Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

BACKGROUND

Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Combining machine learning (ML) approaches is superior to traditional regression analysis for its ability to handle complex data regarding its characteristics of being high-dimensional, non-linear, and highly correlated. The study goal was to use ML algorithms to classify fall risks in community-dwelling older adults with the aid of an explainable artificial intelligence (XAI) approach to increase interpretability.

METHODS

A total of 215 participants were included for analysis. The input information included personal metrics and posturographic parameters obtained from a tracker-based posturography of four standing postures. Two classification criteria were used: with a previous history of falls and the timed-up-and-go (TUG) test. We used three meta-heuristic methods for feature selection to handle the large numbers of parameters and improve efficacy, and the SHapley Additive exPlanations (SHAP) method was used to display the weights of the selected features on the model.

RESULTS

The results showed that posturographic parameters could classify the participants with TUG scores higher or lower than 10 s but were less effective in classifying fall risk according to previous fall history. Feature selections improved the accuracy with the TUG as the classification label, and the Slime Mould Algorithm had the best performance (accuracy: 0.72 to 0.77, area under the curve: 0.80 to 0.90). In contrast, feature selection did not improve the model performance significantly with the previous fall history as a classification label. The SHAP values also helped to display the importance of different features in the model.

CONCLUSION

Posturographic parameters in standing can be used to classify fall risks with high accuracy based on the TUG scores in community-dwelling older adults. Using feature selection improves the model's performance. The results highlight the potential utility of ML algorithms and XAI to provide guidance for developing more robust and accurate fall classification models. Trial registration Not applicable.

Machine Learning Prediction Algorithm for In-Hospital Mortality following Body Contouring

BACKGROUND

Body contouring is a common procedure, but it is worth attention because of concern for a variety of complications, and even the potential for death. As a result, the purpose of this study was to determine the key predictors following body contouring and create models for the risk of mortality using diverse machine learning (ML) models.

METHODS

The National Inpatient Sample database from 2015 to 2017 was queried to identify patients undergoing body contouring. Candidate predictors, such as demographics, comorbidities, personal history, postoperative complications, and operative features, were included. The outcome was in-hospital mortality. Models were compared by area under the curve, accuracy, sensitivity, specificity, positive and negative predictive values, and decision curve analysis.

RESULTS

Overall, 8214 patients undergoing body contouring were identified, among whom 141 (1.72%) died in the hospital. Variable importance plot demonstrated that sepsis was the variable with greatest importance across all ML algorithms, followed by Elixhauser Comorbidity Index, cardiac arrest, and so forth. The naive Bayes model had a higher predictive performance (area under the curve, 0.898; 95% CI, 0.884 to 0.911) among these eight ML models. Similarly, in the decision curve analysis, the naive Bayes model also demonstrated a higher net benefit (ie, the correct classification of in-hospital deaths considering a tradeoff between false-negatives and false-positives) compared with the other seven models across a range of threshold probability values.

CONCLUSION

The ML models, as indicated by this study, can be used to predict in-hospital death for patients at risk who undergo body contouring.

Exploring the Nexus of lower extremity proprioception and postural stability in older adults with osteoporosis: a cross-sectional investigation

Background

Osteoporosis, characterized by reduced bone mass and micro-architectural deterioration, poses a significant public health concern due to increased fracture susceptibility. Beyond bone health, this cross-sectional study aimed to assess and compare lower extremity proprioception and postural stability in individuals with and without osteoporosis and to explore their correlation within the osteoporosis group.

Method

In this prospective cross-sectional study, 80 participants were divided into two groups: osteoporosis (n = 40) and control (n = 40). The demographic characteristics and clinical parameters of the participants were as follows: Age (years) - Osteoporosis group: 65.04 ± 4.33, Control group: 65.24 ± 4.63; Sex (%) - Osteoporosis group: Male 30%, Female 70%; Control group: Male 30%, Female 70%; Body mass index (kg/m2) - Osteoporosis group: 23.7 ± 3.2, Control group: 24.5 ± 4.6; T-score (Lumbar) - Osteoporosis group: -2.86 ± 1.23, Control group: 0.27 ± 0.58; T-score (hip) - Osteoporosis group: -2.28 ± 0.79, Control group: 0.68 ± 0.86. Joint Position Sense (JPS) at the hip, knee, and ankle was assessed using a digital inclinometer, and postural stability was measured using computerized force platforms.

Result

Osteoporosis participants exhibited higher errors in hip (5.63° vs. 2.36°), knee (4.86° vs. 1.98°), and ankle (4.46° vs. 2.02°) JPS compared to controls. Postural stability measures showed increased anterior-posterior sway (10.86 mm vs. 3.98 mm), medial-lateral sway (8.67 mm vs. 2.89 mm), and ellipse area (966.88 mm2 vs. 446.19 mm2) in osteoporosis participants. Furthermore, correlation analyses within the osteoporosis group unveiled significant positive associations between lower extremity proprioception and postural stability. Specifically, hip JPS exhibited a strong positive correlation with anterior-posterior sway (r = 0.493, p = 0.003), medial-lateral sway (r = 0.485, p = 0.003), and ellipse area (r = 0.496, p < 0.001). Knee JPS displayed a moderate positive correlation with anterior-posterior sway (r = 0.397, p = 0.012), medial-lateral sway (r = 0.337, p = 0.032), and ellipse area (r = 0.378, p < 0.001). Similarly, ankle JPS showed a moderate positive correlation with anterior-posterior sway (r = 0.373, p = 0.023), medial-lateral sway (r = 0.308, p = 0.045), and ellipse area (r = 0.368, p = 0.021).

Conclusion

These findings underscore the interplay between proprioceptive deficits, compromised postural stability, and osteoporosis, emphasizing the need for targeted interventions to improve fall prevention strategies and enhance the quality of life for individuals with osteoporosis.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors

BACKGROUND

Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk.

METHODS

Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods.

RESULTS

Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74.

CONCLUSION

These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.