MyMedR

Displaying all 4 publications

Abstract:

Sort:

Shoulder and Neck Balance in Adolescent Idiopathic Scoliosis: Which Radiographic Indices are Reliable and Practical?

Vo Q, Nguyen H, Nguyen HT, Pham BN, Truong TK

Malays Orthop J, 2024 Mar;18(1):51-59.
PMID: 38638659 DOI: 10.5704/MOJ.2403.007

INTRODUCTION: Deformities of the spine and thorax in adolescent idiopathic scoliosis affect appearance. They are a cause of inferiority, affecting psychological well-being and the social life of the patients. To contribute to curve evaluation, planning in curve correction, and improving the post-operative aesthetics, many studies on the correlation between appearance and radiography in the assessment of shoulder and neck balance have been reported recently. In general, these studies did not clarify which indices are required to evaluate shoulder and neck balance. This study aimed to learn about indices to assess shoulder and neck balance in adolescent idiopathic scoliosis in correlation between clinical appearance and radiography.
MATERIALS AND METHODS: This observational study recruited 50 patients with adolescent idiopathic scoliosis who were 12 to 18 years of age with Cobb angle >10°. Based on Pearson correlation coefficient, radiographic parameters such as coracoid height difference (CHD), clavicle rib intersection distance (CRID), clavicle angle (CA), clavicle chest cage angle difference (CCAD), and T1 tilt angle were evaluated in correlation with clinical shoulder and neck balance by difference of inner shoulder height (SHi), difference of outer shoulder height (SHo), and neck tilt angle.
RESULTS: SHi was moderately correlated with T1 tilt angle (r [hereafter] = 0.45), CA (0.47), and CHD (0.57), high-moderately correlated with CRID (0.64), very-highly correlated with CCAD (0.84). SHo was moderately correlated with T1 tilt angle (0.43), highly correlated with CHD (0.60), CA (0.63), and CRID (0.72), and very-highly correlated with CCAD (0.89). T1 tilt angle was high-moderately correlated with neck tilt angle (0.76). The correlation coefficients between clinical and radiographic shoulder and neck balance according to sex, BMI, type of main curve, severity of main curve did not change significantly.
CONCLUSION: There was a very high correlation between SHo (shoulder tilt) and CCAD (0.89); the correlation between SHo and CRID was high-moderate (0.72), but CRID is easier than CCAD to evaluate on radiographs. On the other hand, T1 tilt angle, which is the easiest radiographic parameter to evaluate, had a high-moderate correlation with neck tilt angle (0.76) but a moderate correlation with SHo (0.43).
Fulltext Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

Shirzadi A, Soliamani K, Habibnejhad M, Kavian A, Chapi K, Shahabi H, et al.

Sensors (Basel), 2018 Nov 05;18(11).
PMID: 30400627 DOI: 10.3390/s18113777

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
Multilingualism and developmental language disorder in Southeast Asian speech-language pathology practice: An international survey

Smith G, Verdon SE, Chu SY, Razak RA, Chow D, Rusli YA, et al.

Int J Speech Lang Pathol, 2025 Jan 10.
PMID: 39789966 DOI: 10.1080/17549507.2024.2443052

PURPOSE: This study aims to explore the current practices and challenges faced by speech-language pathologists in three Southeast Asian countries (Malaysia, Indonesia, and Vietnam) in assessing and treating multilingual children with developmental language disorder.
METHOD: A survey was designed and administered to 110 speech-language pathologists across Malaysia, Indonesia, and Vietnam. The survey contained 60 questions on current practices and knowledge of existing resources for assessing and treating multilingual children with developmental language disorder. Data were analysed to identify relationships between practices and demographic variables including country of origin, years of service, and speech-language pathologists' multilingual status.
RESULT: Current practices reveal little knowledge and/or use of standardised tests for developmental language disorder across countries, but relatively high self-perceived competence when working with multilingual clients for Indonesia and Malaysia. However, several challenges were perceived across the board in practice with multilingual children, including socioeconomic challenges (i.e. costs involved for families and social status), insufficient training on the relevant topics, and limited access to appropriate tools and resources in their current practice.
CONCLUSION: Findings suggest the need for training and appropriate assessment tools to ensure the adoption of evidence-based service delivery for multilingual caseloads, minimising misclassification of developmental language disorder and boosting confidence levels in speech-language pathologists in Southeast Asia.
Fulltext Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, et al.

Int J Environ Res Public Health, 2020 04 16;17(8).
PMID: 32316191 DOI: 10.3390/ijerph17082749

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.