Hair Counting Research Articles

Efficacy and Pain Tolerance of Alexandrite Laser Hair Removal at Different Stages of the Menstrual Cycle.

Several different parameters play a role in the transition of hair follicles to the anagen phase, with the role of androgens, progesterone, and estrogen hormones and receptors being significant. The effectiveness of laser hair removal (LHR) and pain tolerance during procedure were investigated during 3 different phases of the menstrual cycle. Forty-eight axillae were randomly divided into 3 groups: menstruation, ovulation, and luteal. Three laser sessions were performed on each axilla at a 1-month interval. Blood hormone levels were measured in the patients. An alexandrite laser was applied during LHR sessions. Before each LHR session and 1 month after the third session, hair follicles in 4-cm2 areas in the center of the axillae were counted. Patients self-assessed the pain they felt during the laser application in each session with a visual pain scale. The average values for hair counting in the groups were as follows (M, menstruation; O, ovulation; L, luteinization): M0 = 47.6, M1 = 27.4, M2 = 16.1, M3 = 9.9; O0 = 41.8, O1 = 21.1, O2 = 13.8, O3 = 8.6; and L0 = 49.4, L1 = 27.1, L2 = 15.1, L3 = 9.8. The average values on the visual analog scale scores in the groups were: M1 = 3.94, M2 = 3.06, M3 = 1.94; O1 = 3.50, O2 = 3.06, O3 = 1.69; and L1 = 3.63, L2 = 2.50, L3 = 1.56. Statistical analysis was conducted with Tukey post hoc analysis after analysis of variance. The results of LHR are not affected by changes in hormone levels during the menstrual cycle in females. Although not statistically significant, it has been observed that pain tolerance during laser application is lower during the menstruation cycle.

Comparison Between Trichoscopic and Histopathological Evaluations of Hair Parameters.

BackgroundCollecting data on hair counts helps dermatologists diagnose hair diseases more accurately. Quantitative trichoscopy analysis and pathologic examination are two common ways to evaluate hair parameters.PurposeThe study aims to compare the effect of quantitative trichoscopy analysis and pathologic examination in hair counting and quantify the hair density of average Chinese people.MethodsTrichoscopy was performed in four scalp regions with a total of twenty-three volunteers without alopecia: frontal, vertex, occipital, and parietal. Hair density parameters were recorded. A 4-mm punch biopsy was performed in the occipital area, and all specimens were transversely sectioned serially and observed to calculate the hair densities.ResultsThe average hair density, hairs per follicle unit, and vellus hair ratio from quantitative trichoscopy analysis in the occipital region were 163.07±28.17/cm2, 1.87±0.25 and 6.60±3.95%, lower than those from biopsy significantly (214.97±48.73/cm2, 2.24±0.30 and 10.48±6.43%). The hair shaft diameter measured by quantitative trichoscopy analysis was 74.52±8.02μm, higher than that by histopathologic examination (68.65±8.00μm) (p<0.05).ConclusionQuantitative trichoscopy is a noninvasive, quick, and convenient way to evaluate hair density. Histopathologic examination is an invasive way but provides more accurate data. The data obtained from trichoscopy and pathological examination are different to some extent, which cannot be substituted entirely for each other. This study established the reference for hair density in the adult Chinese population.

Triple Wavelength and 810 nm Diode Lasers for Hair Removal: A Clinical and &amp;lt;i&amp;gt;in Silico&amp;lt;/i&amp;gt; Comparative Study on Indian Skin

In laser hair removal treatments on dark skin, the high concentration of melanin in the skin competes with the melanin in the hair. During standard laser procedures, with wavelengths of 755 nm or 810 nm, a high level of laser light absorption by melanin in the skin is observed. Therefore, to avoid side effects, lower fluence values are used, which further reduces hair-removal efficacy. To improve results, 810 nm diode lasers operating in dynamic mode, with high frequency and multiple passes, are typically used. The aim of this study is to compare the efficacy and safety of triple-wavelength diode lasers (810 nm, 940 nm, 1060 nm) with that of 810 nm diode lasers on Indian patients. A side-by-side comparison was performed using a triple-wavelength diode laser in stamping mode on one side, and an 810 nm diode laser in dynamic mode on the other. Three subjects with skin type IV on the Fitzpatrick scale participated in the study. Efficacy was assessed through hair counting using clinical photographs, taken before and after the treatments, and the Global Aesthetic Improvement Scale (GAIS). Additionally, comparisons related to epidermal heating and thermal damage to the hair follicle were conducted through mathematical 3D simulations using COMSOL Multiphysics® software. Side effects were also evaluated. A superior end point was observed with triple wavelength compared to the 810 nm diode laser. Hair counting showed a 27% greater hair reduction with triple wavelength. No adverse effects were observed. Thermal simulations revealed 29% higher thermal damage with the triple-wavelength laser compared to the 810 nm diode laser. To conclude, on darker skin types, the triple-wavelength diode laser has been shown to be more effective at removing hair, compared to the 810 nm diode laser, while also being a safe procedure.

Development and qualification of a machine learning algorithm for automated hair counting.

Determining the amount of hair on the scalp has always been an important metric of patient satisfaction for hair growth and hair retention technologies. While simple in concept, this measurement is a difficult, resource intensive task for the dermatologist and the research scientist. Specifically, counting and measuring hair in phototrichogram images is very time consuming and labour intensive. Due to cost, often only a fraction of available images is manually analysed. There is a need for an automated method that can significantly increase speed and throughput while reducing the cost of counting and measuring hair in phototrichogram images. Recent advances in machine learning and deep convolutional neural networks (deep learning) have led to a revolution in the analysis of image, video, speech, text and other sensor data. Image diagnostics have seen remarkable improvements with completely automated methods outperforming both human experts and human-engineered analysis methods. Deep learning methods can also provide speed and cost benefits. To enable use of a deep learning, we created a data set of 288manually annotated phototrichogram images with marked location and length of each hair (the training dataset). We designed a custom neural network architecture and custom image processing algorithms to best utilize the available training data and to maximize performance for hair counting and length measurement. The performance of the algorithm was qualified by comparing hair count and length measurements to an independent ground truth method, the semi-manual Canfield's Hair Metrix method. Leveraging deep neural networks, we have developed capability to apply machine learning to reduce the time needed to acquire data from phototrichograms of patients' scalp from months to seconds. Our algorithm enables fast and fully automated hair counting and length measurement. The algorithm shows high agreement with human manually assisted analysis (ground truth). We have trained and deployed an algorithm utilizing this technology and have demonstrated the reproducibility, accuracy and speed of this algorithm that, once deployed, requires little to no recurring cost or manual intervention for its operation. The method allows fast analysis of large number of images, reducing study cost and significantly reducing study analysis time.

Hair counting method based on image processing technology

The number of hair per unit area of scalp is an important indicator of hair growth. In order to realize the understanding of head fur growing condition, this paper designs a hair counting method based on image processing technology. The color characteristics of the high definition scalp hair images taken by light microscope were analyzed and the Wright test was used to eliminate the shadow and subtle hair interference. Then the original image was preprocessed and pieceby-linear transformation was enhanced, and then the threshold segmentation was performed to extract the hair root image, and a single scalp hair image was counted, and the scalp hair counting function model was constructed to realize the scalp hair counting in the whole region. Analysis shows that the experimental results accord with the physiological characteristics of hair growth, and the method can avoid most of the noise interference of hair image, and meet the actual requirements of scalp hair counting.

An Unsupervised Hair Segmentation and Counting System in Microscopy Images

This paper focuses on the development of medical software for clinical applications using advanced image processing algorithms. Three critical issues of hair segmentation and counting are addressed in this paper. First, the removal of any bright spots due to oil or moisture, which generate circular patterns in the middle of the hair and significantly affect the accuracy of determining the line. Second, two contacting or overlapping hairs are recognized and counted as a single hair. To solve this problem, we proposed a hair-bundling algorithm to calculate any concealed hairs. Finally, hairs may be wavy or curly, making the conventional Hough-based line detection algorithm unsuitable, since it suffers from parameter selections, such as the minimum length of line segment, and distance between line segments. Our proposed hair counting algorithm is substantially more accurate than the Hough-based one, and robust to curls, oily scalp, noise-corruption, and overlapping hairs, under various white balance.

Reliability and Validity of a Photographic Method for Measuring Facial Hair Density in Men

Many studies have investigated hair removal or growth prevention treatments, but they often measure hair density using noninvasive methods that are subjective and qualitative.1 Although photographic and digital hair-counting methods have been used, their reliability and validity remain unknown.2 We describe a simple, noninvasive method of hair counting used in a hair growth prevention treatment trial and assess its reliability and validity.

Automated body hair counting and length measurement

Hair loss or hair excess is a common condition. There is a growing need to quantitatively assess the success of interventions aimed at replenishing areas that lack hair or at removing hair from areas such as the back, the legs, or the arms. Non-invasive methods that do not require staining are highly desirable because the staining process itself may affect the efficacy of the treatment. We introduce a system based on a flatbed scanner and on novel and sensitive image analysis algorithms to count the number of hairs and their individual length. Additionally, a measure of hair visibility is introduced, which allows assessing objectively the severity of the condition. Our system is able to detect even hairs that are difficult to see to a human observer. It is robust to skin impurities or variations in the skin texture and colour. Scanner imaging ensures a sharp image over the whole field. The system analyses on the order of two images per minute, making it suitable for large clinical studies. Counts delivered by a human counter vs. the software were within 10% of each other (N=12). Based on our results, we expect that the software will be useful to a number of researchers investigating medical and cosmetic issues involving objective assessment of pilosity. The algorithm itself may be of use for other applications.

Comparison of Long-Pulsed Alexandrite and Nd:YAG Lasers, Individually and in Combination, for Leg Hair Reduction

To compare the long-term effectiveness and safety of long-pulsed Nd:YAG and alexandrite lasers, individually and in combination, in long-term leg hair reduction. Randomized, single-center, within-participant, investigator-blinded, active-controlled clinical trial. Private skin laser center. Twenty individuals aged 16 to 50 years with skin phototypes III and IV. The medial and lateral sides of each participant's legs were randomly assigned to receive 1 of the following laser treatments: (1) long-pulsed 1064-nm Nd:YAG laser (12-mm spot size); (2) long-pulsed 755-nm alexandrite laser (12-mm spot size); (3) long-pulsed 755-nm alexandrite laser (18-mm spot size); and (4) a combination of long-pulsed 1064-nm Nd:YAG laser and long-pulsed 755-nm alexandrite laser (treatments 1 and 2). Identified areas were treated for a total of 4 sessions at 8-week intervals. Hair reduction from baseline based on hair counting with digital photography by 2 blinded assessors, 8 and 18 months after the last treatment session. Fifteen participants completed the trial. The mean (SD) hair reduction 18 months after the last treatment, as measured by the assessors from digital photographs, were 75.9% (19.0%) for the 12-mm spot size alexandrite laser, 84.3% (12.4%) for the 18-mm spot size alexandrite laser, 73.6% (11.4%) for the Nd:YAG laser, and 77.8% (15.9%) for the combination therapy (analysis of variance, P > .05). The incidence of adverse effects (hyperpigmentation) and pain severity were significantly greater in areas that received combination therapy (P = .001). After 18 months of follow-up, alexandrite and Nd:YAG lasers were efficacious for leg hair removal. Combination therapy did not have any additional benefit and caused more adverse effects.

Hair removal using an 800‐nm Diode Laser: Comparison at different treatment intervals of 45, 60, and 90 days

Some laser irradiation parameters such as wavelength, fluence, pulse duration, and spot size have been shown to influence the damage of any target inside the skin, however, the role of some patients' factors such as hair growth cycle is still under debate. To determine the association of treatment interval and laser treatment outcome. In a retrospective chart review of 176 patients undergoing laser-assisted hair removal with a diode laser 24 patients were selected. All accepted to cease the therapy, and be followed-up for 5 months. At the end of the study the patients were questioned about the efficacy of the treatment as well as the adverse effects. Hair counting was also performed. The adverse effects (pain, blister or erosion, hyperpigmentation, hypopigmentation, and folliculitis) were questioned during the follow-up period. The mean hair reduction was 78.1%, 45.8%, and 28.7% in 45, 60, and 90-day interval groups, respectively (P < 0.0001). The treatment interval was related to the treatment outcome in our study.

Natural history of androgenetic alopecia

Twenty-two men with patterns III-Va androgenetic alopecia were entered into a 10-month study aimed at establishing information on the natural progression of hair loss over a period of time typical of studies of hair growth promoters. The methodology employed was the same as that in published clinical trials of topical minoxidil, but the men refrained from application of either active drug or vehicle to their scalps. As one of the potential explanations for the observed 'placebo-effect' seen in non-vellus hair counts in the topical minoxidil trials was a learning curve of novice hair counters, we were particularly interested in evaluating this in our 'no-treatment' trial. To that end, both a novice (Observer I) and an experienced (Observer II) hair counter independently performed the hair counts. There was a mean decline in the number of vertex target area non-vellus hairs (-17.2 +/- 80.3 for Observer I and -26.6 +/- 63.5 for Observer II) at the end of 10 months; this was not significant. The novice's hair counts were lower than the experienced observer's counts at baseline, and the difference remained relatively constant during the study. Without the application of a placebo, there was no increase in hair growth, making it unlikely that the methods of hair counting led to the 'placebo-effect' seen in prior topical minoxidil studies.