Improving prosthetic selection and predicting BMD from biometric measurements in patients receiving total hip arthroplasty

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

Machine Learning Algorithms Predict Body Mass Index Using Nonlinear Trimodal Regression Analysis from Computed Tomography Scans

In this study Machine Learning supervised regression and classification algorithms are used to predict Body Mass Index (BMI), starting from Computed Tomography scans (CT). From each patient CTs, 11 parameters describing muscle, connective tissue and fat, are extracted creating a patient specific soft tissue profile called Nonlinear Trimodal Regression Analysis (NTRA). Regression and classification are applied in order to predict and classify BMI using Tree-Based algorithms. A proper Train-Test division of the dataset is applied using k_fold Cross-Validation. Various combinations of features are employed with k_fold division in order to obtain the best coefficient of determination (R2) as evaluator of the quality of regression’s prediction. Afterward, BMI is divided into 3 and 5 classes and the same methodology is used to classify it. For this analysis, the accuracy parameter is calculated to evaluate the quality of the results. The max R2 is 0,83 and it is obtained using the 11 NTRA parameters as regressors, k_fold = 16, and the Gradient-Boosting Algorithm. The amplitude of the connective and fat tissue always covers more than 50% of all the feature importance. The best accuracy was 0,80 for 3 classes and 0,74 for 5 classes. The results prove that the 11 NTRA parameters can have a very significant predictive value and the same methodology can be applied in future works to predict other physiological parameters and comorbidities

Testing soft tissue radiodensity parameters interplay with age and self-reported physical activity

Aging well is directly associated to a healthy lifestyle. The focus of this paper is to relate individual wellness with medical image features. Non-linear trimodal regression analysis (NTRA) is a novel method that models the radiodensitometric distributions of x-ray computed tomography (CT) cross-sections. It generates 11 patient-specific parameters that describe the quality and quantity of muscle, fat, and connective tissues. In this research, the relationship of these 11 NTRA parameters with age, physical activity, and lifestyle is investigated in the 3,157 elderly volunteers AGES-I dataset. First, univariate statistical analyses were performed, and subjects were grouped by age and self-reported past (youth–midlife) and present (within 12 months of the survey) physical activity to ascertain which parameters were the most influential. Then, machine learning (ML) analyses were conducted to classify patients using NTRA parameters as input features for three ML algorithms. ML is also used to classify a Lifestyle index using the age groups. This classification analysis yielded robust results with the lifestyle index underlying the relevant differences of the soft tissues between age groups, especially in fat and connective tissue. Univariate statistical models suggested that NTRA parameters may be susceptible to age and differences between past and present physical activity levels. Moreover, for both age and physical activity, lean muscle parameters expressed more significant variation than fat and connective tissues

A Regression Approach to Assess Bone Mineral Density of Patients undergoing Total Hip Arthroplasty through Gait Analysis

Total Hip Arthroplasty (THA) is the gold standard for hip replacement surgery. It can be performed with two different kinds of prostheses: cemented and uncemented. The surgeons have always decided on the type of prosthesis based on the age, sex of the patient and bone stock on x rays. In this paper 42 subjects underwent THA and performed both gait analysis and bone mineral density (BMD) evaluation through CT scans; spatial and temporal gait parameters were used to predict BMD of the distal and proximal parts of the femur before and one year after surgery using machine learning regression analysis. A simple linear regression (LR) and k-nearest neighbor (KNN) were implemented coding with Python Scikit-Learn libraries and some evaluation metrics were computed: the coefficient of determination (R2), mean absolute error, mean squared error and root mean squared error. Both the algorithms had a R2 greater than 75% in predicting both proximal and distal; particularly, LR obtained the highest score of 88.4% in predicting the BMD before the THA and of 81.3% after the THA. All the R2 of KNN ranged from 75% and 77%. All the calculated errors were always below 0.001. In conclusion, this research shows the feasibility of gait parameters for assessing the follow-up after 52 weeks of patients undergoing THA by predicting the BMD. Moreover, the results give insights about the relationship between the patterns of gait and BMD

Healthy Aging within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension

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

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

The 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

External Validation of the Arterial-Based Complexity Score and First Head-to-Head Comparison With the R.E.N.A.L. and PADUA Scores and C-index

The aim of the present study was to prove the arterial-based complexity (ABC) score validity by comparing it with the R.E.N.A.L. (radius, exophytic/endophytic tumor properties, nearness of tumor to deepest portion of collecting system or sinus, anterior/posterior descriptor, location relative to the polar line), PADUA (preoperative aspects and dimension for anatomic classification of renal tumors), and C-index scores. We performed a retrospective analysis of pre- and postoperative data from 234 patients who had undergone open and robot-assisted partial nephrectomy. An external urologist who was unaware of the outcomes reviewed all computed tomography scans to assign the nephrometry scores and determine tumor complexity. We found no statistically significant superiority for the ABC system. Introduction: We performed an external validation of the arterial-based complexity (ABC) score using a head-to-head comparison with the R.E.N.A.L. (radius, exophytic/endophytic tumor properties, nearness of tumor to deepest portion of collecting system or sinus, anterior/posterior descriptor, location relative to the polar line), PADUA (preoperative aspects and dimension for anatomic classification of renal tumors), and C-index scores for the prediction of surgical outcomes after partial nephrectomy. Materials and Methods: The data from a series of consecutive open or robot-assisted partial nephrectomies performed from January 2014 to July 2016 by 4 expert surgeons at a tertiary academic institution were reviewed. After dedicated training, 1 urologist not involved in the surgical procedures evaluated the cross-sectional imaging studies and assigned the nephrometry score using the 4 nephrometry scoring systems. The predictive performance of the ABC and other scoring systems was tested in univariate and multivariable fashion. Results: Overall, 234 patients were recruited (148 men and 86 women; age, 63 ± 10.9 years). The scores were all related to the estimated blood loss, use of hilar clamping, ischemia time, operative time, length of stay, and MIC (margin status, ischemia time, complications) score. They were not related to the occurrence of postoperative complications or, for the C-index and ABC score, the length of stay. In a head-to-head comparison, the ABC was not inferior only to the C-index relative to the occurrence of complications and MIC score, with borderline statistical significance. On multivariate analysis, the ABC score provided significant improvement only for the prediction of the operative and ischemia times. However, its performance was inferior to that of the other scoring systems. In addition, only the PADUA score improved the prediction of artery clamping and MIC score, and only the R.E.N.A.L. score showed an advantage for the prediction of the estimated blood loss. Conclusion: The predictive ability of ABC was inferior to that of well-established existing nephrometry scoring systems, such as the PADUA and R.E.N.A.L. scores

Predicting body mass index and isometric leg strength using soft tissue distributions from computed tomography scans

This paper describes the interconnections and predictive value between Body Mass Index (BMI), Isometric Leg Strength (ISO) and soft tissue distribution from mid-thigh Computed Tomography (CT) scans using Machine Learning (ML) regression and classification algorithms. A novel methodology for soft tissue patient specific CT profile called Nonlinear Trimodal Regression Analysis (NTRA) was developed using radiodensitomentric distribution from a CT scan. This method defines 11 parameters used as input features for Tree-Based ML algorithms in order to apply regression and classification on BMI and ISO. K_fold Cross-Validation with k = 10 is applied to obtain several models to choose the best one using the higher coefficient of determination (R2) as an evaluator of the quality of regression prediction. Following this, BMI and ISO are divided into 3 and 5 classes and the same methodology is used to classify them. For this analysis, an accuracy parameter is calculated to evaluate the quality of the results. The max R2 is 88.9 for the BMI and it is obtained using the Gradient-Boosting Algorithm. The best accuracy was 76.1 for 3 classes and 73.1 for 5 classes. The best results obtained for ISO are R2 = 66.5 and an accuracy of 65.5 for the 3 classes classification. Furthermore, the connective tissue assumes high importance in the prediction process. In this methodological study the feasibility of a ML approach was tested with good results, in order to show a novel approach to study the correlation between physiology parameters and imaging