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

Machine learning methods for determining skin age: A systematic review

AIM

This systematic review explores how machine learning is used in determining skin aging, aiming to evaluate accuracy, limitations, and gaps in the current literature.

MATERIALS AND METHODS

OVID Embase, OVID Medline, IEEE Xplore, and ACM Digitial Library were searched from inception to March 16, 2024.

RESULTS

A total of 1467 non-duplicate articles were screened, and 27 were ultimately included in the systematic review. The machine learning models exhibited a range of accuracies from a mean absolute error of 2.30-8.16 years. The most common approach was full facial image analysis, followed by non-image-based studies utilizing biomarkers such as the methylome and the proteome. The incorporation of dynamic facial expressions in the analysis was shown to improve the accuracy of age estimation, with a mean absolute error of 3.74. Confocal microscopy demonstrated potential for accurate skin aging estimation, with some studies achieving up to 85 % accuracy. Many studies were found with high PROBAST risk of bias scores, most commonly due to small sample sizes.

CONCLUSION

Future studies should aim for greater diversity in ethnicity and variables within datasets to improve generalizability.

Facial adult female acne in China: An analysis based on artificial intelligence over one million

BACKGROUND

To further clarify the acne profile of Chinese adult women, we included 1,156,703 adult women. An artificial intelligence algorithm was used to analyze images taken by high-resolution mobile phones to further explore acne levels in Chinese adult women.

METHOD

In this study, we assessed the severity of acne by evaluating patients' selfies through a smartphone application. Furthermore, we gathered basic user information through a questionnaire, including details such as age, gender, skin sensitivity, and dietary habits.

RESULTS

This study showed a gradual decrease in acne severity from the age of 25 years. A trough was reached between the ages of 40 and 44, followed by a gradual increase in acne severity. In terms of skin problems and acne severity, we have found that oily skin, hypersensitive skin, frequent makeup application and unhealthy dietary habits can affect the severity of acne. For environment and acne severity, we observed that developed city levels, cold seasons and high altitude and strong radiation affect acne severity in adult women. For the results of the AI analyses, the severity of blackheads, pores, dark circles and skin roughness were positively associated with acne severity in adult women.

CONCLUSIONS

AI analysis of high-res phone images in Chinese adult women reveals acne severity trends. Severity decreases after 25, hits a low at 40-44, then gradually rises. Skin type, sensitivity, makeup, diet, urbanization, seasons, altitude, and radiation impact acne. Blackheads, pores, dark circles, and skin roughness are linked to acne severity. These findings inform personalized skincare and public health strategies for adult women.

Artificial intelligence analysis of over a million Chinese men and women reveals level of dark circle in the facial skin aging process

BACKGROUND

To better compare the progression of dark circles and the aging process in Chinese skin. A total of 100 589 Chinese males and 1 838 997 Chinese females aged 18 to 85, without facial skin conditions, and who had access to a smartphone with a high-resolution camera all took selfies.

METHOD

Using a smartphone application with a built-in artificial intelligence algorithm, facial skin diagnostic evaluated the selfies and score the severity of the dark circles with four other facial indicators (including skin type, Pores, Acne vulgaris, and Blackheads). Basic information was collected with online questionnaire, including their age, gender, skin sensitivity, and dietary habits.

RESULTS

In users between the age of 18 and 59, the prevalence of comprehensive, pigmented, and structural type of dark circles all rose with age. However, between the age of 60 and 85, the intensity of all types of dark circles diminished. Besides, vascular dark circles progressively worsen from the age of 18 to their peak at 39, and then gradually decline with age. Females typically have more pronounced black circles under their eyes than males in China. Bad eating habits, urbanization, regular cosmetics use, and sensitive skin positively correlate with severe dark circles. Vascular, comprehensive dark circles were worse in spring. Both pigmented and structural dark circles were worse in the summer. The results indicated that the intensity of dark circles was influenced by oily skin, wide pores, severe blackheads, and severe acne.

CONCLUSIONS

Chinese men and women differed noticeably in the prevalence of each face aging indicator and the appearance of aging dark circles. Selfies could be automatically graded and examined by artificial intelligence, which is a quick and private method for quantifying signs of facial aging and identifying major problems for different populations. Artificial intelligence would assist in the development of individualized preventive and therapeutic interventions.