Background:Hair shaft diameter is one of the most important factors for the outcome of follicular unit extraction (FUE) surgery. In fact, the hair shaft is elliptical. Therefore, it has a long and short axis. Many hair transplantation surgeons use manual micrometer caliper for gauging hair diameter and use the results in the management of recipient and donor area. Aim:In this study, we aimed to identify the dependability of micrometer caliper and also the hair diameter diversity pattern in the donor area.Patients/Methods: Two hundred and seventy hairs were collected from three males with androgenetic alopecia. Hair samples were obtained from the 1 cm 2 boxes from superior to inferior at the mid-point of temporal, parietal, and occipital donor areas.The diameter of each hair was measured both with a micrometer caliper and scanning electron microscopy (SEM).Results: Average diameter measured by scanning electron microscopy was 83.01 µm for the long axis and 51.51 µm for the short axis. The average value for the micrometer caliper measurement was 53.32 µm. Comparison of micrometer caliper results with the short-axis measurements of SEM revealed a strong significant correlation.The hair diameters from superior, middle, and inferior boxes revealed a tendency to decrease toward the inferior regions. Conclusions:A manual micrometer caliper is a dependable tool for planning FUE surgery, and it measures the short axis of the elliptical hair shaft. Hair diameter tends to decrease toward the inferior regions of the donor area. K E Y W O R D S caliper micrometer, hair diameter, hair transplantation, scanning electron microscopy | 1087 Region N Micrometer caliper SEM short axis SEM long axis Mean ± SD, Median (range) Overall 270 53.4 ± 7.8 53.0 (34.0-82.0) 51.5 ± 8.3 50.6 (29.9-82.0) 83.0 ± 12.2 83.3 (49.0-112.1) Occipital 90 52.4 ± 7.1 52.
Robots and artificial intelligence technologies have become very important in the health applications as in many other fields. The proposed system in this work aims to provide detailed analysis of pre-op and post-op stage of FUE hair transplant procedures to enable surgeon to plan and assess success of the operations. In order to achieve this target, a robotic and vision-based system imaging and AI based analysis approach is developed. The proposed system performs analyses in three main stages: initialization, scanning, and analysis. At the initialization stage, 3D model of the patient's head generated at first by locating a depth camera in various positions around the patient by the help of a collaborative robot. At the second stage, where high resolution image capturing is performed in a loop with the usage of the 3D model, raw images are processed by a deep learning based object detection algorithm where follicles in pre-op and extracted follicle positions (i.e. holes) and placed grafts in post-op is detected. At the last stage, thickness of each hair is computed at the detected hair follicle positions using another deep learning-based segmentation approach. These data are combined to obtain objective evaluation criteria to generate patient report. Experimental results show that the developed system can be used successfully in hair transplantation operations.
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