OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor. METHODS: We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE). RESULTS: The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.
- MeSH
- Acetabulum * diagnostic imaging MeSH
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Linear Models MeSH
- Young Adult MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Software * MeSH
- Forensic Anthropology * methods MeSH
- Machine Learning * MeSH
- Age Determination by Skeleton * methods MeSH
- Imaging, Three-Dimensional * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Chronic hyperplastic candidiasis (CHC) presents a distinctive and relatively rare form of oral candidal infection characterized by the presence of white or white-red patches on the oral mucosa. Often mistaken for leukoplakia or erythroleukoplakia due to their appearance, these lesions display nonhomogeneous textures featuring combinations of white and red hyperplastic or nodular surfaces. Predominant locations for such lesions include the tongue, retro-angular mucosa, and buccal mucosa. This paper aims to investigate the potential influence of specific anatomical locations, retro-angular mucosa, on the development and occurrence of CHC. By examining the relationship between risk factors, we present an approach based on machine learning (ML) to predict the location of CHC occurrence. In this way, we employ Gradient Boosting Regression (GBR) to classify CHC lesion locations based on important risk factors. This estimator can serve both research and diagnostic purposes effectively. The findings underscore that the proposed ML technique can be used to predict the occurrence of CHC in retro-angular mucosa compared to other locations. The results also show a high rate of accuracy in predicting lesion locations. Performance assessment relies on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2), and Mean Absolute Error (MAE), consistently revealing favorable results that underscore the robustness and dependability of our classification method. Our research contributes valuable insights to the field, enhancing diagnostic accuracy and informing treatment strategies.
- Publication type
- Journal Article MeSH
BACKGROUND: Motor skills in children have traditionally been examined via challenging speech tasks such as syllable repetition, and calculating the syllabic rate using a stopwatch or by inspecting the oscillogram followed by a laborious comparison of the scores on a look-up table representing the typical performances of children of the given age and sex. As the commonly used performance tables are over-simplified to allow for manual scoring, we raise the question of whether a computational model of motor skills development could be more informative, and could allow for the automated screening of children to detect underdeveloped motor skills. METHODS: We recruited a total of 275 children aged four to 15 years. All the participants were native Czech speakers with no history of hearing or neurological impairments. We recorded each child's performance of/pa/-/ta/-/ka/syllable repetition. Various parameters of diadochokinesis (DDK; DDK rate, DDK regularity, voice onset time [VOT] ratio, syllable, vowel and VOT duration) were investigated in the acoustic signals using supervised reference labels. Female and male participants were analyzed separately by comparing younger, middle, and older age groups of children via ANOVA. Finally, we implemented a fully automated model that estimated the developmental age of a child based on the acoustic signal, and evaluated its accuracy using Pearson's correlation coefficient and normalized root-mean-squared errors (RMSEs). RESULTS: The DDK rate reflected the ages of the children proportionally (p < 0.001). Other DDK parameters also showed strong sensitivity to age (p < 0.001), with the exception of VOT duration, which had a smaller effect (p = 0.091). The effect of age was found to be sex specific for the syllable length (p < 0.001) and DDK rate (p = 0.003). We observed that females spoke more slowly and had a longer VOT at preschool age (p < 0.001). The DDK rate obtained via the automated algorithm was strongly correlated with the reference (p < 0.001, Pearson's correlation coefficient of 0.97), with a low normalized RMSE of 3.77%. CONCLUSIONS: As children develop their motor skills, they are capable of shortening the vowels to increase the rate of syllabic repetitions. The nonlinear development in childhood and adolescence, with a steady state in adulthood, follows a logistic function for the DDK rate. This study demonstrates that the development of motor skills can be examined sensitively and more appropriately by a fully automated noninvasive procedure that also accounts for the dispersion of values within age brackets.
INTRODUCTION: Automated bone age assessment has recently become increasingly popular. The aim of this study was to assess the agreement between automated and manual evaluation of bone age using the method according to Tanner-Whitehouse (TW3) and Greulich-Pyle (GP). METHODS: We evaluated 1285 bone age scans from 1202 children (657 scans from 612 boys) by using both manual and automated (TW3 as well as GP) bone age assessment. BoneXpert software versions 2.4.5.1. (BX2) and 3.2.1. (BX3) (Visiana, Holte, Denmark) were compared with manual evaluation using root mean squared error (RMSE) analysis. RESULTS: RMSE for BX2 was 0.57 and 0.55 years in boys and 0.72 and 0.59 years in girls, respectively for TW3 and GP. For BX3, RMSE was 0.51 and 0.68 years in boys and 0.49 and 0.52 years in girls, respectively for TW3 and GP. Sex- and age-specific analysis for BX2 identified the largest differences between manual and automated TW3 evaluation in girls between 6-7, 12-13, 13-14 and 14-15 years, with RMSE 0.88, 0.81, 0.92 and 0.84 years, respectively. The BX3 version showed better agreement with manual TW3 evaluation (RMSE 0.64, 0.45, 0.46 and 0.57). CONCLUSION: The latest version of the BoneXpert software provides improved and clinically sufficient agreement with manual bone age evaluation in children of both sexes compared to the previous version and may be used for routine bone age evaluation in non-selected cases in pediatric endocrinology care.
- MeSH
- White People MeSH
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Software * MeSH
- Age Determination by Skeleton * methods MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Validation Study MeSH
This study was conducted to evaluate public awareness about COVID with aimed to check public strategies against COVID-19. A semi structured questionnaire was collected and the data was analyzed using some statistical tools (PLS-SEM) and artificial neural networks (ANN). We started by looking at the known causal linkages between the different variables to see if they matched up with the hypotheses that had been proposed. Next, for this reason, we ran a 5,000-sample bootstrapping test to assess how strongly our findings corroborated the null hypothesis. PLS-SEM direct path analysis revealed HRP -> PA-COVID, HI -> PA-COVID, MU -> PA-COVID, PM -> PA-COVID, SD -> PA-COVID. These findings provide credence to the acceptance of hypotheses H1, H3, and H5, but reject hypothesis H2. We have also examined control factors such as respondents' age, gender, and level of education. Age was found to have a positive correlation with PA-COVID, while mean gender and education level were found to not correlate at all with PA-COVID. However, age can be a useful control variable, as a more seasoned individual is likely to have a better understanding of COVID and its effects on independent variables. Study results revealed a small moderation effect in the relationships between understudy independent and dependent variables. Education significantly moderates the relationship of PA-COVID associated with MU, PH, SD, RP, PM, PA-COVID, depicts the moderation role of education on the relationship between MU*Education->PA-COVID, HI*Education->PA.COVID, SD*Education->PA.COVID, HRP*Education->PA.COVID, PM*Education -> PA.COVID. The artificial neural network (ANN) model we've developed for spreading information about COVID-19 (PA-COVID) follows in the footsteps of previous studies. The root means the square of the errors (RMSE). Validity measures how well a model can predict a certain result. With RMSE values of 0.424 for training and 0.394 for testing, we observed that our ANN model for public awareness of COVID-19 (PA-COVID) had a strong predictive ability. Based on the sensitivity analysis results, we determined that PA. COVID had the highest relative normalized relevance for our sample (100%). These factors were then followed by MU (54.6%), HI (11.1%), SD (100.0%), HRP (28.5%), and PM (64.6%) were likewise shown to be the least important factors for consumers in developing countries struggling with diseases caused by contaminated water. In addition, a specific approach was used to construct a goodness-of-fit coefficient to evaluate the performance of the ANN models. The study will aid in the implementation of effective monitoring and public policies to promote the health of local people.
- MeSH
- COVID-19 * epidemiology MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Sustainable Development MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Objective.Noninvasive measurement of oxygen saturation (SpO2) using transmissive photoplethysmography (tPPG) is clinically accepted and widely employed. However, reflective photoplethysmography (rPPG)-currently present in smartwatches-has not become equally accepted, partially because the pathlengths of the red and infrared PPGs are patient-dependent. Thus, even the most popular 'Ratio of Modulation' (R) method requires patient-dependent calibration to reduce the errors in the measurement ofSpO2using rPPGs.Approach.In this paper, a correction factor or 'pathlength ratio'βis introduced in an existing calibration-free algorithm that compensates the patient-dependent pathlength variations, and improved accuracy is obtained in the measurement ofSpO2using rPPGs. The proposed pathlength ratioβis derived through the analytical model of a rPPG signal. Using the new expression and data obtained from a human hypoxia study wherein arterial oxygen saturation values acquired through Blood Gas Analysis were employed as a reference,βis determined.Main results.The results of the analysis show that a specific combination of theβand the measurements on the pulsating part of the natural logarithm of the red and infrared PPG signals yields a reduced root-mean-square error (RMSE). It is shown that the average RMSE in measuringSpO2values reduces to 1 %.Significance.The human hypoxia study data used for this work, obtained in a previous study, coversSpO2values in the range from 70 % to 100 %, and thus shows that the pathlength ratioβproposed here works well in the range of clinical interest. This work demonstrates that the calibration-free method applicable for transmission type PPGs can be extended to determineSpO2using reflective PPGs with the incorporation of the correction factorβ. Our algorithm significantly reduces the number of parameters needed for the estimation, while keeping the RMSE below the clinically accepted 2 %.
- MeSH
- Blood Gas Analysis methods MeSH
- Photoplethysmography * methods MeSH
- Hypoxia MeSH
- Calibration MeSH
- Oxygen metabolism MeSH
- Humans MeSH
- Oximetry * methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
PURPOSE: The aim of this study was to verify the possibility of summing the dose distributions of combined radiotherapeutic treatment of cervical cancer using the extended Lucas-Kanade algorithm for deformable image registration. MATERIALS AND METHODS: First, a deformable registration of planning computed tomography images for the external radiotherapy and brachytherapy treatment of 10 patients with different parameter settings of the Lucas-Kanade algorithm was performed. By evaluating the registered data using landmarks distance, root mean square error of Hounsfield units and 2D gamma analysis, the optimal parameter values were found. Next, with another group of 10 patients, the accuracy of the dose mapping of the optimized Lucas-Kanade algorithm was assessed and compared with Horn-Schunck and modified Demons algorithms using dose differences at landmarks. RESULTS: The best results of the Lucas-Kanade deformable registration were achieved for two pyramid levels in combination with a window size of 3 voxels. With this registration setting, the average landmarks distance was 2.35 mm, the RMSE was the smallest and the average gamma score reached a value of 86.7%. The mean dose difference at the landmarks after mapping the external radiotherapy and brachytherapy dose distributions was 1.33 Gy. A statistically significant difference was observed on comparing the Lucas-Kanade method with the Horn-Schunck and Demons algorithms, where after the deformable registration, the average difference in dose was 1.60 Gy (P-value: 0.0055) and 1.69 Gy (P-value: 0.0012), respectively. CONCLUSION: Lucas-Kanade deformable registration can lead to a more accurate model of dose accumulation and provide a more realistic idea of the dose distribution.
Soil organic carbon (SOC) tends to form complexes with most metallic ions within the soil system. Relatively few studies compare SOC predictions via portable X-ray fluorescence (pXRF) measured data coupled with the Cubist algorithm. The current study applied three different Cubist models to estimate SOC while using several pXRF measured data. Soil samples (n = 158) were collected from the Litavka floodplain area during two separate sampling campaigns in 2018. Thirteen pXRF data or predictors (K, Ca, Rb, Mn, Fe, As, Ba, Th, Pb, Sr, Ti, Zr, and Zn) were selected to develop the proposed models. Validation and comparison of the models applied the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results revealed that Cubist 1, utilizing all the predictors yielded the best model outcome (MAE = 0.51%, RMSE = 0.68%, R2 = 0.78) followed by Cubist 2, using predictors with relatively high importance (VarImp. predictors) (MAE = 0.64%, RMSE = 0.82%, R2 = 0.68), and lastly Cubist 3 with predictors showing a significantly positive correlation (MAE = 0.69%, RMSE = 0.90%, R2 = 0.62). The Cubist 1 model was considered more promising for explaining the complex relationships between SOC and the pXRF data used. Moreover, for the estimation of SOC in temperate floodplain soils all the Cubist models gave an acceptable model. However, future research should focus on using other auxiliary data [e.g., soil properties, data from other sensors (e.g., FieldSpec)] as well as extend the study area to cover more soil types hence improve model robustness as well as parsimoniousness.
- MeSH
- Algorithms MeSH
- Soil Pollutants * analysis MeSH
- Environmental Monitoring MeSH
- Soil * MeSH
- Carbon analysis MeSH
- Publication type
- Journal Article MeSH
Ionic liquids (ILs) have attracted increasing attention both in the scientific community and the industry in the past two decades. Their risk of being inevitable released to ecosystem lights up the urgent research on their toxicity to the environment. To reduce the time and capital consumption on testing tremendous ILs ecotoxicity experimentally, it is essential to construct predictive models for estimating their toxicity. The objective of this study is to provide a new approach for evaluating the ecotoxicity of ILs. A comprehensive ecotoxicity dataset for Vibrio fischeri involving 142 ILs, was collected and investigated. The electrostatic potential surface areas (SEP) of separate cations and anions of ILs were firstly applied to develop predictive models for ecotoxicity on Vibrio fischeri. In addition, an intelligent algorithm named extreme learning machine (ELM) was employed to establish the predictive model. The squared correlation coefficients (R2), the average absolute error (AAE%) and the root-mean-square error (RMSE) of the developed model are 0.9272, 0.2101 and 0.3262 for the entire set, respectively. The proposed approach based on the high R2 and low deviation has remarkable potential for predicting ILs ecotoxicity on Vibrio fischeri.
Dating of clay bricks (adobe) and plasters is a relevant topic not only for building historians in the Pannonian region. Especially in vernacular architecture in this region, clay with straw amendments is a dominant construction material. The paper presents the potential of the molecular decay of these amendments to establish prediction tools for age based on infrared spectroscopic measurements. Preliminary results revealed spectral differences between the different plant parts, especially culms, nodes, and ear spindles. Based on these results, a first prediction model is presented including 14 historic samples. The coefficient of determination for the validation reached 62.2%, the (RMSE) root mean squared error amounted to 93 years. Taking the limited sample amount and the high material heterogeneity into account, this result can be seen as a promising output. Accordingly, sample size should be increased to a minimum of 100 objects and separate models for the different plant parts should be established.
- MeSH
- Time Factors MeSH
- Clay chemistry MeSH
- Construction Materials analysis MeSH
- Spectrum Analysis MeSH
- Trace Elements analysis MeSH
- Publication type
- Journal Article MeSH
