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Statistics in Medicine Dec 2022Estimating relationships between multiple incomplete patient measurements requires methods to cope with missing values. Multiple imputation is one approach to address... (Review)
Review
Estimating relationships between multiple incomplete patient measurements requires methods to cope with missing values. Multiple imputation is one approach to address missing data by filling in plausible values for those that are missing. Multiple imputation procedures can be classified into two broad types: joint modeling (JM) and fully conditional specification (FCS). JM fits a multivariate distribution for the entire set of variables, but it may be complex to define and implement. FCS imputes missing data variable-by-variable from a set of conditional distributions. In many studies, FCS is easier to define and implement than JM, but it may be based on incompatible conditional models. Imputation methods based on multilevel modeling show improved operating characteristics when imputing longitudinal data, but they can be computationally intensive, especially when imputing multiple variables simultaneously. We review current MI methods for incomplete longitudinal data and their implementation on widely accessible software. Using simulated data from the National Health and Aging Trends Study, we compare their performance for monotone and intermittent missing data patterns. Our simulations demonstrate that in a longitudinal study with a limited number of repeated observations and time-varying variables, FCS-Standard is a computationally efficient imputation method that is accurate and precise for univariate single-level and multilevel regression models. When the analyses comprise multivariate multilevel models, FCS-LMM-latent is a statistically valid procedure with overall more accurate estimates, but it requires more intensive computations. Imputation methods based on generalized linear multilevel models can lead to biased subject-level variance estimates when the statistical analyses involve hierarchical models.
Topics: Humans; Longitudinal Studies; Models, Statistical; Biometry; Research Design; Software; Computer Simulation
PubMed: 36220138
DOI: 10.1002/sim.9592 -
BioMed Research International 2022To compare the total corneal astigmatism (TCA) measured by IOLMaster 700 and Pentacam and to investigate the consistency of corneal keratometry (CK) measured by...
PURPOSE
To compare the total corneal astigmatism (TCA) measured by IOLMaster 700 and Pentacam and to investigate the consistency of corneal keratometry (CK) measured by IOLMaster and Pentacam.
METHODS
Cataract patients were retrospectively enrolled in March and April, 2021. Retrospective analysis was performed on those patients with binocular and monocular CK measured by IOLMaster and Pentacam.
RESULTS
A total of 102 patients (204 eyes) were included, 64 of whom were female (62.75%). The flat (K1) and steep (K2) CK of anterior corneal surface (ACS) and flat (TK1) and steep (TK2) of total cornea measured with IOLMaster 700 were 44.16 ± 1.60 D, 45.09 ± 1.68 D, 44.12 ± 1.62 D, and 45.14 ± 1.69 D, respectively. Those measured with Pentacam were 44.31 ± 1.57 D, 45.22 ± 1.65 D, 44.15 ± 1.67 D, and 45.19 ± 1.82 D, respectively. The astigmatism of ACS and TCA were -0.94 ± 0.63 D and -1.02 ± 0.69 D ( < 0.01) in the IOLMaster group and -0.90 ± 0.59 D and -1.05 ± 0.81 D in the Pentacam group, respectively ( < 0.01). TCA measurement results of IOLMaster and Pentacam were consistent (Pearson = 0.710, < 0.01), and there was no significant difference ( = 0.591).
CONCLUSIONS
Total corneal astigmatism measured by IOLMaster was consistent with that measured by Pentacam. The difference between the astigmatism of anterior corneal surface and total cornea was detected in the measurement of IOLMaster and Pentacam, respectively.
Topics: Astigmatism; Biometry; Cataract; Cornea; Corneal Diseases; Corneal Topography; Female; Humans; Male; Retrospective Studies
PubMed: 35845936
DOI: 10.1155/2022/9236006 -
Survey of Ophthalmology 2022In pediatric ophthalmology it is often necessary to obtain axial length in young children. For children older than 3 years, noncontact biometry can be used. For younger... (Meta-Analysis)
Meta-Analysis Review
In pediatric ophthalmology it is often necessary to obtain axial length in young children. For children older than 3 years, noncontact biometry can be used. For younger children this is usually not an option, and the clinician needs to rely on other imaging modalities. Depicted data curves in textbooks elaborate on few studies and limited number of subjects. The existing literature regarding normal axial length for preterm infants and term newborns is summarized and critically appraised for number of subjects, relevance, measurement method and error, gender and retinopathy of prematurity. We obtained axial length measurements for a total number of 6,575 eyes in 27 papers published from 1964 to 2018 (9 papers with 2,272 eyes for preterm children, 24 papers with 4,303 eyes for term children). Initially, axial length increases rapidly: from a mean 5.1-16.2 mm in week 12 to week 37 gestational age. From 38 weeks, growth rate decreases from 16.2 mm to a mean of 21.8 mm at 3 years old. Male infants have a larger average axial length than females at birth; the difference is 0.24 mm (95%CI: 0.15-0.33, P < 0.001). We present a useful growth curve and formula that may serve as a reference for diagnosing abnormal growth.
Topics: Biometry; Child, Preschool; Eye; Female; Gestational Age; Humans; Infant; Infant, Newborn; Infant, Premature; Male; Refraction, Ocular
PubMed: 34116120
DOI: 10.1016/j.survophthal.2021.05.010 -
Sensors (Basel, Switzerland) Jan 2023In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite... (Review)
Review
In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.
Topics: Humans; Biometry; Biometric Identification; Electrocardiography; Bibliometrics
PubMed: 36772546
DOI: 10.3390/s23031507 -
Sensors (Basel, Switzerland) Sep 2022Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its...
Identifying an individual based on their physical/behavioral characteristics is known as biometric recognition. Gait is one of the most reliable biometrics due to its advantages, such as being perceivable at a long distance and difficult to replicate. The existing works mostly leverage Convolutional Neural Networks for gait recognition. The Convolutional Neural Networks perform well in image recognition tasks; however, they lack the attention mechanism to emphasize more on the significant regions of the image. The attention mechanism encodes information in the image patches, which facilitates the model to learn the substantial features in the specific regions. In light of this, this work employs the Vision Transformer (ViT) with an attention mechanism for gait recognition, referred to as Gait-ViT. In the proposed Gait-ViT, the gait energy image is first obtained by averaging the series of images over the gait cycle. The images are then split into patches and transformed into sequences by flattening and patch embedding. Position embedding, along with patch embedding, are applied on the sequence of patches to restore the positional information of the patches. Subsequently, the sequence of vectors is fed to the Transformer encoder to produce the final gait representation. As for the classification, the first element of the sequence is sent to the multi-layer perceptron to predict the class label. The proposed method obtained 99.93% on CASIA-B, 100% on OU-ISIR D and 99.51% on OU-LP, which exhibit the ability of the Vision Transformer model to outperform the state-of-the-art methods.
Topics: Biometry; Gait; Neural Networks, Computer; Pattern Recognition, Automated
PubMed: 36236462
DOI: 10.3390/s22197362 -
Sensors (Basel, Switzerland) Jan 2023A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to...
A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.
Topics: Bayes Theorem; Eye Movements; Biometry; Biometric Identification; Eye-Tracking Technology
PubMed: 36772302
DOI: 10.3390/s23031262 -
BMC Pregnancy and Childbirth Dec 2023Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective,...
BACKGROUND
Fetal birth weight (FBW) estimation involves predicting the weight of a fetus prior to delivery. This prediction serves as a crucial input for ensuring effective, accurate, and appropriate obstetric planning, management, and decision-making. Typically, there are two methods used to estimate FBW: the clinical method (which involves measuring fundal height and performing abdominal palpation) or sonographic evaluation. The accuracy of clinical method estimation relies heavily on the experience of the clinician. Sonographic evaluation involves utilizing various mathematical models to estimate FBW, primarily relying on fetal biometry. However, these models often demonstrate estimation errors that exceed acceptable levels, which can result in inadequate labor and delivery management planning. One source of this estimation error is sociodemographic variations between population groups in different countries. Additionally, inter- and intra-observer variability during fetal biometry measurement also contributes to errors in FBW estimation.
METHODS
In this research, a novel mathematical model was proposed through multiple regression analysis to predict FBW with an accepted level of estimation error. To develop the model, population data consisting of fetal biometry, fetal ultrasound images, obstetric variables, and maternal sociodemographic factors (age, marital status, ethnicity, educational status, occupational status, income, etc.) of the mother were collected. Two approaches were used to develop the mathematical model. The first method was based on fetal biometry data measured by a physician and the second used fetal biometry data measured using an image processing algorithm. The image processing algorithm comprises preprocessing, segmentation, feature extraction, and fetal biometry measurement.
RESULTS
The model developed using the two approaches were tested to assess their performance in estimating FBW, and they achieved mean percentage errors of 7.53% and 5.89%, respectively. Based on these results, the second model was chosen as the final model.
CONCLUSION
The findings indicate that the developed model can estimate FBW with an acceptable level of error for the Ethiopian population. Furthermore, this model outperforms existing models for FBW estimation. The proposed approach has the potential to reduce infant and maternal mortality rates by providing accurate fetal birth weight estimates for informed obstetric planning.
Topics: Pregnancy; Female; Humans; Birth Weight; Fetal Weight; Ultrasonography, Prenatal; Biometry; Fetus; Gestational Age
PubMed: 38082249
DOI: 10.1186/s12884-023-06145-9 -
Sensors (Basel, Switzerland) Oct 2022Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting...
Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.
Topics: Biometry; Computer Security; Humans; Machine Learning; Privacy; Telemedicine
PubMed: 36236752
DOI: 10.3390/s22197655 -
Scientific Reports Mar 2022To evaluate the performance of a new swept source optical coherence tomography optical biometer, ANTERION, in ocular biometry and intraocular lens (IOL) calculation...
To evaluate the performance of a new swept source optical coherence tomography optical biometer, ANTERION, in ocular biometry and intraocular lens (IOL) calculation compared with the reference standard of Dual Scheimpflug Analyzer (GALILEI, G6). A prospective comparative study was conducted in a tertiary eye center. Cataract patients were scanned with both devices in a random fashion, and parameters from the devices were analyzed in terms of mean difference and intraclass correlation coefficient (ICC). Bland-Altman plots were performed to compare agreement between the devices. Ninety-six eyes from 96 patients were enrolled for evaluation. With the exception of ACD, all parameters were significantly different, but excellent agreement was revealed for all of them. The mean difference in axial length was 0.03 mm, and ICC was 0.999. Calculated IOL power with Barrett formula revealed that 93.75% were within 1 diopter and the prediction error was 0.03 diopter. Biometry of the devices were arithmetically different. However, the mean difference of the key factors in IOL calculation were small and appeared to be negligible for the purposes of clinical application. The performance of ANTERION was comparable to that of G6 in biometric measurement and IOL calculation; however, the devices cannot be used interchangeably.
Topics: Axial Length, Eye; Biometry; Humans; Interferometry; Prospective Studies; Reproducibility of Results; Tomography, Optical Coherence
PubMed: 35246594
DOI: 10.1038/s41598-022-07696-1 -
Experimental Physiology May 2011
Topics: Biometry; Data Display
PubMed: 21511757
DOI: 10.1113/expphysiol.2011.057323