Tool to visualize and evaluate operator proficiency in laser hair-removal treatments

Abstract

Background: The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot).

Methods: A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices.

Results: The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used.

Conclusions: Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment.

Figure 1

Simulation bed used in this experiment. Lasers emitted on the human skin layer were reflected to an infrared camera. A silicon rubber sheet was stacked on top of the simulation bed to mimic the friction of human skin and a normal PC camera was utilized for the visualization of the laser.

Figure 2

Applicator tip of the laser device used in the experiments. The size of the applicator tip is larger than the laser window owing to the cooling area located at its perimeter.

Figure 3

Design of simulation experiments. Two different treatments techniques were simulated and each treatment technique was composed of two recorded sessions of the simulation (filled circles). Feedback on the procedural performance was given between these two successive sessions.

Figure 4

Block diagram of image processing steps to synthesize an LDM. Laser-exposed frames were extracted from the video recorded during a simulation session. Each laser-exposed frame was binarized to clearly detect the laser spot, then its centroid position was computed. The LDM was reconstructed by overlaying the template image of the laser window to each centroid position of the detected laser spot.

Figure 5

Mean intensity plot of the recorded video to specify laser-exposed frames. Laser-exposed frames were extracted from the video based on the brightness scale of frames. Peaks in the plot represent laser-exposed frames; however, some peaks with less than 30% of normal peak height were not counted as valid laser-exposed frames. These frames occurred when the laser was fired in the air, mostly during directional changes of the laser applicator when the SBS technique was simulated.

Figure 6

Illustration of LDM synthesis by locating laser spots and overlaying template images. The raw images of laser spots were binarized and a square template mimicking the actual size and shape of the laser-emitting window was overlaid to the centroid position of each laser spot by allowing superimposition. The reconstruction process was necessary because distortions were evident in the raw images, caused by the scattering effect of the rubber sheet and the point spread characteristics of the camera system.

Figure 7

Synthetic LDM to illustrate the computation process of performance indices. The LDM has three overlapped laser spots and the pixel values indicate the amount of laser exposure at the site. Here, the maximum redundancy in laser delivery is 3 and the centroid position of each laser spot was marked as red. The indices δ₀ and δ_z are computed based on the pixel values, and μ and υ are computed based on the centroid position of laser spots.

Figure 8

Differences in indices among performance groups. Each performance group was characterized by using a combination of indices (*P < 0.05, **P < 0.01). The quantitative description of satisfactory patterns in laser delivery in LHR treatment aims to achieve minimal randomness in the spatial distribution of laser spots, with accurate estimation of the size of the laser spots.

Figure 9

Comparison of LDMs that represent different levels of performance. Nine LDMs selected from three performance groups are shown to exemplify different levels of performance. The LDMs from group G presented better laser distribution than the rest. Specifically, group P₊ and group P_- exhibited more overlapping and omission, respectively, than group G.

Figure 10

Individual changes of μ after training for the sliding technique. The greater the deviation an operator exhibited in μ from the ideal value of 12 mm, the greater improvement was observed after training, except for subject 6.