Accuracy Of Gender Prediction Research Articles

Inferring gender from first names: Comparing the accuracy of Genderize, Gender API, and the gender R package on authors of diverse nationality

Meta-researchers commonly leverage tools that infer gender from first names, especially when studying gender disparities. However, tools vary in their accuracy, ease of use, and cost. The objective of this study was to compare the accuracy and cost of the commercial software Genderize and Gender API, and the open-source gender R package. Differences in binary gender prediction accuracy between the three services were evaluated. Gender prediction accuracy was tested on a multi-national dataset of 32,968 gender-labeled clinical trial authors. Additionally, two datasets from previous studies with 5779 and 6131 names, respectively, were re-evaluated with modern implementations of Genderize and Gender API. The gender inference accuracy of Genderize and Gender API were compared, both with and without supplying trialists’ country of origin in the API call. The accuracy of the gender R package was only evaluated without supplying countries of origin. The accuracy of Genderize, Gender API, and the gender R package were defined as the percentage of correct gender predictions. Accuracy differences between methods were evaluated using McNemar’s test. Genderize and Gender API demonstrated 96.6% and 96.1% accuracy, respectively, when countries of origin were not supplied in the API calls. Genderize and Gender API achieved the highest accuracy when predicting the gender of German authors with accuracies greater than 98%. Genderize and Gender API were least accurate with South Korean, Chinese, Singaporean, and Taiwanese authors, demonstrating below 82% accuracy. Genderize can provide similar accuracy to Gender API while being 4.85x less expensive. The gender R package achieved below 86% accuracy on the full dataset. In the replication studies, Genderize and gender API demonstrated better performance than in the original publications. Our results indicate that Genderize and Gender API achieve similar accuracy on a multinational dataset. The gender R package is uniformly less accurate than Genderize and Gender API.

PSVII-6 Gender prediction in Hanwoo cattle using custom SNP arrays

Abstract Hanwoo is one of the indigenous cattle breeds in South Korea, and genetic evaluation at the national level is conducted using custom Illumina SNP arrays. Accurate gender assignment is essential in genetic evaluation and breeding programs for livestock species. In this study, we described a method for using custom single nucleotide polymorphism (SNP) array data to predict gender of Hanwoo cattle using logistic regression model. We analyzed a set of 958 SNPs, with 947 located on the X chromosome and 11 on the Y chromosome, focusing on heterozygosity rates from the X chromosome and missing rates from Y chromosome SNPs. Using a training dataset comprising 5,000 bulls and 2,005 cows with accurately recorded genders, we built a prediction model. Subsequently, we evaluated the performance of the model on test datasets consisting of 6,657 bulls, 27 steers, and 8,418 cows. The concordance between predicted gender and recorded gender were 99.98%, 100.00% and 99.98% for bulls, steers, and cows, respectively. This result showed better accuracy of gender prediction compared with the GENOMESTUDIO tool that uses only the heterozygosity rate of SNPs in X chromosome in the case of restricted number of SNPs in sex chromosomes. Overall, our findings could be used as an effective method for more precise genetic evaluation by correcting potential gender errors in Hanwoo cattle breeding programs.

Knowledge Distillation for Enhanced Age and Gender Prediction Accuracy

In recent years, the ability to accurately predict age and gender from facial images has gained significant traction across various fields such as personalized marketing, human–computer interaction, and security surveillance. However, the high computational cost of the current models limits their practicality for real-time applications on resource-constrained devices. This study addressed this challenge by leveraging knowledge distillation to develop lightweight age and gender prediction models that maintain a high accuracy. We propose a knowledge distillation method using teacher bounds for the efficient learning of small models for age and gender. This method allows the student model to selectively receive the teacher model’s knowledge, preventing it from unconditionally learning from the teacher in challenging age/gender prediction tasks involving factors like illusions and makeup. Our experiments used MobileNetV3 and EfficientFormer as the student models and Vision Outlooker (VOLO)-D1 as the teacher model, resulting in substantial efficiency improvements. MobileNetV3-Small, one of the student models we experimented with, achieved a 94.27% reduction in parameters and a 99.17% reduction in Giga Floating Point Operations per Second (GFLOPs). Furthermore, the distilled MobileNetV3-Small model improved gender prediction accuracy from 88.11% to 90.78%. Our findings confirm that knowledge distillation can effectively enhance model performance across diverse demographic groups while ensuring efficiency for deployment on embedded devices. This research advances the development of practical, high-performance AI applications in resource-limited environments.

The potential of synthetic minority oversampling technique to enhance the precision of gender prediction: an investigation of artificial neural networks with cephalometry

BACKGROUND: When creating models utilizing artificial neural networks, the quantity of training data and the distribution of data need to be considered, particularly when making gender predictions. AIM: This study seeks to determine the potential impact of using the synthetic minority oversampling technique (SMOTE) on gender prediction using the artificial neural networks model. MATERIALS AND METHODS: The current study utilized a dataset consisting of 297 cephalometric measurements from Indonesian patients, comprising 229 samples from females and 68 samples from males. WebCeph is used to measure certain parameters, such as Sella-Nation-Point A (SNA) angle, mandibular length, mandibular angle, Sella-Glabella-Point A (SGA) angle, and diagnosis. Data processing and artificial neural networks model creation were conducted using Python. RESULTS: The gender identification accuracy of the artificial neural networks model is 87% for females and 0% for males, resulting in an overall average accuracy of 78%. When using SMOTE, the accuracy is 22%, with 0% for females and 37% for males. However, when using SMOTE and normalization, the accuracy increases to 71%, with 82% for females and 30% for males. The accuracy of normalization without SMOTE is 76%, with 86% for females and 14% for males. CONCLUSIONS: This research has proven the efficacy of SMOTE in improving the classification of male matrices. Nevertheless, this study reveals that the overall accuracy results of SMOTE are suboptimal in comparison to the absence of SMOTE and normalization. The application of data balancing strategies is necessary to achieve optimal accuracy in gender prediction when artificial neural networks, and other parameters must be applied.

Exploring Gender Differences in Fatwa through Machine Learning

This paper focuses on exploring the differences in inquiries made by men and women within a religious context. Additionally, we aim to ascertain whether it’s feasible to forecast the popularity of answers and the factors contributing to their popularity. To achieve this, we compile a new dataset comprising 40,000 question-answer pairs categorized by gender and popularity. These are sourced from online question-and-answer platforms. Our methodology involves comprehensive experimental analysis, utilizing advanced Arabic text preprocessing alongside machine learning algorithms. We concentrate on two primary objectives: predicting the gender of the questioner and forecasting the popularity of answers. Furthermore, we delve into thematic variations based on gender and address pivotal research queries that offer new perspectives within this domain. These include investigating the differences between questions posed by women versus men, exploring the potential for automated classification of queries by gender, predicting the popularity of fatwas, and identifying the contributing factors to their popularity. Our experimental findings demonstrate a 98% accuracy in gender prediction, precise predictions of popularity with minimal margin for error, and the identification of topics and their associations that are more inclined towards either men or women. We intend to share both the dataset and the source code openly with the research community.

Enhancing Handwritten Signature Identification and Palm Biometric Objectives

Soft biometrics are already widely used as a support tool for user identification. However, it is not the only use for biometric information that is conceivable because such information can be sufficient to obtain minimal details from the user that are unrelated to his identity. Examples of what might be referred to as soft biometrics include gender, hand orientation, and emotional state. Utilizing physiologic modalities for soft-biometric work is extremely prevalent, prediction, but behavioral data is frequently disregarded. Keystroke dynamics and handwriting signature are two potential behavioral modalities that could be used to predict a user's gender, but they are rarely discussed in the literature together. This study seeks to fill this gap by examining the influence of combining these two distinct biometric modalities on the accuracy of gender prediction and the best way. Key Words: Item key-strokes, Bio-metric signatures, digital signs, dynamic temporal wrapping (DTW)

Gender Dimorphism in Maxillary Permanent Canine Odontometrics Based on a Three-Dimensional Digital Method and Discriminant Function Analysis in the Saudi Population

The present study aimed to analyze the gender dimorphism in odontometrics of permanent maxillary canines using a three-dimensional digital method and to test the accuracy in gender estimation using discriminant function analysis in a sample of the Saudi population. A total of 120 diagnostic dental casts of patients aged 16–29 years were used in the present study. Plaster models of their maxillary dentition obtained from the archives were scanned and digitally measured using a three-dimensional digital method. The mesiodistal width of the right and left maxillary canines and intercanine distance were measured. Gender dimorphism was established using the Garn method. Data were statistically analyzed using descriptive statistics, the Mann–Whitney U test and discriminant analyses. Males showed larger mean dimensions of canines than females with regard to both mesiodistal width and intercanine distance, and the difference was statistically significant (p < 0.05). The right maxillary canine mesiodistal width showed a higher percentage of gender dimorphism (3.5%). Discriminant function analysis showed that the overall accuracy of gender prediction was 80.5% for the multivariate analysis. The univariate discriminant function equation revealed that intercanine distance was the most accurate predictor of gender (78%), followed by the right canine mesiodistal width (76.3%). The use of three-dimensional technology for odontometrics presents a promising method, and permanent maxillary canine parameters can be used as an acceptable ancillary tool for gender estimation in forensic science.

Gender prediction with descriptive textual data using a Machine Learning approach

Social media are well-established means of online communication, generating vast amounts of data. In this paper, we focus on Twitter and investigate behavioural differences between male and female users on social media. Using Natural Language Processing and Machine Learning approaches, we propose a user gender identification method that considers both the tweets and the Twitter profile description of a user. For experimentation and evaluation, we enriched and used an existing Twitter User Gender Classification dataset, which is freely available on Kaggle. We considered a variety of methods and components, such as the Bag of Words model, pre-trained word embeddings (GLOVE, BERT, GPT2 and Word2Vec) and machine learners, e.g., Naïve Bayes, Support Vector Machines and Random Forests. Evaluation results have shown that including the Twitter profile description of a user significantly improves gender classification accuracy, by 10% approximately. Stanford’s GLOVE embedding model, pre-trained on 2 billion tweets, 27 billion tokens and a vocabulary size of 1.2 million words, achieved the highest gender prediction accuracy, considering both the tweets and the profile description of a user. Statistical significance has been assessed using McNemar’s two-tailed test.

Prediction of gender from longitudinal MRI data via deep learning on adolescent data reveals unique patterns associated with brain structure and change over a two-year period

Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs > 200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.

Development Of Iris Based Age And Gender Detection System

Biometric recognition has shown their utility in different aspect including reducing search space significantly which has led to improve recognition performance, reduced computation time and faster processing time, a person is identified automatically by processing the unique features that are posed by the individual. The existing biometric systems recognize and accept a person, else simply reject a person if not enrolled, but these systems do not give software attributes of a person which is needed to search impostor as it has been observed in many occasions and especially in Nigeria that identification of an individual’s age and gender does not go beyond physical factors which in most cases can be manipulated or spoofed to carry out nefarious activities. This project focuses on developing an iris-based age and gender detection system for certain individuals to identify a person in real time. Left and right irises of 190 subjects constituting 1,716 images were acquired and Five Hundred and Seventy (570) left Irises were normalized to a uniform size of 250 by 250 pixels. Three Hundred and Forty-Two (342) images were used for training while the remaining Two Hundred and Twenty-Eight (228) were used for testing. The acquired images were preprocessed by performing segmentation, filtering and normalization using Daugman’s Rubber Sheet Model. Deep learning pre-trained networks are adopted to extract features from iris images. Further, these features are trained and classified using the multi-class Support Vector Machines (SVM) model for performance evaluation of the system. The system was implemented in Matrix Laboratory 9.0 (R2016a). The performance of the system was evaluated using accuracy, precision, recall and False Positive Rate (FPR). The hypothesis stating ‘the iris has age, and gender-related information’ is proven correct from experimental results. The evaluation results showed that False Positive Rate, Recall, Precision and Accuracy of gender prediction were 2.19%, 92.31%, 96.55% and 95.62% respectively at 0.75 threshold while for age prediction, the best values were obtained at age group of 20-24 for FPR, Recall, Precision and Accuracy which were 5.61%, 94.37%, 96.18% and 94.38% respectively. The developed model gave good performance with high recognition accuracy, recall, precision and low FPR values. Therefore, the developed model can be used in all firms and industries where security and personal identification is desired for security purpose and in going through investigation of criminal records for detecting age and gender of individuals.

Deep Learning Prediction and Visualization of Gender Related Brain Changes from Longitudinal Structural MRI Data in the ABCD Study.

Deep learning algorithms for predicting from neuroimaging data have shown considerable promise. Deep learning models that take advantage of the data's 3D structure have been proven to outperform ordinary machine learning on a number of learning tasks[1]. The majority of past research in this area, however, has focused on data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a major longitudinal development research, we examine the use of structural MRI data to predict gender and to identify gender related changes in brain structure. The results demonstrate that gender prediction accuracy is extremely high (>94%), and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal regions in addition to temporal lobe. When evaluating gender predictive changes specific to a two year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Overall, our findings show a robust pattern of gender related structural brain changes, even over a small age range. This suggests the potential for evaluating the relationship of these changes to various behavioral and environmental factors to further study how the brain develops during adolescence. Clinical relevance- These results are not focused on clinical relevance currently, but in the future may be useful to characterize interactions between gender and potentially clinically relevant measures in adolescents.

Comparison of Gender Prediction Accuracy between Regression Models Derived from Hand, Foot Measurements and Long Bone Measurements in a Sample of Kolhapur Population

Introduction: Here, we aim to compare the accuracy of the regression formula derived to predict genderusing data on measurements of foot and hand with the formula derived to predict gender using the lengthmeasurements data of the long bones (tibia and ulna).Methods: Patients attending the outpatient services at the Orthopedic Department, and between the agerange 18 to 50 years were recruited (n=1000; 500 males and 500 females). Subjects suffering from any kindof bone deformity were excluded. Vallois method was used to estimate the measurements of hand, foot,tibia, and ulna. Regression formulas were obtained from the hand, foot-long bones measurements; to predictgender, using multiple logistic regression.Results: Differences between male’s and female’s measurements of hand (P<0.001), long bones (P<0.001),and foot (P<0.001) were significant. The accuracy of the model used to predict gender, which was calculatedfrom the dimensions of hand and foot was 81.5%. The accuracy of the model used to predict gender calculatedfrom the long bone measurement was 78.3%.Conclusions: Dimensions of hand and foot are a better predictor (81.5%) of gender vs the length of longbones (tibia and ulna) (78.3%) in the Kolhapur population.

Radiomorphometric of the Jaw for Gender Prediction: A Digital Panoramic Study

Background. Gender identification by using skeletal identificati on is an important tool in forensic, medico-legal, bioarkeology, and anthropology. Traditional morphological methods depended on the anthropologist subjectivity that caused a significant difference among the observer. This study aims to develop the discriminant function for gender prediction in a Surabaya-Indonesia population age ranges 15-25-year-olds by using a panoramic radiograph. This research used 273 panoramic radiographs consisted of 129 male panoramic radiographs and 144 female panoramic radiographs. The researchers measured 11 parameters of the jaw such as two gonial angles, two mandibular ramus heights, two mandibular ramus widths, two mandibular corpus lengths, two nasal line maxilla, and anterior mandibular corpus heights. The researchers analyzed the data by using the discriminant analysis of the IBM SPSS statistic 24. Results. the result of our study shows there were significant differences in the jaw morphometry between males and females, except the mandibular ramus widths. The jaw size in males was larger than that of the female. The biggest dimorphism variables based on the Wilks lambda value were gonial angles, mandibular ramus heights, mandibular corpus lengths, and nasal lines. While the smallest dimorphism variables were mandibular ramus widths. The accuracy of discriminant analysis for each variable ranges from 47.3% to 93.8%. Conclusion. This preliminary study in Surabaya-Indonesia population age ranges 15-25-year-olds by using panoramic radiograph shows the highest accuracy of gender prediction by using discriminant function was obtained from the combination of the nine jaw parameters.

Convolution Neural Network Application in the Simultaneous Detection of Gender and Variety of Silkworm (Bombyx mori) Cocoons

Convolution neural network (CNN) has been widely studied in the field of computer vision with good results, and a lot of research is still needed to show its application effect in the near-infrared spectroscopy with 1-D input data and small sample set. In this paper, the near-infrared diffuse transmission spectra of 2386 silkworm (Bombyx mori) cocoons from the 4th day of cocooning to the 13th day before turning into moths were collected. After the mean center preprocessing, CNN, SVM and LDA were used to model and analyze. Experimental results showed that, with the convolution kernel width 100, the convolution kernel num 16, and the stride 50, the good generalization effect of CNN model can be achieved when the simple structure of 6 layers without pooling layer was adopted. For each day, the gender prediction accuracy of cocoons is more than 90% and the highest accuracy is 100% on the 6th day with optimal model CNN. For each day, the variety prediction accuracy of cocoons is more than 98% and the highest accuracy is 100% with optimal model LDA. There is no significant correlation between pupa age and the gender and variety detection accuracy of silkworm cocoons. This can help broaden the pupa gender identification time in the actual production of silkworm egg farms. In the application of simultaneous identification of gender and variety, CNN model has the highest accuracy of 94%, LDA model has the medium accuracy of 92.5%, and SVM model has the lowest accuracy of 89.5%.

Mystery behind dimensions- “revealing gender by Foramen Magnum ”- a study

Abstract Introduction Human identification is of vital significance to the society and one of the positive indicators for identity is gender. This research paper presents a database of morphometric variables of Foramen Magnum and Occipital Condyle and their significance in gender determination along with different shapes of foramen magnum. Aim The aim of the study was to evaluate the dimensions and shapes of foramen magnum and occipital condyles as an aid in gender determination using computed tomographic (CT) images. Materials and methodology The present study included 60 axial CT scans of which 30 were males and 30 were females in the age range of 20-70 year fulfilling the inclusion and exclusion criteria. The various radiological parameters of foramen magnum and occipital condyles of 60 subjects including 30 males and 30 females were observed and significant sexual dimorphic differences were observed in various metric variables of Foramen Magnum and Occipital Condyle which were considerably larger in male population. Results Discriminant function analysis revealed an accuracy of 80% for Foramen Magnum, 78.3% for Occipital Condyle and 88.3% for concomitant dimensions of Foramen Magnum and Occipital Condyle. Conclusion This study is distinctive in that morphological variation of shapes of Foramen Magnum with respect to gender observed and certain different morphometric parameters which have been found to be statistically significant both for Foramen Magnum and Occipital Condyle have not been studied in the past. Hence, it can be concluded that morphometric evaluation of dimensions of Foramen Magnum and Occipital Condyle using recent and advanced radiological methods such as CT increases the accuracy of gender prediction.

Demographic Information Prediction: A Portrait of Smartphone Application Users

Demographic information is usually treated as private data ( e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. Then, by considering the category-unbalance problem, we make use of the correlation between users’ demographic information and their requested Internet resources to make the prediction, and propose an optimal method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21 percent of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance ( e.g. , 73.84 percent of Accuracy in the prediction of age group and 66.42 percent of Accuracy in the prediction of phone level).

Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.

Prevalence of certain forms of psychopathology, such as autism and depression, differs between genders and understanding gender differences of the neurotypical brain may provide insights into risk and protective factors. In recent research, resting state functional magnetic resonance imaging (rfMRI) is widely used to map the inherent functional networks of the brain. Although previous studies have reported gender differences in rfMRI, the robustness of gender differences is not well characterized. In this study, we use a large data set to test whether rfMRI functional connectivity (FC) can be used to predict gender and identify FC features that are most predictive of gender. We utilized rfMRI data from 820 healthy controls from the Human Connectome Project. By applying a predefined functional template and partial least squares regression modeling, we achieved a gender prediction accuracy of 87% when multi-run rfMRI was used. Permutation tests confirmed that gender prediction was reliable ( p<.001). Effects of motion, age, handedness, blood pressure, weight, and brain volume on gender prediction are discussed. Further, we found that FC features within the default mode (DMN), fronto-parietal and sensorimotor networks contributed most to gender prediction. In the DMN, right fusiform gyrus and right ventromedial prefrontal cortex were important contributors. The above regions have been previously implicated in aspects of social functioning and this suggests potential gender differences in social cognition mediated by the DMN. Our findings demonstrate that gender can be reliably predicted using rfMRI data and highlight the importance of controlling for gender in brain imaging studies.

P03.28: Determination of fetal gender by ultrasound at 11–14 weeks

To assess the accuracy of gender assignment at the time of routine first trimester fetal risk assessment for aneuploidy. To document the learning curve required to obtain maximum diagnostic accuracy. A prospective observational study was carried out on 615 singleton pregnancies during their first trimester ultrasound. The mid-sagittal plane was located and the genital tubercle identified. An assessment was made whether the phallus pointed cranially (male) or caudally (female) or whether it was uncertain. Follow up was obtained from the patients after birth, hospital birth records, obstetrician or karyotyping results. Follow up was available for 592 patients. Of these, 488 (82.4%) had gender assigned on the basis of their scan with an overall success rate of correctly identifying the gender of 84.0%. We found that it was a skill that was quickly learned with a small but definite learning curve. Gender assignment was possible in 80.0% at the beginning compared to 91.1% at the end of the study (p < 0.01) due to an improved ability to correctly identify the genitalia. The gestational age of the fetus had a small impact on the ability and accuracy of gender determination. When the crown-rump length was 45.0–56.0 mm, the accuracy was 78.4% compared to 92.0% when the crown-rump length was 67.8–83.0 mm (p < 0.01). If the gender assigned was male, then this was more likely to be correct (89.8%) than if the fetus was thought to be female (80.2%) (p < 0.01). This study demonstrated that gender assignment is possible at the 11–14 week scan in 82.4% of patients with an overall accuracy of 84.0%. Gestational age had a small impact on ability and accuracy of gender prediction.

Sonographic early fetal gender assignment: a longitudinal study in pregnancies after in vitro fertilization

A longitudinal evaluation by sonography of external genitalia in human embryos/early fetuses with a known time from fertilization is lacking. Our aim was to assign by sonographic evaluation of external genitalia the early fetal gender in a cohort of pregnancies after in vitro fertilization. Sonographic examinations were performed in each case in three subsequent sessions over a period of time early in gestation in order to establish a temporal threshold, expressed in terms of days from fertilization, at which absolute accuracy in gender prediction is achievable. Thirty-two fetuses were included in this prospective longitudinal study. Each was examined three times for gender assignment. The first observation was performed between 65 and 69 days from fertilization, the second between 70 and 74 days and the third between 75 and 79 days. Transvaginal and/or transabdominal sonography was used to detect the 'sagittal sign' as a marker of fetal gender. The results of ultrasound examinations were compared with gender at birth or with karyotype obtained from amniotic fluid cells or chorionic villus sampling. Fetal gender assignment was feasible in 29 out of 32 fetuses (90%) at the first examination and in all cases at the second and third examinations. Fetal gender prediction was correct in 76% of cases in which fetal gender was assigned (22/29) at the first examination; accuracy for males was 46% (6/13) and for females 100% (16/16). At the second and third examinations, accuracy for gender prediction achieved 100% for both genders. Concerning the temporal threshold, absolute accuracy in gender prediction was achieved at 69 days from fertilization, corresponding to 11+6 weeks based on the last menstrual period. This study provides important information about the earliest stage, expressed in terms of days from fertilization, at which it is possible to make a certain diagnosis of fetal gender by sonography.

Erythroid-specific antibodies enhance detection of fetal nucleated erythrocytes in maternal blood.

Fetal nucleated erythrocytes (NRBC) in maternal blood are a non-invasive source of fetal DNA for prenatal genetic screening. We compared the effectiveness of three monoclonal antibodies for the separation of fetal cells from maternal blood by flow sorting. Mononuclear blood cells from 49 healthy pregnant women were incubated with antibody to CD 71, CD 36, and/or glycophorin A (GPA), employed singly or in combination with each other. These monoclonal antibodies recognize surface antigens on haematopoietic precursor cells. Successful isolation of fetal cells was defined as detection of Y chromosomal sequences in maternal blood from women carrying male fetuses, with absence of Y sequences when female fetuses were carried. Thus, gender prediction accuracy was used as a measure of fetal cell separation. Using anti-CD 71 to isolate fetal cells, gender prediction was 57 per cent correct; with anti-CD 36, it was 88 per cent correct. Anti-GPA, an erythrocyte-specific antigen, used alone or in combination with anti-CD 71 or 36, improved gender prediction to 100 per cent. We conclude that antibody to GPA improves the retrieval of fetal NRBC from maternal blood, permitting genetic analysis by the polymerase chain reaction.