Automatic Grading System Research Articles

Accurate system for assessment and selection of human embryo after in vitro fertilization

In Vitro Fertilization (IVF) is a complex medical procedure designed to assist individuals in overcoming infertility by facilitating the union of sperm and egg outside the human body. Not all embryos created during the IVF process have the same potential for successful implantation and development. Embryo grading allows embryologists to assess the quality of embryos based on specific criteria, such as cell division, symmetry, and the presence of fragmentation. By assigning grades, they can identify the most viable embryos with the highest likelihood of successful implantation. The conventional methods for embryo grading are time-consuming, labor-intensive, and prone to errors due to their subjective nature. Therefore, there is a pressing need for the development of an accurate and automated grading system to address these challenges. In this study, I propose a human embryo grading system based on machine learning. The proposed system utilizes embryo images as inputs and produces probabilities to predict the success or failure of each embryo. I utilize a convolutional neural network to develop the grading model and introduce a novel hierarchical feature extraction approach to enhance accuracy. Through extensive experiments, I demonstrate that the proposed method surpasses previous approaches with a significant performance margin.

An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network.

Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice.

AI활용교육에 대한 중등 수학교사의 인식 분석

Objectives The purpose of this study is to determine the level of awareness of secondary school mathematics teachers on the use of AI in mathematics education and the use of AI in various stages of lesson design. Methods A survey was conducted among 126 math teachers working in secondary schools. To analyze the survey results, the frequency, mean value, and standard deviation of each question were analyzed. In addition, in-dependent samples t-test and one-way analysis of variance were conducted to check the significance of differ-ences in respondents' characteristics as needed. Results The main findings of the study are as follows First, the results of the chi-square test were used to de-termine the statistical significance of whether the respondents had experience in teaching using A.I. Second, the chi-square test was used to determine whether the respondents had experience in teaching using A.I. The results showed that there were statistically significant differences in age and teaching experience, and the higher the age and teaching experience, the more teachers who had experience in teaching using A.I.. Third, the items that mathematics teachers expect more help from A.I. in lesson design are “diagnosing various learning problems by visualizing learner data (M=4.47),” “analyzing learning data such as learners' learning styles and learning patterns (M=4.4),” and “providing learning materials to overcome learning deficiencies (M=4.37)”. Fourth, when analyzing the perceptions of using AI in each stage of lesson design according to individual characteristics, the female mean (M=4.16) of ‘lesson design and development stage’ was higher than the male mean (M=3.88), and the difference was statistically significant. Conclusions Based on the results of the analysis, the following implications can be drawn for school math classes and lesson design activities. First, it is necessary to develop the professionalism of secondary math teachers so that math education using AI can be introduced and actively applied in the school field. Second, it is necessary to develop and disseminate teaching materials utilizing AI so that math education using AI can be activated. Third, research on AI-based math teaching and learning platforms is needed so that AI can be utilized in each stage of lesson design. Fourth, research and development of automatic grading systems utilizing AI technology should be conducted to support math education.

Research on automatic biomass grading and quality assessment technology for tobacco industry based on deep convolutional neural network

Abstract Automatic tobacco grading systems can promote the standardization and unification of tobacco acquisition activities, improve production efficiency, and promote the long-term stable development of this industry. In this paper, we first extract the shape and color features of tobacco leaf structure, use a support vector machine to determine the difficult samples, add the difficult samples to the training set, and use the deep convolutional neural network method to assign initial values to the model, which reduces the occurrence of a model overfitting phenomenon and improves the prediction accuracy. Constructing a convolutional neural network based on EfficientNetV2-S for an automatic tobacco grading model. The performance of the model was tested on HSV and RGB images, and finally, automatic tobacco grading was applied to two batches of high-quality and good tobacco. It is found that the accuracy of the model training set and test set of this paper is 0.077 and 0.094 higher than that of the VGG16 model on HSV and RGB images, respectively, and there is no upward trend in the loss function. The model’s training operation time for grading two batches of tobacco is 27.56 seconds, which is more efficient. The accuracy rate of the two batches of tobacco is higher than 90%. The model in this paper in the practical application performance in addition to a higher degree of grading efficiency and accuracy, which is stronger than the manual identification in terms of efficiency and accuracy recognition stability. This paper’s research offers a feasible path and useful exploration to effectively improve production efficiency and optimize the process in China’s tobacco industry.

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification

Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help predict the disease progression and prognosis. However, the lack of precise detection and classification tools has created an unmet medical need, underscoring the importance of exploring the clinical utility of FT. Thus, to address this gap, we introduce an automatic FT grading system (called DeepGraFT) using classification-and-segmentation co-decision models by deep learning. ConvNeXt, utilizing transfer learning from pretrained ImageNet weights, was employed for the classification algorithm, aligning with a region of interest based on the ETDRS grading system to boost performance. A segmentation model was developed to detect FT exits, complementing the classification for improved grading accuracy. The training set of DeepGraFT was from our in-house cohort (MAGIC), and the validation sets consisted of the rest part of in-house cohort and an independent public cohort (UK Biobank). DeepGraFT demonstrated a high performance in the training stage and achieved an impressive accuracy in validation phase (in-house cohort: 86.85 %; public cohort: 81.50 %). Furthermore, our findings demonstrated that DeepGraFT surpasses machine learning-based classification models in FT classification, achieving a 5.57 % increase in accuracy. Ablation analysis revealed that the introduced modules significantly enhanced classification effectiveness and elevated accuracy from 79.85 % to 86.85 %. Further analysis using the results provided by DeepGraFT unveiled a significant negative association between FT and spherical equivalent (SE) in the UK Biobank cohort. In conclusion, DeepGraFT accentuates potential benefits of the deep learning model in automating the grading of FT and allows for potential utility as a clinical-decision support tool for predicting progression of pathological myopia.

Comparison of Different AIED Models and Evaluation Methods

In the educational field, although machine which attempts to learn AI is still in their early stages, the approach has yet to show remarkable results when facing complex challenges without obvious cut-off points, such as grading students’ papers or exploring enormous and complicated data collections. AI can also be used to create virtual learning environments, intelligent testing systems, and automated grading systems. AI in educational fields refers to the application of AI technology to enhance and support the studying processes, such as tracking students’ behavior and constructing models that can accurately hypothesize students’ achievements. It can include the use of AI-powered tutoring systems, personalized learning platforms, and data analysis tools that can help teachers and administrators better understand student needs and progress. This paper mainly concentrates on the field of artificial intelligence tutoring and summarizes the methods by which intelligent tutors assess student performance by comparing some student models and the input, output, and model forms of methods for evaluating fine-grained interactions of intelligent tutors. This article also provides basic information and new perspectives for studying which methods to use to model and evaluate tutorial learning.

Automatic Short Answer Grading System Based on Semantic Networks and Support Vector Machine

In education, exams are used to asses students’ acquired knowledge; however, the manual assessment of exams consumes a lot of teachers’ time and effort. In addition, educational institutions recently leaned toward distance education and e-learning due the Coronavirus pandemic. Thus, they needed to conduct exams electronically, which requires an automated assessment system. Although it is easy to develop an automated assessment system for objective questions. However, subjective questions require answers comprised of free text and are harder to automatically assess since grading them needs to semantically compare the students’ answers with the correct ones. In this paper, we present an automatic short answer grading method by measuring the semantic similarity between the students answer and the correct answer. A semantic network was built to represent the relationship between the words of the two texts to calculate semantic similarity. Representing a text as a semantic network is the best knowledge representation that comes closest to human comprehension of the text, where the semantic network reflects the sentence's semantic, syntactical, and structural knowledge. Several features were extracted from the semantic network and used as input to train the support vector machine (SVM) model to predict the degree of the targeted semantic similarity. The proposed method was tested on the Mohler dataset that is publicly available online. The obtained results were evaluated and reported in terms of Pearson correlation and root mean squared error (RMSE) where it achieved 0.63 and 0.83 respectively. The proposed method outperformed all previous methods on the used dataset.

An Automated Grading System Based on Topological Features for the Evaluation of Corneal Fluorescein Staining in Dry Eye Disease

Background: Corneal fluorescein staining is a key biomarker in evaluating dry eye disease. However, subjective scales of corneal fluorescein staining are lacking in consistency and increase the difficulties of an accurate diagnosis for clinicians. This study aimed to propose an automatic machine learning-based method for corneal fluorescein staining evaluation by utilizing prior information about the spatial connection and distribution of the staining region. Methods: We proposed an end-to-end automatic machine learning-based classification model that consists of staining region identification, feature signature construction, and machine learning-based classification, which fully scrutinizes the multiscale topological features together with conventional texture and morphological features. The proposed model was evaluated using retrospective data from Beijing Tongren Hospital. Two masked ophthalmologists scored images independently using the Sjögren’s International Collaborative Clinical Alliance Ocular Staining Score scale. Results: A total of 382 images were enrolled in the study. A signature with six topological features, two textural features, and two morphological features was constructed after feature extraction and selection. Support vector machines showed the best classification performance (accuracy: 82.67%, area under the curve: 96.59%) with the designed signature. Meanwhile, topological features contributed more to the classification, compared with other features. According to the distribution and correlation with features and scores, topological features performed better than others. Conclusions: An automatic machine learning-based method was advanced for corneal fluorescein staining evaluation. The topological features in presenting the spatial connectivity and distribution of staining regions are essential for an efficient corneal fluorescein staining evaluation. This result implies the clinical application of topological features in dry-eye diagnosis and therapeutic effect evaluation.

Software System for Automatic Grading of Paper Tests

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.

Domain and Histopathology Adaptations–Based Classification for Malignancy Grading System

Accurate proliferation rate quantification can be used to devise an appropriate treatment for breast cancer. Pathologists use breast tissue biopsy glass slides stained with hematoxylin and eosin to obtain grading information. However, this manual evaluation may lead to high costs and be ineffective because diagnosis depends on the facility and the pathologists' insights and experiences. Convolutional neural network acts as a computer-based observer to improve clinicians' capacity in grading breast cancer. Therefore, this study proposes a novel scheme for automatic breast cancer malignancy grading from invasive ductal carcinoma. The proposed classifiers implement multistage transfer learning incorporating domain and histopathologic transformations. Domain adaptation using pretrained models, such as InceptionResNetV2, InceptionV3, NASNet-Large, ResNet50, ResNet101, VGG19, and Xception, was applied to classify the ×40 magnification BreaKHis data set into eight classes. Subsequently, InceptionV3 and Xception, which contain the domain and histopathology pretrained weights, were determined to be the best for this study and used to categorize the Databiox database into grades 1, 2, or 3. To provide a comprehensive report, this study offered a patchless automated grading system for magnification-dependent and magnification-independent classifications. With an overall accuracy (means ±SD) of 90.17%±3.08% to 97.67%±1.09% and an F1 score of 0.9013 to 0.9760 for magnification-dependent classification, the classifiers in this work achieved outstanding performance. The proposed approach could be used for breast cancer grading systems in clinical settings.

Automated Grading System for Breast Cancer Histopathological Images Using Histogram of Oriented Gradients (HOG) Algorithm

Breast cancer is the most common type of cancer in the world, affecting both men and women. In 2023, the American Cancer Society's reported that there will be approximately 297,800 new cases of invasive breast cancer in women and 2,850 in men, along with 55,750 cases of ductal carcinoma in situ (DCIS) in women. Further, an estimated 43,750 deaths are expected from breast cancer, of which approximately 43,180 are among women and 570 are among men. In this paper, we propose an automated grading system for breast cancer based on tumor's histopathological images using a combination of the Histogram of Oriented Gradients (HOG) for feature extraction and machine learning algorithms. The proposed system has four main phases: image preprocessing and segmentation, feature extraction, classification, and integration with a website. Grayscale conversion, enhancement, noise and artifact removal methods are used during the image preprocessing stage. Then the image is segment during the segmentation phase to extract regions of interest. And then, features are extracted from the obtained region of interest using the Histogram of Oriented Gradients (HOG) algorithm. The next, the images are classified into three distinct breast cancer grades based on the extracted features using machine learning algorithms. Moreover, the effectiveness of the proposed system was evaluated and reported using vary evaluation methods and the results showed a remarkable accuracy of up to 97% by the SVM classifier. Finally, the machine learning model is integrated into a website to improve the detection and diagnosis of breast cancer disease and facilitate the access and use of patient data. This will make the work easier for physicians to enhance breast cancer detection and treatment

Cheating Automatic Short Answer Grading with the Adversarial Usage of Adjectives and Adverbs

Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However, for short answer formats, automatic grading is challenging due to natural language ambiguity and versatility. While automatic short answer grading models are beginning to compare to human performance on some datasets, their robustness, especially to adversarially manipulated data, is questionable. Exploitable vulnerabilities in grading models can have far-reaching consequences ranging from cheating students receiving undeserved credit to undermining automatic grading altogether—even when most predictions are valid. In this paper, we devise a black-box adversarial attack tailored to the educational short answer grading scenario to investigate the grading models’ robustness. In our attack, we insert adjectives and adverbs into natural places of incorrect student answers, fooling the model into predicting them as correct. We observed a loss of prediction accuracy between 10 and 22 percentage points using the state-of-the-art models BERT and T5. While our attack made answers appear less natural to humans in our experiments, it did not significantly increase the graders’ suspicions of cheating. Based on our experiments, we provide recommendations for utilizing automatic grading systems more safely in practice.

Literature Review of Automated Grading Systems Utilizing MRI for Neuroforaminal Stenosis.

Cervical neural foraminal stenosis is a common and debilitating condition affecting people between the ages 40-60. Although it is established that MRI is the best method of scanning the neural foramen, the question remains whether there is a role for three-dimensional MRIs and whether it is possible to develop a computer-aided automated grading system to establish the degree of clinically relevant cervical foraminal stenosis. The study's objective is to conduct a literature review of existing or recently developed automated grading systems for the cervical neural foramen, including volumetric MRI evaluations of the foramen. A systematic search of Cochrane Library, Cochrane Clinical Trials, Ovid MEDLINE, EMBASE, CINAHL, ACM Digital Library and Institute of Electrical and Electronics Engineers (IEEE), and Web of Science was performed for reports examining automated systems and volumetric scanning foraminal stenosis published before 31.07.2021. 3971 articles were identified of which 8 were included in the study. The automated grading systems of the neural foramen focus largely on the lumbar spine with elements that may be applicable to the cervical spine. Although there are established studies on the automated grading of the lumbar spine, it is uncertain whether any of these are reproducible in the cervical spine. Visual grading systems for the cervical spine demonstrate good inter-reader reliability between radiologists and clinicians. The Park visual grading method shows strong inter-reader reliability across radiologists and clinicians despite the limited data on the correlation with neurological symptoms or surgical outcome. There is scope for further development of an automated grading system for cervical foraminal stenosis to improve the speed and consistency of image interpretation.

Implementation of an Automated Grading System for Formalized Answer Sheet Using EasyOCR

Implementation of an Automated Grading System for Formalized Answer Sheet Using EasyOCR

Evaluation of Submission Limits and Regression Penalties to Improve Student Behavior with Automatic Assessment Systems

Objectives . Automatic assessment systems are widely used to provide rapid feedback for students and reduce grading time. Despite the benefits of increased efficiency and improved pedagogical outcomes, an ongoing challenge is mitigating poor student behaviors when interacting with automatic assessment systems including numerous submissions, trial-and-error, and relying on marking feedback for problem solving. These behaviors negatively affect student learning as well as have significant impact on system resources. This research quantitatively examines how utilizing submission policies such as limiting the number of submissions and applying regression penalties can reduce negative student behaviors. The hypothesis is that both submission policies will have a significant impact on student behavior and reduce both the number of submissions and regressions in student performance. The research questions evaluate the impact on student behavior, determine which submission policy is the most effective, and what submission policy is preferred by students. Participants . The study involved two course sections in two different semesters consisting of a total of 224 students at the University of British Columbia, a research-intensive university. The students were evaluated using an automated assessment system in a large third year database course. Study Methods . The two course sections used an automated assessment system for constructing database design diagrams for assignments and exams. The first section had no limits on the number of submissions for both assignments and exams. The second section had limits for the exams but no limits on assignments. On the midterm, participants were randomly assigned to have either a restriction on the total number of submissions or unlimited submissions but with regression penalties if a graded answer was lower than a previous submission. On the final exam, students were given the option of selecting their submission policy. Student academic performance and submission profiles were compared between the course sections and the different submission policies. Findings. Unrestricted use of automatic grading systems results in high occurrence of undesirable student behavior including trial-and-error guessing and reduced time between submissions without sufficient independent thought. Both submission policies of limiting maximum submissions and utilizing regression penalties significantly reduce these behaviors by up to 85%. Overall, students prefer maximum submission limits, and demonstrate improved behavior and educational outcomes. Conclusions . Automated assessment systems when used for larger problems related to design and programming have benefits when deployed with submission restrictions (maximum attempts or regression penalty) for both improved student learning behaviors and to reduce the computational costs for the system. This is especially important for summative assessment but reasonable limits for formative assessments are also valuable.

Proving and Disproving Equivalence of Functional Programming Assignments

We present an automated approach to verify the correctness of programming assignments, such as the ones that arise in a functional programming course. Our approach takes as input student submissions and reference solutions, and uses equivalence checking to automatically prove or disprove correctness of each submission. To be effective in the context of a real-world programming course, an automated grading system must be both robust, to support programs written in a variety of style, and scalable, to treat hundreds of submissions at once. We achieve robustness by handling recursion using functional induction and by handling auxiliary functions using function call matching. We achieve scalability using a clustering algorithm that leverages the transitivity of equivalence to discover intermediate reference solutions among student submissions. We implement our approach on top of the Stainless verification system, to support equivalence checking of Scala programs. We evaluate our system and its components on over 4000 programs drawn from a functional programming course and from the program equivalence checking literature; this is the largest such evaluation to date. We show that our system is capable of proving program correctness by generating inductive equivalence proofs, and providing counterexamples for incorrect programs, with a high success rate.

Novel Framework for Improving the Correctness of Reference Answers to Enhance Results of ASAG Systems.

Usage of online learning platforms increases day by day and henceforth, there emerges the need for automated grading systems to assess the learner's performance. Evaluating these answers demands for a well-grounded reference answer which aids a strong foundation for better grading. Since reference answers impacts the exactness of grading answers of learners, its correctness remains a great concern. A framework that addresses the reference answer exactness in Automated Short Answer Grading (ASAG) systems was developed. This framework includes material content acquisition, clustering collective content, expert answer as key components which was later fed to a zero-shot classifier for a strong reference answer generation. Then, the computed reference answers along with student answers and questions from Mohler dataset were fed to an ensemble of transformers to produce relevant grades. The aforementioned models' RMSE and correlation values were compared against the past values of the dataset. Based on the observations made, this model outperforms the previous approaches.

Leveraging Big Data for Educational Improvement: Opportunities, Challenges, and Future Directions

Big Data analysis in education has the potential to enhance student academic performance by providing personalized learning experiences. By collecting and analyzing student behaviour and performance data, educators can identify areas where students are struggling and develop targeted interventions to help them improve their learning outcomes. For instance, educators can use big data to identify each student’s learning style and tailor their teaching methodology to suit their needs. This personalized approach can enhance student engagement and motivation, leading to improved academic performance. Big Data can also be used to improve the grading system by eliminating manual grading errors and providing objective and accurate grading based on data analysis. Using automated grading systems allows educators to save time and reduce grading bias, leading to fairer and more consistent grading practices. This can also help educators identify areas where students need additional support, and provide targeted feedback to help students improve their performance. Big Data can also be used to reduce dropout rates by providing early warning indicators to identify students who are at risk of dropping out. By analyzing data on attendance, grades, and other factors, educators can identify students who may be struggling and provide targeted support to help them succeed. This can help reduce dropout rates and improve student retention rates. Overall, the use of Big Data analysis in education has numerous benefits, including enhancing student academic performance, improving grading practices, increasing student engagement and motivation, and reducing dropout rates. However, it is important to ensure that student privacy and data security are prioritized in the use of Big Data in education. By taking a responsible and ethical approach, educators and institutions can harness the power of Big Data to improve the quality of education and prepare students for success in the future

Fruit Grading System

Fruit grading systems are critical for ensuring the quality, consistency, and efficiency of fruit production and distribution. These systems utilize advanced technologies such as computer vision and automation to accurately identify and classify fruit based on various quality parameters such as size, colour, and blemishes. The use of automated grading systems reduces the need for manual labour, increases throughput, and improves accuracy by minimizing human error. This results in a faster and more efficient grading process that can handle large volumes of fruit quickly and consistently, reducing the time and cost of fruit grading. Additionally, fruit grading systems play a crucial role in reducing waste and promoting sustainability in the fruit industry. By accurately grading fruit, less high-quality fruit goes to waste, and resources are used more efficiently. Grading systems can also identify opportunities for optimization, such as reducing packaging waste or identifying areas for process improvement. Overall, fruit grading systems are essential for ensuring high-quality fruit that meets the demands of the industry while also promoting sustainability and efficient distribution. Key Words: Object Detection, Computer Vision, Machine Learning, Deep Learning, Fruit Grading, TensorFlow.

Objective and automatic grading system of facial signs from smartphones' pictures in South African men: Validation versus dermatologists and characterization of changes with age.

To evaluate the capacity of the automatic detection system to accurately grade, from selfie pictures, the severity of eight facial signs in South African men. Selfie pictures (obtained from frontal and back cameras) of 281 South African men differently aged (20-70years) were obtained and analyzed by an automatic artificial intelligence (AI)-based automatic grading system. Data were compared with the clinical gradings made by experts and dermatologists. In all facial signs, both series of gradings were found highly correlated with, however, different coefficients (0.59-0.95), those of marionette lines and cheek pores being of lower values. No differences were observed between data obtained by frontal and back cameras. With age, in most cases, gradings show up to the 50-59year age-class, linear-like changes. When compared to men of other ancestries, South African men present lower wrinkles/texture, pigmentation, and ptosis/sagging scores till 50-59years, albeit not much different in the cheek pores sign. The early onset (mean age) of visibility of wrinkles/texture for South African men were (i.e., reaching grade >1) 39 and 45years for ptosis/sagging. This study completes and enlarges the previous works conducted on men of other ancestries by showing some South African specificities and slight differences with men of comparable phototypes (Afro American).