Magnus Kjartan Gislason

Whiplash-Associated Disorders (WADs) often result from traffic accidents, leading to persistent symptoms, including neck pain, disability, dizziness, and central sensitization (CS). A key concern is cervical range of motion (cROM) impairment and sensorimotor dysfunction, which contribute to prolonged disability. This study assessed functional performance in individuals with subacute (>1, p < 0.001). Neck disability (p < 0.001) and pain intensity (p = 0.015) affected cROM within the WAD group. Interaction effects revealed greater amplitude accuracy (AA) impairments at greater difficulty levels (p = 0.043), while time on target (TOT) differences decreased (p < 0.001). Dizziness was associated with increased undershoot (p < 0.001), while pain negatively impacted both AA (p = 0.003) and TOT (p = 0.037). Position sense did not differentiate WAD patients from controls. Findings suggest task-dependent sensorimotor deficits, highlighting the need for multimodal assessment. Early CS screening may optimize rehabilitation and prevent chronic disability.

Aquatic resistance training has been proven to be beneficial to many people, in particular those struggling with degenerative joint diseases or recovering from other musculoskeletal issues as the reaction forces acting on the joints become lower, but without compromising the cardiovascular and neuromuscular benefit of the movement. Little has been written on the load produced by or measurements of the devices used in aquatic resistance training. Therefore, uncertainties exist regarding details of how much load can be applied onto the foot when performing the movements and how to quantify progression. In this study, an instrumented robotic arm was designed, built, and used to measure the load acting on the three different types of fins during a simulated flexion/extension movement of a knee. The angular velocities of the knee ranged from 25°/s to 150°/s, which represent the physiological range of in vivo movements. The results demonstrated that the load followed a second-order polynomial with the angular velocities. The load is therefore a function of the angular velocity, the surface area of the fins, and the location of the fins away from the joint center rotation. We modeled the progression of speeds at maximal voluntary movements based on previous studies. The maximum loads measured between 11 kg and 13 kg in extension and 6 kg and 9 kg in flexion at 150°/s rotational velocity.

The strong age dependency of many deleterious health outcomes likely reflects the cumulative effects from a variety of risk and protective factors that occur over one's life course. This notion has become increasingly explored in the etiology of chronic disease and associated comorbidities in aging. Our recent work has shown the robust classification of individuals at risk for cardiovascular pathophysiology using CT-based soft tissue radiodensity parameters obtained from nonlinear trimodal regression analysis (NTRA). Past and present lifestyle influences the incidence of comorbidities like hypertension (HTN), diabetes (DM) and cardiac diseases. 2,943 elderly subjects from the AGES-Reykjavik study were sorted into a three-level binary-tree structure defined by: 1) lifestyle factors (smoking and self-reported physical activity level), 2) comorbid HTN or DM, and 3) cardiac pathophysiology. NTRA parameters were extracted from mid-thigh CT cross-sections to quantify radiodensitometric changes in three tissue types: lean muscle, fat, and loose-connective tissue. Between-group differences were assessed at each binary-tree level, which were then used in tree-based machine learning (ML) models to classify subjects with DM or HTN. Classification scores for detecting HTN or DM based on lifestyle factors were excellent (AUCROC: 0.978 and 0.990, respectively). Finally, tissue importance analysis underlined the comparatively-high significance of connective tissue parameters in ML classification, while predictive models of DM onset from five-year longitudinal data gave a classification accuracy of 94.9%. Altogether, this work serves as an important milestone toward the construction of predictive tools for assessing the impact of lifestyle factors and healthy aging based on a single image.

There 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.

Total Hip Arthroplasty has been one of the most successful surgical procedure in terms of patient outcomes and satisfaction. However, due to increase in life expectancy and the related incidence of age-dependent bone diseases, a growing number of cases of intra-operative fractures lead to revision surgery with high rates of morbidity and mortality. Surgeons choose the type of the implant, either cemented or cementless prosthesis, on the basis of the age, the quality of the bone and the general medical conditions of the patients. Generally, no quantitative measures are available to assess the intra-operative fracture risk. Consequently, the decision-making process is mainly based on surgical operators’ expertise and qualitative information obtained from imaging. Motivated by this scenario, we here propose a mechanical-supported strategy to assist surgeons in their decisions, by giving intelligible maps of the risk fracture which take into account the interplay between the actual mechanical strength distribution inside the bone tissue and its response to the forces exerted by the implant. In the presented study, we produce charts and patient-specific synthetic “traffic-light” indicators of fracture risk, by making use of ad hoc analytical solutions to predict the stress levels in the bone by means of Computed Tomography-based mechanical and geometrical parameters of the patient. We felt that if implemented in a friendly software or proposed as an app, the strategy could constitute a practical tool to help the medical decision-making process, in particular with respect to the choice of adopting cemented or cementless implant.

The 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 indexand isometric leg strength using tree-based regression algorithms. Results obtained from thesemodels 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 parametersand comorbidities.