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

This paper focuses on non-invasive blood glucose determination using photoplethysmographic (PPG) signals, which is crucial for managing diabetes. Diabetes stands as one of the world’s major chronic diseases. Untreated diabetes frequently leads to fatalities. Current self-monitoring techniques for measuring diabetes require invasive procedures such as blood or bodily fluid sampling, which may be very uncomfortable. Hence, there is an opportunity for non-invasive blood glucose monitoring through smart devices capable of measuring PPG signals. The primary goal of this research was to propose methods for glycemic classification into two groups (low and high glycemia) and to predict specific glycemia values using machine learning techniques. Two datasets were created by measuring PPG signals from 16 individuals using two different smart devices – a smart wristband and a smartphone. Simultaneously, the reference blood glucose levels were invasively measured using a glucometer. The PPG signals were preprocessed, and 27 different features were extracted. With the use of feature selection, only 10 relevant features were chosen. Numerous machine learning models were developed. Random Forest (RF) and Support Vector Machine (SVM) with the radial basis function (RBF) kernel performed best in classifying PPG signals into two groups. These models achieved an accuracy of 76% (SVM) and 75% (RF) on the smart wristband test dataset. The functionality of the proposed models was then verified on the smartphone test dataset, where both models achieved similar accuracy: 74% (SVM) and 75% (RF). For predicting specific glycemia values, RF performed best. Mean Absolute Error (MAE) was 1.25 mmol/l on the smart wristband test dataset and 1.37 mmol/l on the smartphone test dataset.

Age-at-death estimation of adult skeletal remains is a key part of biological profile estimation, yet it remains problematic for several reasons. One of them may be the subjective nature of the evaluation of age-related changes, or the fact that the human eye is unable to detect all the relevant surface changes. We have several aims: (1) to validate already existing computer models for age estimation; (2) to propose our own expert system based on computational approaches to eliminate the factor of subjectivity and to use the full potential of surface changes on an articulation area; and (3) to determine what age range the pubic symphysis is useful for age estimation. A sample of 483 3D representations of the pubic symphyseal surfaces from the ossa coxae of adult individuals coming from four European (two from Portugal, one from Switzerland and Greece) and one Asian (Thailand) identified skeletal collections was used. A validation of published algorithms showed very high error in our dataset-the Mean Absolute Error (MAE) ranged from 16.2 and 25.1 years. Two completely new approaches were proposed in this paper: SASS (Simple Automated Symphyseal Surface-based) and AANNESS (Advanced Automated Neural Network-grounded Extended Symphyseal Surface-based), whose MAE values are 11.7 and 10.6 years, respectively. Lastly, it was demonstrated that our models could estimate the age-at-death using the pubic symphysis over the entire adult age range. The proposed models offer objective age estimates with low estimation error (compared to traditional visual methods) and are able to estimate age using the pubic symphysis across the entire adult age range.

• MeSH
• Data Mining MeSH
• Adult MeSH
• Humans MeSH
• Forensic Anthropology methods   MeSH
• Pubic Symphysis * MeSH
• Age Determination by Skeleton methods   MeSH
• Imaging, Three-Dimensional MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Publication type
• Journal Article MeSH

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

Ciele: Analyzovať naše výsledky refrakcie po operácii sivého zákalu v závislosti od rôznych premených akými sú rôzne typy monofokálnych umelých vnútroočných šošoviek, vzorce pre ich výpočet, pohlavie, vek a lateralita Miesto výskumu: Klinika oftalmológie LFUK a UNB Ružinov, Bratislava, Slovensko Dizajn: Retrospektívna štúdia. Metodika: Analyzovali sme 173 očí (118 pacientov) po nekomplikovanej operácii sivého zákalu. Zisťovali sme rozdiel a absolútny rozdiel medzi skutočnou a odhadovanou pooperačnou refrakciou, tzv. priemernú chybu predikcie pooperačnej refrakcie (prediction error, PE) a jej priemernú absolútnu hodnotu (mean absolute error, MAE). Výsledky a záver: Neboli preukázané štatisticky významné rozdiely v PE a MAE v závislosti od jednotlivých vzorcov, typov umelých vnútroočných šošoviek, pohlavia, veku a laterality.

Purpose: To analyze refractive results after cataract surgery in relation to used type of monofocal intraocular lens, calculation formula, to age, gender and laterality. Settings: Department of Ophthalmology, Comenius University and University hospital in Bratislava, Slovakia Methods: We analyzed 173 eyes (118 patients) after uneventful cataract surgery. We calculated prediction error (PE) and mean absolute error (MAE) of postoperative refraction. Results and conclusion: We found no statistically significant differences in PE and MAE in relation to types of used IOL, calculation formulas, gender, age or laterality.

• Keywords
• optická biometrie, monofokální IOL, kalkulace IOL, pooperační refrakce,
• MeSH
• Biometry MeSH
• Adult MeSH
• Lens Implantation, Intraocular MeSH
• Data Interpretation, Statistical MeSH
• Cataract * therapy   MeSH
• Middle Aged MeSH
• Humans MeSH
• Postoperative Period * MeSH
• Refraction, Ocular * MeSH
• Retrospective Studies MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Treatment Outcome MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH

In order to analyze and improve the dental age estimation in children and adolescents for forensic purposes, 22 age estimation methods were compared to a sample of 976 orthopantomographs (662 males, 314 females) of healthy Czech children and adolescents aged between 2.7 and 20.5 years. All methods are compared in terms of the accuracy and complexity and are based on various data mining methods or on simple mathematical operations. The winning method is presented in detail. The comparison showed that only three methods provide the best accuracy while remaining user-friendly. These methods were used to build a tabular multiple linear regression model, an M5P tree model and support vector machine model with first-order polynomial kernel. All of them have mean absolute error (MAE) under 0.7 years for both males and females. The other well-performing data mining methods (RBF neural network, K-nearest neighbors, Kstar, etc.) have similar or slightly better accuracy, but they are not user-friendly as they require computing equipment and the implementation as computer program. The lowest estimation accuracy provides the traditional model based on age averages (MAE under 0.96 years). Different relevancy of various teeth for the age estimation was found. This finding also explains the lowest accuracy of the traditional averages-based model. In this paper, a technique for missing data replacement for the cases with missing teeth is presented in detail as well as the constrained tabular multiple regression model. Also, we provide free age prediction software based on this wining model.

• MeSH
• Data Mining MeSH
• Dentition, Permanent * MeSH
• Child MeSH
• Humans MeSH
• Linear Models MeSH
• Adolescent MeSH
• Young Adult MeSH
• Neural Networks, Computer MeSH
• Child, Preschool MeSH
• Radiography, Panoramic MeSH
• Decision Trees MeSH
• Software MeSH
• Support Vector Machine MeSH
• Age Determination by Teeth methods   MeSH
• Tooth growth & development   MeSH
• Check Tag
• Child MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH