Identifying fallers among ophthalmic patients using classification tree methodology

Global burden of fall and associated factors among individual with low vision: A systematic-review and meta-analysis

INTRODUCTION

Low vision has a significant global health problem that impacts the personal, economical, psychological, and social life of an individual. Each year around 684 000 individuals die from falls, 80% of these deaths occur are in low- and middle-income countries. The risk of falling significantly increases with visual impairment. This review aimed to determine the global pooled prevalence of fall and associated factors among individuals with low vision.

METHODS AND MATERIALS

Systematic search of published studies done on PubMed, EMBASE, MEDLINE, Cochrane, Scopus, Web of Science CINAHL and, Google Scholar. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to report the findings. Quality of studies was assessed using the modified Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using a random-effects method using the STATA™ Version 14 software.

RESULT

Thirty-five (35) studies from different regions involving 175,297 participants included in this meta-analysis. The overall pooled global prevalence fall among individual with low vision was17.7% (95% CI: 16.4-18.9) whereas the highest prevalence was 35.5%; (95% CI: 28.4-42.5) in Australia and the lowest was 19.7%; (95% CI: 7.6-31.8) seen in South America. Fear of falling (OR: 0.16(95%CI 0.09-0.30), and severity of visual impairment (OR: 0.27(95%CI (0.18-0.39) increases the odds of falling.

CONCLUSION

As one cause of accidental death, the prevalence of falls among individuals with low vision is high. Fear of falling and severity of falling increases the odds of falling. Different stakeholders should give due attention and plan effective strategies to reduce the fall among this population.

Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review

The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.

The Identification of Elderly People with High Fall Risk Using Machine Learning Algorithms

Falling is an important public health issue, and predicting the fall risk can reduce the incidence of injury events in the elderly. However, most of the existing studies may have additional human and financial costs for community workers and doctors. Therefore, it is socially important to identify elderly people who are at high fall risk through a reasonable and cost-effective method. We evaluated the potential of multifractal, machine learning algorithms to identify the elderly at high fall risk. We developed a 42-point calibration model of the human body and recorded the three-dimensional coordinate datasets. The stability of the motion trajectory is calculated by the multifractal algorithm and used as an input dimension to compare the performance of the six classifiers. The results showed that the instability of the faller group was significantly greater than that of the no-faller group in the male and female cohorts (p < 0.005), and the Gradient Boosting Decision Tree classifier showed the best performance. The findings could help elderly people at high fall risk to identify individualized risk factors and facilitate tailored fall interventions.

Using conditional inference forests to examine predictive ability for future falls and syncope in older adults: Results from The Irish Longitudinal Study on Ageing

BACKGROUND

The extent to which gait and mobility measures predict falls relative to other risk factors is unclear. This study examined predictive accuracy of over 70 baseline risk factors, including gait and mobility, for future falls and syncope using conditional inference forest models.

METHODS

Data from three waves of The Irish Longitudinal Study on Ageing (TILDA), a population-based study of community-dwelling adults aged ≥50 years were used (n=4,706). Outcome variables were recurrent falls, injurious falls, unexplained falls and syncope occurring over four year follow-up. Predictive accuracy was calculated using 5 fold cross-validation; as there was class imbalance, the algorithm was trained using undersampling of the larger class. Classification rate, area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (PRAUC) assessed predictive accuracy.

RESULTS

Highest overall accuracy was 69.7% for recurrent falls in 50-64 year olds. AUROC and PRAUC were ≤0.69 and ≤0.39 respectively for all outcomes indicating low predictive accuracy. History of falls, unsteadiness while walking, fear of falling, mobility, medications , mental health and cardiovascular health and function were the most important predictors for most outcomes.

CONCLUSIONS

Conditional inference forest models using over 70 risk factors resulted in low predictive accuracy for future recurrent, injurious and unexplained falls and syncope in community-dwelling adults. Gait and mobility impairments were important predictors of most outcomes but did not discriminate well between fallers and non-fallers. Results highlight the importance of multifactorial risk assessment and intervention and validate key modifiable risk factors for future falls and syncope.