A New Predictive Model for Tattoo Removal: Leveraging Patient and Tattoo Characteristics

The objective of this research was to investigate key parameters impacting the process of tattoo removal and to propose a new predictive model for estimating the number of sessions necessary for the complete removal of black tattoos with a picosecond laser.

This prospective study involved 116 patients aged 18-62 years who visited our center between January 2020 and June 2024 for the full treatment of black tattoos. Data were collected about patient (age, gender, and phototype), tattoo specifics (age, size, location, ink density, country/region where the tattoo was created, if it was realized by an amateur or a professional tattoo artist, tattoo settings) and the total number of laser treatments. Treatments were performed with a Picosure laser (Cynosure, USA) at 755 nm using fluences of 0.69-6.37 J/cm² a pulsation length of 650 ps. Multi-way analysis of variance was performed to estimate the effect of each parameter. In order to estimate the number of sessions for complete tattoo removal, a predictive model was then created by the addition of parameter interactions (two-by-two only). Additional factors were included or excluded by a stepwise approach (backward and forward). Inclusion and exclusion criteria were based on p value thresholds.

ANOVA results revealed that ink density had the most significant impact on laser tattoo removal, followed by tattoo location, age, and design technique (dots, lines, or both). Country/region of origin and whether the tattoo was amateur or professional had a marginal effect, whereas design type (drawing or letters), patient age, skin type, gender, and tattoo size showed no significant influence. Based on these findings, we developed a new efficient predictive tool to estimate the number of picosecond laser sessions required for complete black tattoo removal. The Smarrito-Pineau (SP) model could be readily implemented since variables are easily assessable. It was possible to provide personalized treatment plans and enhance patient satisfaction by offering more accurate treatment timelines.