scholarly journals Classification and Morphometric Features of Pterion in Thai Population with Potential Sex Prediction

Medicina ◽  
2021 ◽  
Vol 57 (11) ◽  
pp. 1282
Author(s):  
Nongnut Uabundit ◽  
Arada Chaiyamoon ◽  
Sitthichai Iamsaard ◽  
Laphatrada Yurasakpong ◽  
Chanin Nantasenamat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Age Estimation ◽  
Brain Lesion ◽  
Mastoid Process ◽  
Morphometric Measurements ◽  
Thai Population ◽  
Anatomical Studies ◽  
Anterior Aspect ◽  
Morphometric Features ◽  
Males And Females

Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.

Observation on the breeding biology of Polypedates teraiensis (Dubois, 1987) (Amphibia: Rhacophoridae)

2017 ◽  
Vol 17 (1) ◽  
pp. 25-32
Author(s):  
Jacinta Lalchhanhimi ◽  
Lalremsanga H.T.
Keyword(s):  
Sexual Dimorphism ◽  
Breeding Season ◽  
Sound Production ◽  
Reproductive Function ◽  
Breeding Biology ◽  
Tree Frog ◽  
University Campus ◽  
Morphometric Measurements ◽  
Advertisement Calls ◽  
Males And Females

The breeding biology of tree frog, Polypedates teraiensis was studied during the breeding season at Mizoram University Campus. It was found that sound production by male during the breeding season was primarily a reproductive function and advertisement calls attract females to the breeding areas and announce other males that a given territory is occupied. The aim of this study was to provide the detailed information on the breeding behaviour and the advertisement calls of Polypedates teraiensis. The morphometric measurements of the amplecting pairs (males and females) for sexual dimorphism along with clutch sizes were also studied.

scholarly journals Spinal Muscular Atrophy after Nusinersen Therapy: Improved Physiology in Pediatric Patients with No Significant Change in Urine, Serum, and Liquor 1H-NMR Metabolomes in Comparison to an Age-Matched, Healthy Cohort

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 206
Author(s):  
Leon Deutsch ◽  
Damjan Osredkar ◽  
Janez Plavec ◽  
Blaž Stres
Keyword(s):  
Machine Learning ◽  
Spinal Muscular Atrophy ◽  
1H Nmr ◽  
Pediatric Patients ◽  
Muscular Atrophy ◽  
Male And Female ◽  
Before And After ◽  
Healthy Cohort ◽  
Males And Females ◽  
Urine Serum

Spinal muscular atrophy (SMA) is a genetically heterogeneous group of rare neuromuscular diseases and was until recently the most common genetic cause of death in children. The effects of 2-month nusinersen therapy on urine, serum, and liquor 1H-NMR metabolomes in SMA males and females were not explored yet, especially not in comparison to the urine 1H-NMR metabolomes of matching male and female cohorts. In this prospective, single-centered study, urine, serum, and liquor samples were collected from 25 male and female pediatric patients with SMA before and after 2 months of nusinersen therapy and urine samples from a matching healthy cohort (n = 125). Nusinersen intrathecal application was the first therapy for the treatment of SMA by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Metabolomes were analyzed using targeted metabolomics utilizing 600 MHz 1H-NMR, parametric and nonparametric multivariate statistical analyses, machine learning, and modeling. Medical assessment before and after nusinersen therapy showed significant improvements of movement, posture, and strength according to various medical tests. No significant differences were found in metabolomes before and after nusinersen therapy in urine, serum, and liquor samples using an ensemble of statistical and machine learning approaches. In comparison to a healthy cohort, 1H-NMR metabolomes of SMA patients contained a reduced number and concentration of urine metabolites and differed significantly between males and females as well. Significantly larger data scatter was observed for SMA patients in comparison to matched healthy controls. Machine learning confirmed urinary creatinine as the most significant, distinguishing SMA patients from the healthy cohort. The positive effects of nusinersen therapy clearly preceded or took place devoid of significant rearrangements in the 1H-NMR metabolomic makeup of serum, urine, and liquor. Urine creatinine was successful at distinguishing SMA patients from the matched healthy cohort, which is a simple systemic novelty linking creatinine and SMA to the physiology of inactivity and diabetes, and it facilitates the monitoring of SMA disease in pediatric patients through non-invasive urine collection.

scholarly journals Label Enhancement for Label Distribution Learning via Prior Knowledge

2020 ◽  
Author(s):  
Yongbiao Gao ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Emotion Recognition ◽  
Prior Knowledge ◽  
Decision Process ◽  
Age Estimation ◽  
State Of The Art ◽  
Learning Agent ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to an instance. However, most of training datasets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. We propose to use the prior knowledge to recover the label distributions. The process of recovering the label distributions from the logical labels is called label enhancement. In this paper, we formulate the label enhancement as a dynamic decision process. Thus, the label distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

scholarly journals Image Analysis of Morphometric Evaluation from the Heart Valves for Age Estimation in Thai Population

2020 ◽  
Vol 38 (3) ◽  
pp. 726-730
Author(s):  
Treerat Gumpangseth ◽  
Pornhatai Komutrattananont ◽  
Patison Palee ◽  
Sukon Prasitwattanaseree ◽  
Pasuk Mahakkanukrauh
Keyword(s):  
Image Analysis ◽  
Age Estimation ◽  
Heart Valves ◽  
Thai Population ◽  
Morphometric Evaluation

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

scholarly journals Testing the London atlas for age estimation in Thai population

2019 ◽  
Vol 78 (3) ◽  
pp. 161-164
Author(s):  
Wanwisah Namwong ◽  
Scheila Mânica
Keyword(s):  
Age Estimation ◽  
Thai Population

scholarly journals Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5463
Author(s):  
Wei-Hsiang Yu ◽  
Chih-Hao Li ◽  
Ren-Ching Wang ◽  
Chao-Yuan Yeh ◽  
Shih-Sung Chuang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
T Cell ◽  
Deep Neural Network ◽  
Cell Lymphoma ◽  
Morphological Features ◽  
T Cell Lymphoma ◽  
Cell Segmentation ◽  
T Cell Lymphomas ◽  
Morphometric Features

The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) versus intestinal T-cell lymphoma, not otherwise specified (ITCL-NOS), which is considered a major challenge for pathological diagnosis. A total of 40 histopathological whole-slide images (WSIs) from 40 surgically resected PITL cases were used as the dataset for model training and testing. A deep neural network was trained to detect and segment the nuclei of lymphocytes. Quantitative nuclear morphometrics were further computed from these predicted contours. A decision-tree-based machine learning algorithm, XGBoost, was then trained to classify PITL cases into two disease subtypes using these nuclear morphometric features. The deep neural network achieved an average precision of 0.881 in the cell segmentation work. In terms of classifying MEITL versus ITCL-NOS, the XGBoost model achieved an area under receiver operating characteristic curve (AUC) of 0.966. Our research demonstrated an accurate, human-interpretable approach to using machine learning algorithms for reducing the high dimensionality of image features and classifying T cell lymphomas that present challenges in morphologic diagnosis. The quantitative nuclear morphometric features may lead to further discoveries concerning the relationship between cellular phenotype and disease status.

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