Fund (Grant number: 935836; Landspítali Háskólasjúkrahús). Publisher Copyright: Copyright © 2022 Stehle, Aubonnet, Hassan, Recenti, Jacob, Petersen and Gargiulo.Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task. Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses. Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML). Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.Peer reviewe

Development of soft tissue asymmetry indicators to characterize aging and functional mobility

Publisher Copyright: Copyright © 2023 Ricciardi, Ponsiglione, Recenti, Amato, Gislason, Chang and Gargiulo.Introduction: The aging population poses significant challenges to healthcare systems globally, necessitating a comprehensive understanding of age-related changes affecting physical function. Age-related functional decline highlights the urgency of understanding how tissue composition changes impact mobility, independence, and quality of life in older adults. Previous research has emphasized the influence of muscle quality, but the role of tissue composition asymmetry across various tissue types remains understudied. This work develops asymmetry indicators based on muscle, connective and fat tissue extracted from cross-sectional CT scans, and shows their interplay with BMI and lower extremity function among community-dwelling older adults. Methods: We used data from 3157 older adults from 71 to 98 years of age (mean: 80.06). Tissue composition asymmetry was defined by the differences between the right and left sides using CT scans and the non-Linear Trimodal Regression Analysis (NTRA) parameters. Functional mobility was measured through a 6-meter gait (Normal-GAIT and Fast-GAIT) and the Timed Up and Go (TUG) performance test. Statistical analysis included paired t-tests, polynomial fitting curves, and regression analysis to uncover relationships between tissue asymmetry, age, and functional mobility. Results: Findings revealed an increase in tissue composition asymmetry with age. Notably, muscle and connective tissue width asymmetry showed significant variation across age groups. BMI classifications and gait tasks also influenced tissue asymmetry. The Fast-GAIT task demonstrated a substantial separation in tissue asymmetry between normal and slow groups, whereas the Normal-GAIT and the TUG task did not exhibit such distinction. Muscle quality, as reflected by asymmetry indicators, appears crucial in understanding age-related changes in muscle function, while fat and connective tissue play roles in body composition and mobility. Discussion: Our study emphasizes the importance of tissue asymmetry indicators in understanding how muscle function changes with age in older individuals, demonstrating their role as risk factor and their potential employment in clinical assessment. We also identified the influence of fat and connective tissue on body composition and functional mobility. Incorporating the NTRA technology into clinical evaluations could enable personalized interventions for older adults, promoting healthier aging and maintaining physical function.Peer reviewe

Association between metabolic conditions, physical activity and self-efficacy before and after a first-line exercise and education intervention for osteoarthritis: a longitudinal register study using the SOAD cohort.

ObjectivesTo investigate the association of diabetes, hypertension and overweight/obesity with physical activity (PA), self-efficacy for pain and self-efficacy for other symptoms before and after a 6- week exercise and education intervention for knee and hip osteoarthritis (OA), and to assess outcome disparities based on metabolic health.MethodsRegister-based cohort study using the Swedish Osteoarthritis and Diabetes cohort. We used Body Mass Index, medical records and medication dispensation to define overweight/obesity, hypertension and diabetes at baseline (exposures). PA was self-reported (weekly minutes), and self-efficacy was measured using the 'Arthritis Self-Efficacy Scale' (ASES) (score 10-100) (outcomes). We used linear mixed-effect models to estimate associations between exposures and outcomes, adjusted for confounders.ResultsWe included 80 893 individuals with knee or hip OA. Those with metabolic conditions consistently showed lower PA and self-efficacy, with baseline disparities persisting after the intervention, particularly when all three conditions coexisted (PA difference: baseline 107 min [95% CI: 97; 118], 3-month 97 [86; 108], 12-month 109 [95; 123]; ASES-pain difference: baseline 5.6 [3.9; 7.3], 3-month 5.9 [4.1; 7.7], 12-month 8.2 [6.1; 10.4]; ASES-other symptoms difference: baseline 6.1 [4.6; 7.7], 3-month 6.4 [4.8; 8.0], 12-month 8.2 [6.3; 10.1]).ConclusionsMetabolic conditions were associated with lower PA and self-efficacy, with differences increasing with the number of co-existing conditions. The baseline disparities associated with metabolic conditions persisted after the intervention, with both groups showing improvement at 3 months but reverting to baseline by 12 months. This suggests that current guideline-based interventions for OA may not reduce long-term disparities related to metabolic conditions

Toward New Assessment of Knee Cartilage Degeneration

Funding Information: The authors would like to thank the project RESTORE for their contribution to this study, Marco Ghiselli and Kristján Örn Jóhannesson from the National University Hospital of Iceland for the medical image acquisition, Vicenzo Cangiano for his help in medical image segmentation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is part of the European project RESTORE ( https://restoreproject.eu/ ), funded by the European Union’s Horizon 2020 research and innovation program (grant agreement ID: 814558). This work has also been funded by Landspitalin Science fund (grant number: 960221). Publisher Copyright: © The Author(s) 2022. Publisher Copyright: © The Author(s) 2022.Objective: Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. Design: We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. Results: Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. Conclusion: The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.Peer reviewe

Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadThere are two surgical approaches to performing total hip arthroplasty (THA): a cemented or uncemented type of prosthesis. The choice is usually based on the experience of the orthopaedic surgeon and on parameters such as the age and gender of the patient. Using machine learning (ML) techniques on quantitative biomechanical and bone quality data extracted from computed tomography, electromyography and gait analysis, the aim of this paper was, firstly, to help clinicians use patient-specific biomarkers from diagnostic exams in the prosthetic decision-making process. The second aim was to evaluate patient long-term outcomes by predicting the bone mineral density (BMD) of the proximal and distal parts of the femur using advanced image processing analysis techniques and ML. The ML analyses were performed on diagnostic patient data extracted from a national database of 51 THA patients using the Knime analytics platform. The classification analysis achieved 93% accuracy in choosing the type of prosthesis; the regression analysis on the BMD data showed a coefficient of determination of about 0.6. The start and stop of the electromyographic signals were identified as the best predictors. This study shows a patient-specific approach could be helpful in the decision-making process and provide clinicians with information regarding the follow up of patients. Keywords: clinical decision making; database analyses; electromyography; machine learning; total hip arthroplasty.University of Reykjavik Icelandic National Hospital Rannis (Rannis Icelandic Research Fund (Rannsoknasjodur)) A&C M-C Foundation of Translational Myology, Padova, Ital