In this study, we proposed establishing an automatic computer-assisted surgical planning approach based on average population models....
We built the average population models from humerus datasets using the Advanced Normalization Toolkits (ANTs) and Shapeworks. Experiments include (1) evaluation of the average population models before...
The average population models predicted clinical locations on the new dataset with acceptable errors when compared to the ground truth determined by an expert. However, the templates built from ANTs p...
The average population model could be utilized to assist anatomical landmarks checking automatically and following surgical decisions for new patients who are not involved in the dataset used to gener...
Accurate representation of bone shape is important for subject-specific musculoskeletal models as it may influence modelling of joint kinematics, kinetics, and muscle dynamics. Statistical shape model...
We created three-dimensional models of 56 femurs, 29 pelves, 56 tibias, 56 fibulas, and 56 patellae through segmentation of magnetic resonance images taken from 29 typically developing children (15 fe...
Femurs, pelves, tibias, fibulas, and patellae reconstructed via SSM using full-input had RMSE between 0.89 ± 0.10 mm (patella) and 1.98 ± 0.38 mm (pelvis), Jaccard indices between 0.77 ± 0.03 (pelvis)...
The SSM of paediatric lower limb bones showed reconstruction accuracy consistent with previously developed SSM and outperformed adult-based SSM when used to reconstruct paediatric bones....
This study investigated morphological characteristics of the soleus muscle in cerebral palsy (CP) and typically developing (TD) cohorts using a statistical shape model and differentiated dominant feat...
Three-dimensional (3D) reconstruction of lower limbs is of great interest in surgical planning, computer assisted surgery, and for biomechanical applications. The use of 3D imaging modalities such as ...
Questioned document examination aims to assess if a document of interest has been forged. Spectroscopy-based methods are the gold standard for this type of evaluation. In the past 15 years, Matrix-Ass...
Statistical physics is widely used to study the nonlinear mechanical behaviors of rock. For the limitations of existing statistical damage models and Weibull distribution, a new statistical damage wit...
This systematic review aims to ascertain how accurately 3D models can be predicted from two-dimensional (2D) imaging utilising statistical shape modelling....
A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which ...
2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error)....
Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required...
Climate change is projected to threaten ecological communities through changes in temperature, rainfall, runoff patterns, and mediated changes in other environmental variables. Their combined effects ...
Since December 2019, the COVID-19 outbreak has touched every area of everyday life and caused immense destruction to the planet. More than 150 nations have been affected by the coronavirus outbreak. M...
Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates....
The study aimed to compare how different model metrics rank the importance of various covariates....
A cross-sectional cohort study was conducted retrospectively using the National Health and Nutrition Examination Survey (NHANES), which is publicly available....
This study employed univariate logistic models to filter out strong, independent covariates associated with sleep disorder outcome, which were then used in machine-learning models, of which, the most ...
The XGBoost model had the highest mean AUROC of 0.865 (SD = 0.010) with Accuracy of 0.762 (SD = 0.019), F1 of 0.875 (SD = 0.766), Sensitivity of 0.768 (SD = 0.023), Specificity of 0.782 (SD = 0.025), ...
The ranking of important variables associated with sleep disorder in this cohort from the machine learning models were not related to those from regression models....
