A Simulation-Based Decision Support Framework for Laser Parameter Selection Across Skin Phototypes

A Simulation-Based Decision Support Framework for Laser Parameter Selection Across Skin Phototypes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Simulation-Based Decision Support Framework for Laser Parameter Selection Across Skin Phototypes Nur Ecer, Omer Karakoyun, Kadir Kaya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9327225/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Purpose Laser parameter selection remains challenging in dermatology, particularly across diverse skin phototypes where inappropriate wavelength choice increases the risk of epidermal injury. This study aimed to develop a simulation-based decision support framework to guide wavelength selection based on phototype-dependent epidermal energy distribution. Methods A Monte Carlo light transport model was used to simulate photon propagation in a three-layer skin model across Fitzpatrick skin types I–VI. Four commonly used wavelengths (532, 755, 808, and 1064 nm) were evaluated. Epidermal energy deposition was quantified using the Epidermal Thermal Risk Index (ETRI). A decision support framework was constructed by categorizing ETRI values into risk levels (low, moderate, high) to enable cross-condition comparison and clinical interpretation. Results Shorter wavelengths demonstrated significantly higher epidermal energy deposition, particularly in higher phototypes. At 532 nm, ETRI increased markedly with melanin content, indicating elevated epidermal thermal burden. In contrast, 1064 nm maintained consistently low ETRI values across all phototypes. The proposed framework enabled clear differentiation of wavelength suitability, with longer wavelengths demonstrating more favorable epidermis-to-dermis energy distribution in darker skin types. Conclusion This study presents a simulation-based framework that translates optical modeling into clinically interpretable guidance for laser parameter selection. The findings support preferential use of longer wavelengths in higher phototypes and highlight the potential of model-based tools for improving safety and treatment planning in dermatologic laser applications. Laser therapy Skin phototype Monte Carlo simulation Dermatology Wavelength selection Decision support system Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTIONS Laser and light-based technologies have become fundamental tools in dermatology, with widespread applications in hair removal, vascular and pigmented lesion treatment, and skin rejuvenation. These procedures rely on the principle of selective photothermolysis, in which specific chromophores absorb optical energy leading to controlled thermal injury of targeted structures while sparing surrounding tissue [ 1 ]. Despite their broad utility, treatment outcomes and safety profiles vary significantly depending on patient-specific factors, particularly skin pigmentation. Skin phototype, commonly classified using the Fitzpatrick scale, plays a critical role in determining laser–tissue interactions [ 2 ]. Melanin, the primary epidermal chromophore, exhibits strong wavelength-dependent absorption, which increases the risk of non-specific epidermal heating in individuals with higher phototypes (IV–VI). Clinically, this has been associated with a higher incidence of adverse effects such as burns, post-inflammatory hyperpigmentation, and scarring in darker skin types [ 3 – 5 ]. As a result, wavelength selection is a key determinant of both treatment efficacy and safety in dermatologic laser applications. Longer wavelengths, particularly in the near-infrared range (e.g., 1064 nm), are generally preferred for treating patients with increased melanin content due to their lower absorption by epidermal melanin and deeper tissue penetration [ 6 , 7 ]. However, current clinical decision-making remains largely experience-based and lacks quantitative, standardized tools to guide parameter selection across different skin phototypes and treatment scenarios. This gap highlights the need for objective frameworks that can translate underlying optical principles into clinically actionable guidance. Computational modeling, particularly Monte Carlo simulation of photon transport, has been widely used to investigate light–tissue interactions and predict energy distribution within biological tissues [ 8 , 9 ]. These models enable systematic evaluation of wavelength-dependent absorption and scattering effects under controlled conditions, offering a powerful approach to study phototype-specific differences that are difficult to isolate experimentally. Nevertheless, existing studies often focus on isolated wavelengths or limited skin types and rarely provide clinically interpretable metrics for decision-making. In this study, we present a simulation-based decision support framework for laser parameter selection across Fitzpatrick skin phototypes. Using a Monte Carlo model, we quantify epidermal energy deposition across commonly used dermatologic wavelengths (532, 755, 808, and 1064 nm). We introduce a normalized metric, the Epidermal Thermal Risk Index (ETRI), and integrate it into a clinically interpretable classification system to facilitate wavelength selection and risk stratification. This framework aims to bridge the gap between optical modeling and clinical practice by providing a practical tool for improving safety and treatment planning in dermatologic laser applications. 2. MATERIALS AND METHODS 2.1 Skin Optical Model A three-layer planar tissue model was constructed to represent the epidermis, dermis, and subcutaneous tissue. This simplified geometry was selected to isolate the primary variable of interest—melanin-dependent epidermal absorption—while maintaining computational efficiency. The model assumes homogeneous optical properties within each layer and does not explicitly account for microstructural heterogeneity such as discrete melanocyte distribution, vascular networks, or adnexal structures. Layer thicknesses and optical parameters were assigned based on previously published literature [ 10 – 12 ]. The epidermal thickness was set between 60 and 100 µm, while dermal and subcutaneous layers were modeled as 2.0 mm and 5.0 mm, respectively. Optical properties included the absorption coefficient (µₐ), reduced scattering coefficient (µₛ′), anisotropy factor (g), and refractive index (n), all defined as wavelength-dependent where applicable. 2.2 Melanin Absorption Model Epidermal absorption was modeled as a combination of melanin and baseline tissue absorption: µa=fmel⋅µmel+(1 − fmel)⋅µbaseline\mu_a = f_{mel} \cdot \mu_{mel} + (1 - f_{mel}) \cdot \mu_{baseline}µa​=fmel​⋅µmel​+(1 − fmel​)⋅µbaseline​ Melanin absorption followed a wavelength-dependent power-law relationship [ 13 ]: µmel = 6.6×1011⋅λ − 3.33\mu_{mel} = 6.6 \times 10^{11} \cdot \lambda^{-3.33}µmel​=6.6×1011⋅λ − 3.33 Melanin volume fractions corresponding to Fitzpatrick skin types were assigned based on experimental measurements reported in the literature [ 14 ], ranging from 1.3% (Type I) to 35% (Type VI). 2.3 Monte Carlo Simulation Photon transport was simulated using a Monte Carlo method based on the MCML framework [ 15 ]. Simulations were implemented in Python (version 3.9) with Numba acceleration to improve computational efficiency. A flat-top circular beam with a diameter of 6 mm was used to approximate clinical laser delivery conditions. Photons were launched perpendicular to the tissue surface and propagated through the layered medium using probabilistic sampling of scattering and absorption events. The simulation domain measured 10 × 10 × 7.1 mm with a voxel resolution of 50 µm. Scattering was modeled using the Henyey–Greenstein phase function. Boundary conditions included Fresnel reflection at the air–tissue interface and internal reflections between tissue layers. Each simulation consisted of 2 × 10⁶ photon packets. For each condition, five independent runs were performed, and results were averaged to ensure numerical stability. 2.4 Evaluated Wavelengths and Phototypes Four clinically relevant laser wavelengths were evaluated: 532 nm, 755 nm, 808 nm, and 1064 nm. Simulations were conducted across all six Fitzpatrick skin phototypes (I–VI), enabling systematic assessment of wavelength-dependent energy distribution as a function of melanin content. 2.5 Epidermal Thermal Risk Index (ETRI) To quantify epidermal energy burden, the Epidermal Thermal Risk Index (ETRI) was defined as: ETRI=EepidermisEtotal×100ETRI = \frac{E_{epidermis}}{E_{total}} \times 100ETRI=Etotal​Eepidermis​​×100 where EepidermisE_{epidermis}Eepidermis​ represents the absorbed energy within the epidermal layer and EtotalE_{total}Etotal​ is the total absorbed energy across all tissue layers. ETRI provides a normalized metric enabling direct comparison across wavelengths and phototypes independent of absolute fluence. Risk categories were defined as: Low risk: 50% 2.6 Multi-Objective Optimization Framework To extend the model beyond single-metric risk assessment, a multi-objective framework was introduced to evaluate both epidermal safety and dermal energy delivery. 2.6.1 Benefit–Risk Ratio (BRR) A novel metric, the Benefit–Risk Ratio (BRR), was defined as: BRR=EdermisEepidermisBRR = \frac{E_{dermis}}{E_{epidermis}}BRR=Eepidermis​Edermis​​ where EdermisE_{dermis}Edermis​ represents the absorbed energy within the dermis. Higher BRR values indicate more favorable energy distribution, characterized by increased dermal targeting with reduced epidermal exposure. Lower BRR values reflect predominant epidermal absorption and potentially higher risk of adverse effects. 2.6.2 Composite Scoring and Ranking To enable comparative evaluation across conditions, ETRI and BRR values were normalized to a 0–1 range. A composite score was defined as: Score=w1⋅(1 − ETRInorm)+w2⋅BRRnormScore = w_1 \cdot (1 - ETRI_{norm}) + w_2 \cdot BRR_{norm}Score=w1​⋅(1 − ETRInorm​)+w2​⋅BRRnorm​ where w1w_1w1​ and w2w_2w2​ represent weighting coefficients for safety and efficacy, respectively. Equal weighting (w1 = w2=0.5w_1 = w_2 = 0.5w1​=w2​=0.5) was used as the baseline configuration. For each phototype, wavelengths were ranked according to the composite score, and the highest-scoring wavelength was identified as the optimal parameter. 2.7 Decision Support Framework A decision support framework was constructed by integrating ETRI-based risk categories with BRR-based efficacy assessment and composite ranking. This framework enables simultaneous evaluation of safety and performance, providing a clinically interpretable basis for wavelength selection across different skin phototypes. 2.8 Sensitivity Analysis A one-way sensitivity analysis was performed to evaluate the influence of key parameters on model outputs. Melanin volume fraction (± 20%), epidermal thickness (± 25%), and reduced scattering coefficient (± 30%) were varied independently. Changes in ETRI and BRR were quantified to assess parameter sensitivity. 2.9 Statistical Analysis Results are reported as mean ± standard deviation across five independent simulation runs. The coefficient of variation (CV) was calculated to assess numerical stability. As simulations are deterministic under fixed conditions, variability reflects computational precision rather than biological variability. 3. RESULTS 3.1 Wavelength-Dependent Epidermal Energy Deposition Epidermal energy deposition, quantified by the Epidermal Thermal Risk Index (ETRI), demonstrated strong dependence on both wavelength and skin phototype (Table 1 , Fig. 1 ). ETRI values ranged from 10.7% (Type I at 1064 nm) to 90.7% (Type VI at 532 nm), representing an approximately 8-fold variation across the evaluated conditions. At 532 nm, ETRI increased progressively with melanin content, from 28.2% in Type I to 90.7% in Type VI, indicating marked confinement of optical energy within the epidermis in darker skin types. Phototypes IV–VI exceeded the high-risk threshold (> 50%) at this wavelength, with Type VI demonstrating the highest epidermal energy burden across all evaluated conditions. In contrast, 1064 nm maintained consistently lower ETRI values across all phototypes, ranging from 10.7% (Type I) to 55.0% (Type VI). Notably, even in Type VI, the ETRI at 1064 nm (55.0%) was comparable to the values observed at shorter wavelengths for intermediate phototypes (e.g., Type III at 532 nm: 56.0%), suggesting that wavelength selection can substantially influence epidermal thermal burden independent of skin pigmentation. Intermediate wavelengths (755 nm and 808 nm) exhibited similar ETRI profiles, with moderate increases across phototypes. At 808 nm, ETRI remained below 50% for Types I–IV, transitioning to high-risk levels only in Types V (58.3%) and VI (69.2%). 3.2 Dermal Energy Deposition and Benefit–Risk Ratio The Benefit–Risk Ratio (BRR), defined as the ratio of dermal to epidermal energy deposition, provided a complementary metric for evaluating wavelength performance (Table 2 , Fig. 2 ). Higher BRR values indicate more favorable energy distribution, with greater dermal penetration relative to epidermal absorption. BRR demonstrated substantial variation across conditions, ranging from 0.10 (Type VI at 532 nm) to 8.32 (Type I at 1064 nm). At 1064 nm, BRR remained above 0.82 across all phototypes, indicating near-equivalent or greater dermal energy deposition relative to epidermal absorption even in highly pigmented skin. In contrast, at 532 nm, BRR values fell below 1.0 for Types III–VI, indicating predominant epidermal confinement of optical energy. The inverse relationship between BRR and melanin content was most pronounced at shorter wavelengths. At 532 nm, BRR decreased by a factor of 25 from Type I (2.55) to Type VI (0.10). At 1064 nm, this reduction was more modest, with BRR decreasing by a factor of approximately 10 across the same phototype range (8.32 to 0.82). 3.3 Composite Score and Wavelength Ranking Integration of ETRI and BRR into a composite score enabled unified evaluation of wavelength performance considering both safety and efficacy (Table 3 , Fig. 3 ). The composite score ranged from 0 (least favorable) to 1 (most favorable), with equal weighting assigned to normalized safety (1 – ETRI norm ) and efficacy (BRR norm ) components. Across all phototypes, 1064 nm consistently achieved the highest composite scores, ranging from 1.00 (Type I) to 0.27 (Type VI). This wavelength was identified as the optimal parameter choice for all evaluated skin types based on the multi-objective framework. Importantly, the superiority of 1064 nm was consistent across all phototypes, indicating that wavelength selection alone can substantially mitigate epidermal risk even in highly pigmented skin. For lower phototypes (I–III), multiple wavelengths demonstrated acceptable performance, with composite scores above 0.40 for all wavelengths except 532 nm in Type III. However, for higher phototypes (IV–VI), the performance gap between 1064 nm and other wavelengths widened substantially. In Type VI, the composite score for 1064 nm (0.27) was approximately twice that of 808 nm (0.16) and more than 20-fold higher than 532 nm (0.00). 3.4 Decision Support Framework Based on the integrated analysis of ETRI, BRR, and composite scores, a clinical decision support framework was constructed (Table 4 ). This framework categorizes wavelength suitability into three levels: Recommended (composite score ≥ 0.50), Use with Caution (0.25–0.49), and Avoid (< 0.25). These thresholds are simulation-derived and intended as guidance rather than absolute contraindications. 3.5 Summary of Key Findings The principal findings of this study can be summarized as follows: (1) ETRI demonstrated an approximately 8-fold variation across evaluated conditions, with the highest values observed at 532 nm in Type VI (90.7%) and lowest at 1064 nm in Type I (10.7%); (2) BRR values indicated that 1064 nm achieved superior dermal penetration relative to epidermal absorption across all phototypes; (3) the multi-objective composite score consistently identified 1064 nm as the optimal wavelength choice, regardless of phototype; and (4) the decision support framework provides a practical basis for clinical wavelength selection, particularly emphasizing avoidance of shorter wavelengths in higher phototypes and preferential use of longer wavelengths to optimize the safety–efficacy balance. 4. DISCUSSION This study presents a simulation-based decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment (ETRI) with a novel efficacy metric (BRR), we provide a multi-objective approach that addresses both safety and treatment performance considerations. The principal finding is that 1064 nm consistently outperforms shorter wavelengths across all evaluated phototypes when both safety and efficacy are considered simultaneously. The clinical implications of these findings are substantial. For patients with higher phototypes (IV–VI), the model strongly supports preferential use of longer wavelengths, particularly 1064 nm. At this wavelength, even Type VI skin maintained a composite score of 0.27, compared to near-zero scores for 532 nm. This quantitative differentiation provides an objective basis for wavelength selection that complements clinical experience and may be particularly valuable for practitioners with limited experience treating diverse skin types. This study presents a simulation-based decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment (ETRI) with a novel efficacy metric (BRR), we provide a multi-objective approach that addresses both safety and treatment performance considerations. The principal finding is that 1064 nm consistently outperforms shorter wavelengths across all evaluated phototypes when both safety and efficacy are considered simultaneously. The clinical implications of these findings are substantial. For patients with higher phototypes (IV–VI), the model strongly supports preferential use of longer wavelengths, particularly 1064 nm [ 16 – 18 ]. At this wavelength, even Type VI skin maintained a composite score of 0.27, compared to near-zero scores for 532 nm. This quantitative differentiation provides an objective basis for wavelength selection that complements clinical experience and may be particularly valuable for practitioners with limited experience treating diverse skin types. Our findings are consistent with established principles of selective photothermolysis and clinical observations regarding laser safety in skin of color [ 1 , 2 ]. The marked increase in ETRI at shorter wavelengths in higher phototypes aligns with the known wavelength-dependent absorption characteristics of melanin and the clinically observed higher incidence of adverse effects in darker skin types [ 3 – 5 , 16 – 18 ]. Previous Monte Carlo studies have characterized light transport in skin tissue [ 8 , 9 ], but typically focused on single wavelengths or limited phototype ranges. The present study extends this work by systematically evaluating four clinically relevant wavelengths across all six Fitzpatrick phototypes and, importantly, by translating simulation outputs into a clinically interpretable decision framework. The introduction of BRR as a complementary metric represents a methodological advancement, enabling simultaneous consideration of safety and efficacy rather than risk assessment alone. The Benefit–Risk Ratio introduced in this study provides a complementary perspective to conventional risk-focused metrics. While ETRI quantifies the proportion of energy deposited in the epidermis (a surrogate for thermal injury risk), BRR evaluates the efficiency of energy delivery to the dermis relative to epidermal absorption. A BRR value greater than 1 indicates that more energy reaches the dermis than is absorbed in the epidermis, suggesting favorable conditions for effective treatment of dermal targets while minimizing epidermal thermal burden [ 16 , 19 ]. The dramatic variation in BRR across conditions—from 0.10 (Type VI at 532 nm) to 8.32 (Type I at 1064 nm)—highlights the substantial impact of wavelength selection on treatment efficiency. For Type VI skin, the choice of 1064 nm over 532 nm results in an 8-fold improvement in BRR, indicating substantially more favorable energy distribution for dermal targeting. The decision support matrix (Table 4 ) provides a practical tool for rapid clinical assessment of wavelength suitability. For Type I–II skin, all evaluated wavelengths demonstrate acceptable performance, affording clinicians flexibility in wavelength selection based on treatment indication and available equipment. However, for Type IV–VI skin, the framework clearly indicates that shorter wavelengths should be avoided, with 1064 nm representing the preferred choice even though it receives a "Caution" designation in the highest phototypes [ 16 – 18 ]. It is important to emphasize that the thresholds used in the decision matrix are simulation-derived and intended as guidance rather than absolute contraindications. Clinical decision-making must integrate these model-based recommendations with individual patient factors, treatment indication, operator experience, and device-specific parameters including pulse duration and fluence settings. Several limitations should be considered when interpreting these findings. First, the model employs a simplified three-layer tissue geometry that does not account for microstructural heterogeneity including discrete melanocyte distribution, vascular networks, or adnexal structures. Second, melanin volume fractions were assigned based on population averages and may not reflect individual variation within phototype categories. Third, the simulation does not incorporate temporal dynamics of heat diffusion or thermal relaxation, which are critical determinants of actual tissue injury [ 20 ]. Additionally, ETRI and BRR should be interpreted as relative optical burden metrics rather than direct predictors of clinical injury. The relationship between simulated energy deposition and actual thermal damage depends on numerous factors not captured in the present model, including pulse duration, repetition rate, cooling methods, and individual tissue thermal properties. Future work incorporating thermal modeling and clinical validation would strengthen the translational relevance of these findings. Several directions for future research emerge from this work. Integration of thermal modeling would enable prediction of temperature distributions and thermal damage thresholds. Expansion of the wavelength range to include additional clinically relevant wavelengths (e.g., 595 nm, 755 nm pulsed, 1540 nm) would broaden the applicability of the framework. Clinical validation studies correlating simulation predictions with treatment outcomes and adverse events would establish the predictive validity of the decision framework [ 20 ]. Furthermore, the multi-objective framework developed here could be extended to incorporate additional clinical objectives, such as target chromophore specificity or depth-specific energy delivery for different treatment indications. Such extensions would enhance the clinical utility of simulation-based decision support in dermatologic laser applications. 5. CONCLUSION This study presents a simulation-based multi-objective decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment with a novel benefit–risk metric, we provide a quantitative basis for wavelength selection that considers both safety and treatment efficacy. The principal findings demonstrate that: (1) epidermal thermal burden varies substantially across wavelengths and phototypes, with an approximately 8-fold range in ETRI values; (2) the Benefit–Risk Ratio provides a complementary efficacy metric that captures the efficiency of dermal energy delivery; (3) 1064 nm consistently achieves the highest composite scores across all phototypes; and (4) the decision support matrix offers a practical tool for clinical wavelength selection, with particular emphasis on avoiding shorter wavelengths in higher phototypes. These findings support a shift toward wavelength-driven rather than phototype-limited paradigms in laser treatment planning. The framework developed here may serve as a foundation for evidence-based parameter selection, particularly for practitioners treating diverse patient populations. Future clinical validation studies will be essential to establish the predictive value of simulation-based decision support in dermatologic laser practice. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflicts of Interest The authors declare no conflicts of interest. Ethics Approval Not applicable. This study is based on computational simulation. Data Availability The simulation code and data are available at: https://github.com/omar-k2025/A-Simulation-Based-Decision-Support-Framework-for-Laser-Parameter-Selection-Across-Skin-Phototypes. DOI: 10.5281/zenodo.19427750 Code Availability Monte Carlo simulation code (Python/Jupyter) available under MIT License at the repository above. Author Contribution NE: Conceptualization, Methodology, Software, Writing–Original Draft. ÖK: Validation, Investigation, Writing–Review & Editing. 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Additional Declarations No competing interests reported. Supplementary Files Tables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 29 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor assigned by journal 12 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 05 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9327225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625814434,"identity":"34942cdc-20f9-48df-be37-c3b65f7a65dc","order_by":0,"name":"Nur Ecer","email":"","orcid":"","institution":"Private Dermatology Practice","correspondingAuthor":false,"prefix":"","firstName":"Nur","middleName":"","lastName":"Ecer","suffix":""},{"id":625814437,"identity":"0a72153a-6d7f-4776-81fd-eef0329d4518","order_by":1,"name":"Omer Karakoyun","email":"","orcid":"","institution":"Diyarbakır Gazi Yaşargil Eğitim ve Araştırma Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Omer","middleName":"","lastName":"Karakoyun","suffix":""},{"id":625814439,"identity":"2b7bd0db-5ca3-413f-931f-fff33c504bd6","order_by":2,"name":"Kadir Kaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3RsUrDQBjA8S8E4nLS9UKk7SNcJx0qvsqFQKYoBUEylQOhbp1b0Hc48QXuyJDlMGs3A0KnCGZLcfF6LRSkiY4O91/y3fDLJXcANts/DAMQAUD16Aph1tvQ34hHBdADwV0EdgSR3fM34j9kz1kK8YDkqpab5uKG5MwpP2YwPWfHSYDiiVSQjLi65hmi+JYo4Y6eZoDPxHHSh4RIBqnDhSb6X0K+ol5wqknbl/V7lSFXvKhK2WzJW3ny1UUCbHZJ9MsTEMjsAp7bRfzFeiIZiaPlak0yFONwqcJ7//EV+4sWgovopWZpdDkvove6GU/DeZ7Jz+pu3Gs9ZZO+kaHYz0PhMOi4lkMD9nOw2Ww2275v24tiFaYWcA0AAAAASUVORK5CYII=","orcid":"","institution":"Istinye University","correspondingAuthor":true,"prefix":"","firstName":"Kadir","middleName":"","lastName":"Kaya","suffix":""}],"badges":[],"createdAt":"2026-04-05 15:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9327225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9327225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107707437,"identity":"76a3407f-6128-4921-acb0-c284662ea950","added_by":"auto","created_at":"2026-04-24 09:20:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51707,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap visualization of ETRI (%) across wavelengths and Fitzpatrick phototypes. Color scale ranges from green (low risk) to red (high risk).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9327225/v1/c39898740bd02f7fc65b9a66.jpeg"},{"id":107706529,"identity":"ed2bc171-092d-4155-9ac5-d904804ba796","added_by":"auto","created_at":"2026-04-24 09:18:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51951,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap visualization of Benefit–Risk Ratio (BRR) across wavelengths and phototypes. Higher values (green) indicate more favorable dermal energy delivery relative to epidermal absorption.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9327225/v1/4a1dfa514fe23af4bcb0e2ed.jpeg"},{"id":107628014,"identity":"8d707544-6162-443d-a957-f528908ef88d","added_by":"auto","created_at":"2026-04-23 11:04:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54039,"visible":true,"origin":"","legend":"\u003cp\u003eComposite Score heatmap integrating safety (ETRI) and efficacy (BRR) metrics. Higher scores (green) indicate more favorable overall performance for wavelength selection.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9327225/v1/2dc49f2c48b6c976be02e787.jpeg"},{"id":107709281,"identity":"9d9a1477-2bd6-4285-bcda-919c9f26a0b7","added_by":"auto","created_at":"2026-04-24 09:35:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":355298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9327225/v1/8bd32a35-b565-4231-a3d6-2a9dc99c6603.pdf"},{"id":107628012,"identity":"8e349543-a82a-4e22-bfb1-d5a0230986d2","added_by":"auto","created_at":"2026-04-23 11:04:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19427,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9327225/v1/d44c4ab93077ba07e38b4700.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Simulation-Based Decision Support Framework for Laser Parameter Selection Across Skin Phototypes","fulltext":[{"header":"1. INTRODUCTIONS","content":"\u003cp\u003eLaser and light-based technologies have become fundamental tools in dermatology, with widespread applications in hair removal, vascular and pigmented lesion treatment, and skin rejuvenation. These procedures rely on the principle of selective photothermolysis, in which specific chromophores absorb optical energy leading to controlled thermal injury of targeted structures while sparing surrounding tissue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite their broad utility, treatment outcomes and safety profiles vary significantly depending on patient-specific factors, particularly skin pigmentation.\u003c/p\u003e \u003cp\u003eSkin phototype, commonly classified using the Fitzpatrick scale, plays a critical role in determining laser\u0026ndash;tissue interactions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Melanin, the primary epidermal chromophore, exhibits strong wavelength-dependent absorption, which increases the risk of non-specific epidermal heating in individuals with higher phototypes (IV\u0026ndash;VI). Clinically, this has been associated with a higher incidence of adverse effects such as burns, post-inflammatory hyperpigmentation, and scarring in darker skin types [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As a result, wavelength selection is a key determinant of both treatment efficacy and safety in dermatologic laser applications.\u003c/p\u003e \u003cp\u003eLonger wavelengths, particularly in the near-infrared range (e.g., 1064 nm), are generally preferred for treating patients with increased melanin content due to their lower absorption by epidermal melanin and deeper tissue penetration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, current clinical decision-making remains largely experience-based and lacks quantitative, standardized tools to guide parameter selection across different skin phototypes and treatment scenarios. This gap highlights the need for objective frameworks that can translate underlying optical principles into clinically actionable guidance.\u003c/p\u003e \u003cp\u003eComputational modeling, particularly Monte Carlo simulation of photon transport, has been widely used to investigate light\u0026ndash;tissue interactions and predict energy distribution within biological tissues [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These models enable systematic evaluation of wavelength-dependent absorption and scattering effects under controlled conditions, offering a powerful approach to study phototype-specific differences that are difficult to isolate experimentally. Nevertheless, existing studies often focus on isolated wavelengths or limited skin types and rarely provide clinically interpretable metrics for decision-making.\u003c/p\u003e \u003cp\u003eIn this study, we present a simulation-based decision support framework for laser parameter selection across Fitzpatrick skin phototypes. Using a Monte Carlo model, we quantify epidermal energy deposition across commonly used dermatologic wavelengths (532, 755, 808, and 1064 nm). We introduce a normalized metric, the Epidermal Thermal Risk Index (ETRI), and integrate it into a clinically interpretable classification system to facilitate wavelength selection and risk stratification. This framework aims to bridge the gap between optical modeling and clinical practice by providing a practical tool for improving safety and treatment planning in dermatologic laser applications.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Skin Optical Model\u003c/h2\u003e \u003cp\u003eA three-layer planar tissue model was constructed to represent the epidermis, dermis, and subcutaneous tissue. This simplified geometry was selected to isolate the primary variable of interest\u0026mdash;melanin-dependent epidermal absorption\u0026mdash;while maintaining computational efficiency. The model assumes homogeneous optical properties within each layer and does not explicitly account for microstructural heterogeneity such as discrete melanocyte distribution, vascular networks, or adnexal structures.\u003c/p\u003e \u003cp\u003eLayer thicknesses and optical parameters were assigned based on previously published literature [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The epidermal thickness was set between 60 and 100 \u0026micro;m, while dermal and subcutaneous layers were modeled as 2.0 mm and 5.0 mm, respectively. Optical properties included the absorption coefficient (\u0026micro;ₐ), reduced scattering coefficient (\u0026micro;ₛ\u0026prime;), anisotropy factor (g), and refractive index (n), all defined as wavelength-dependent where applicable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Melanin Absorption Model\u003c/h2\u003e \u003cp\u003eEpidermal absorption was modeled as a combination of melanin and baseline tissue absorption:\u003c/p\u003e \u003cp\u003e\u0026micro;a=fmel\u0026sdot;\u0026micro;mel+(1\u0026thinsp;\u0026minus;\u0026thinsp;fmel)\u0026sdot;\u0026micro;baseline\\mu_a\u0026thinsp;=\u0026thinsp;f_{mel} \\cdot \\mu_{mel} + (1 - f_{mel}) \\cdot \\mu_{baseline}\u0026micro;a​=fmel​\u0026sdot;\u0026micro;mel​+(1\u0026thinsp;\u0026minus;\u0026thinsp;fmel​)\u0026sdot;\u0026micro;baseline​\u003c/p\u003e \u003cp\u003eMelanin absorption followed a wavelength-dependent power-law relationship [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e\u0026micro;mel\u0026thinsp;=\u0026thinsp;6.6\u0026times;1011\u0026sdot;λ\u0026thinsp;\u0026minus;\u0026thinsp;3.33\\mu_{mel} = 6.6 \\times 10^{11} \\cdot \\lambda^{-3.33}\u0026micro;mel​=6.6\u0026times;1011\u0026sdot;λ\u0026thinsp;\u0026minus;\u0026thinsp;3.33\u003c/p\u003e \u003cp\u003eMelanin volume fractions corresponding to Fitzpatrick skin types were assigned based on experimental measurements reported in the literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], ranging from 1.3% (Type I) to 35% (Type VI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Monte Carlo Simulation\u003c/h2\u003e \u003cp\u003ePhoton transport was simulated using a Monte Carlo method based on the MCML framework [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Simulations were implemented in Python (version 3.9) with Numba acceleration to improve computational efficiency.\u003c/p\u003e \u003cp\u003eA flat-top circular beam with a diameter of 6 mm was used to approximate clinical laser delivery conditions. Photons were launched perpendicular to the tissue surface and propagated through the layered medium using probabilistic sampling of scattering and absorption events.\u003c/p\u003e \u003cp\u003eThe simulation domain measured 10 \u0026times; 10 \u0026times; 7.1 mm with a voxel resolution of 50 \u0026micro;m. Scattering was modeled using the Henyey\u0026ndash;Greenstein phase function. Boundary conditions included Fresnel reflection at the air\u0026ndash;tissue interface and internal reflections between tissue layers.\u003c/p\u003e \u003cp\u003eEach simulation consisted of 2 \u0026times; 10⁶ photon packets. For each condition, five independent runs were performed, and results were averaged to ensure numerical stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Evaluated Wavelengths and Phototypes\u003c/h2\u003e \u003cp\u003eFour clinically relevant laser wavelengths were evaluated: 532 nm, 755 nm, 808 nm, and 1064 nm. Simulations were conducted across all six Fitzpatrick skin phototypes (I\u0026ndash;VI), enabling systematic assessment of wavelength-dependent energy distribution as a function of melanin content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Epidermal Thermal Risk Index (ETRI)\u003c/h2\u003e \u003cp\u003eTo quantify epidermal energy burden, the Epidermal Thermal Risk Index (ETRI) was defined as:\u003c/p\u003e \u003cp\u003eETRI=EepidermisEtotal\u0026times;100ETRI = \\frac{E_{epidermis}}{E_{total}} \\times 100ETRI=Etotal​Eepidermis​​\u0026times;100\u003c/p\u003e \u003cp\u003ewhere EepidermisE_{epidermis}Eepidermis​ represents the absorbed energy within the epidermal layer and EtotalE_{total}Etotal​ is the total absorbed energy across all tissue layers.\u003c/p\u003e \u003cp\u003eETRI provides a normalized metric enabling direct comparison across wavelengths and phototypes independent of absolute fluence. Risk categories were defined as:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLow risk: \u0026lt;30%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eModerate risk: 30\u0026ndash;50%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHigh risk: \u0026gt;50%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Multi-Objective Optimization Framework\u003c/h2\u003e \u003cp\u003eTo extend the model beyond single-metric risk assessment, a multi-objective framework was introduced to evaluate both epidermal safety and dermal energy delivery.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Benefit\u0026ndash;Risk Ratio (BRR)\u003c/h2\u003e \u003cp\u003eA novel metric, the Benefit\u0026ndash;Risk Ratio (BRR), was defined as:\u003c/p\u003e \u003cp\u003eBRR=EdermisEepidermisBRR = \\frac{E_{dermis}}{E_{epidermis}}BRR=Eepidermis​Edermis​​\u003c/p\u003e \u003cp\u003ewhere EdermisE_{dermis}Edermis​ represents the absorbed energy within the dermis.\u003c/p\u003e \u003cp\u003eHigher BRR values indicate more favorable energy distribution, characterized by increased dermal targeting with reduced epidermal exposure. Lower BRR values reflect predominant epidermal absorption and potentially higher risk of adverse effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Composite Scoring and Ranking\u003c/h2\u003e \u003cp\u003eTo enable comparative evaluation across conditions, ETRI and BRR values were normalized to a 0\u0026ndash;1 range. A composite score was defined as:\u003c/p\u003e \u003cp\u003eScore=w1\u0026sdot;(1\u0026thinsp;\u0026minus;\u0026thinsp;ETRInorm)+w2\u0026sdot;BRRnormScore\u0026thinsp;=\u0026thinsp;w_1 \\cdot (1 - ETRI_{norm}) + w_2 \\cdot BRR_{norm}Score=w1​\u0026sdot;(1\u0026thinsp;\u0026minus;\u0026thinsp;ETRInorm​)+w2​\u0026sdot;BRRnorm​\u003c/p\u003e \u003cp\u003ewhere w1w_1w1​ and w2w_2w2​ represent weighting coefficients for safety and efficacy, respectively. Equal weighting (w1\u0026thinsp;=\u0026thinsp;w2=0.5w_1\u0026thinsp;=\u0026thinsp;w_2\u0026thinsp;=\u0026thinsp;0.5w1​=w2​=0.5) was used as the baseline configuration.\u003c/p\u003e \u003cp\u003eFor each phototype, wavelengths were ranked according to the composite score, and the highest-scoring wavelength was identified as the optimal parameter.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Decision Support Framework\u003c/h2\u003e \u003cp\u003eA decision support framework was constructed by integrating ETRI-based risk categories with BRR-based efficacy assessment and composite ranking. This framework enables simultaneous evaluation of safety and performance, providing a clinically interpretable basis for wavelength selection across different skin phototypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eA one-way sensitivity analysis was performed to evaluate the influence of key parameters on model outputs. Melanin volume fraction (\u0026plusmn;\u0026thinsp;20%), epidermal thickness (\u0026plusmn;\u0026thinsp;25%), and reduced scattering coefficient (\u0026plusmn;\u0026thinsp;30%) were varied independently. Changes in ETRI and BRR were quantified to assess parameter sensitivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eResults are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation across five independent simulation runs. The coefficient of variation (CV) was calculated to assess numerical stability. As simulations are deterministic under fixed conditions, variability reflects computational precision rather than biological variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Wavelength-Dependent Epidermal Energy Deposition\u003c/h2\u003e\n \u003cp\u003eEpidermal energy deposition, quantified by the Epidermal Thermal Risk Index (ETRI), demonstrated strong dependence on both wavelength and skin phototype (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ETRI values ranged from 10.7% (Type I at 1064 nm) to 90.7% (Type VI at 532 nm), representing an approximately 8-fold variation across the evaluated conditions.\u003c/p\u003e\n \u003cp\u003eAt 532 nm, ETRI increased progressively with melanin content, from 28.2% in Type I to 90.7% in Type VI, indicating marked confinement of optical energy within the epidermis in darker skin types. Phototypes IV\u0026ndash;VI exceeded the high-risk threshold (\u0026gt;\u0026thinsp;50%) at this wavelength, with Type VI demonstrating the highest epidermal energy burden across all evaluated conditions.\u003c/p\u003e\n \u003cp\u003eIn contrast, 1064 nm maintained consistently lower ETRI values across all phototypes, ranging from 10.7% (Type I) to 55.0% (Type VI). Notably, even in Type VI, the ETRI at 1064 nm (55.0%) was comparable to the values observed at shorter wavelengths for intermediate phototypes (e.g., Type III at 532 nm: 56.0%), suggesting that wavelength selection can substantially influence epidermal thermal burden independent of skin pigmentation.\u003c/p\u003e\n \u003cp\u003eIntermediate wavelengths (755 nm and 808 nm) exhibited similar ETRI profiles, with moderate increases across phototypes. At 808 nm, ETRI remained below 50% for Types I\u0026ndash;IV, transitioning to high-risk levels only in Types V (58.3%) and VI (69.2%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Dermal Energy Deposition and Benefit\u0026ndash;Risk Ratio\u003c/h2\u003e\n \u003cp\u003eThe Benefit\u0026ndash;Risk Ratio (BRR), defined as the ratio of dermal to epidermal energy deposition, provided a complementary metric for evaluating wavelength performance (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Higher BRR values indicate more favorable energy distribution, with greater dermal penetration relative to epidermal absorption.\u003c/p\u003e\n \u003cp\u003eBRR demonstrated substantial variation across conditions, ranging from 0.10 (Type VI at 532 nm) to 8.32 (Type I at 1064 nm). At 1064 nm, BRR remained above 0.82 across all phototypes, indicating near-equivalent or greater dermal energy deposition relative to epidermal absorption even in highly pigmented skin. In contrast, at 532 nm, BRR values fell below 1.0 for Types III\u0026ndash;VI, indicating predominant epidermal confinement of optical energy.\u003c/p\u003e\n \u003cp\u003eThe inverse relationship between BRR and melanin content was most pronounced at shorter wavelengths. At 532 nm, BRR decreased by a factor of 25 from Type I (2.55) to Type VI (0.10). At 1064 nm, this reduction was more modest, with BRR decreasing by a factor of approximately 10 across the same phototype range (8.32 to 0.82).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Composite Score and Wavelength Ranking\u003c/h2\u003e\n \u003cp\u003eIntegration of ETRI and BRR into a composite score enabled unified evaluation of wavelength performance considering both safety and efficacy (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The composite score ranged from 0 (least favorable) to 1 (most favorable), with equal weighting assigned to normalized safety (1 \u0026ndash; ETRI\u003csub\u003enorm\u003c/sub\u003e) and efficacy (BRR\u003csub\u003enorm\u003c/sub\u003e) components.\u003c/p\u003e\n \u003cp\u003eAcross all phototypes, 1064 nm consistently achieved the highest composite scores, ranging from 1.00 (Type I) to 0.27 (Type VI). This wavelength was identified as the optimal parameter choice for all evaluated skin types based on the multi-objective framework. Importantly, the superiority of 1064 nm was consistent across all phototypes, indicating that wavelength selection alone can substantially mitigate epidermal risk even in highly pigmented skin.\u003c/p\u003e\n \u003cp\u003eFor lower phototypes (I\u0026ndash;III), multiple wavelengths demonstrated acceptable performance, with composite scores above 0.40 for all wavelengths except 532 nm in Type III. However, for higher phototypes (IV\u0026ndash;VI), the performance gap between 1064 nm and other wavelengths widened substantially. In Type VI, the composite score for 1064 nm (0.27) was approximately twice that of 808 nm (0.16) and more than 20-fold higher than 532 nm (0.00).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Decision Support Framework\u003c/h2\u003e\n \u003cp\u003eBased on the integrated analysis of ETRI, BRR, and composite scores, a clinical decision support framework was constructed (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This framework categorizes wavelength suitability into three levels: Recommended (composite score\u0026thinsp;\u0026ge;\u0026thinsp;0.50), Use with Caution (0.25\u0026ndash;0.49), and Avoid (\u0026lt;\u0026thinsp;0.25). These thresholds are simulation-derived and intended as guidance rather than absolute contraindications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Summary of Key Findings\u003c/h2\u003e\n \u003cp\u003eThe principal findings of this study can be summarized as follows: (1) ETRI demonstrated an approximately 8-fold variation across evaluated conditions, with the highest values observed at 532 nm in Type VI (90.7%) and lowest at 1064 nm in Type I (10.7%); (2) BRR values indicated that 1064 nm achieved superior dermal penetration relative to epidermal absorption across all phototypes; (3) the multi-objective composite score consistently identified 1064 nm as the optimal wavelength choice, regardless of phototype; and (4) the decision support framework provides a practical basis for clinical wavelength selection, particularly emphasizing avoidance of shorter wavelengths in higher phototypes and preferential use of longer wavelengths to optimize the safety\u0026ndash;efficacy balance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study presents a simulation-based decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment (ETRI) with a novel efficacy metric (BRR), we provide a multi-objective approach that addresses both safety and treatment performance considerations. The principal finding is that 1064 nm consistently outperforms shorter wavelengths across all evaluated phototypes when both safety and efficacy are considered simultaneously.\u003c/p\u003e \u003cp\u003eThe clinical implications of these findings are substantial. For patients with higher phototypes (IV\u0026ndash;VI), the model strongly supports preferential use of longer wavelengths, particularly 1064 nm. At this wavelength, even Type VI skin maintained a composite score of 0.27, compared to near-zero scores for 532 nm. This quantitative differentiation provides an objective basis for wavelength selection that complements clinical experience and may be particularly valuable for practitioners with limited experience treating diverse skin types.\u003c/p\u003e\u003cp\u003eThis study presents a simulation-based decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment (ETRI) with a novel efficacy metric (BRR), we provide a multi-objective approach that addresses both safety and treatment performance considerations. The principal finding is that 1064 nm consistently outperforms shorter wavelengths across all evaluated phototypes when both safety and efficacy are considered simultaneously.\u003c/p\u003e \u003cp\u003eThe clinical implications of these findings are substantial. For patients with higher phototypes (IV\u0026ndash;VI), the model strongly supports preferential use of longer wavelengths, particularly 1064 nm [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At this wavelength, even Type VI skin maintained a composite score of 0.27, compared to near-zero scores for 532 nm. This quantitative differentiation provides an objective basis for wavelength selection that complements clinical experience and may be particularly valuable for practitioners with limited experience treating diverse skin types.\u003c/p\u003e \u003cp\u003eOur findings are consistent with established principles of selective photothermolysis and clinical observations regarding laser safety in skin of color [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The marked increase in ETRI at shorter wavelengths in higher phototypes aligns with the known wavelength-dependent absorption characteristics of melanin and the clinically observed higher incidence of adverse effects in darker skin types [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious Monte Carlo studies have characterized light transport in skin tissue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], but typically focused on single wavelengths or limited phototype ranges. The present study extends this work by systematically evaluating four clinically relevant wavelengths across all six Fitzpatrick phototypes and, importantly, by translating simulation outputs into a clinically interpretable decision framework. The introduction of BRR as a complementary metric represents a methodological advancement, enabling simultaneous consideration of safety and efficacy rather than risk assessment alone.\u003c/p\u003e \u003cp\u003eThe Benefit\u0026ndash;Risk Ratio introduced in this study provides a complementary perspective to conventional risk-focused metrics. While ETRI quantifies the proportion of energy deposited in the epidermis (a surrogate for thermal injury risk), BRR evaluates the efficiency of energy delivery to the dermis relative to epidermal absorption. A BRR value greater than 1 indicates that more energy reaches the dermis than is absorbed in the epidermis, suggesting favorable conditions for effective treatment of dermal targets while minimizing epidermal thermal burden [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe dramatic variation in BRR across conditions\u0026mdash;from 0.10 (Type VI at 532 nm) to 8.32 (Type I at 1064 nm)\u0026mdash;highlights the substantial impact of wavelength selection on treatment efficiency. For Type VI skin, the choice of 1064 nm over 532 nm results in an 8-fold improvement in BRR, indicating substantially more favorable energy distribution for dermal targeting.\u003c/p\u003e \u003cp\u003eThe decision support matrix (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provides a practical tool for rapid clinical assessment of wavelength suitability. For Type I\u0026ndash;II skin, all evaluated wavelengths demonstrate acceptable performance, affording clinicians flexibility in wavelength selection based on treatment indication and available equipment. However, for Type IV\u0026ndash;VI skin, the framework clearly indicates that shorter wavelengths should be avoided, with 1064 nm representing the preferred choice even though it receives a \"Caution\" designation in the highest phototypes [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to emphasize that the thresholds used in the decision matrix are simulation-derived and intended as guidance rather than absolute contraindications. Clinical decision-making must integrate these model-based recommendations with individual patient factors, treatment indication, operator experience, and device-specific parameters including pulse duration and fluence settings.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, the model employs a simplified three-layer tissue geometry that does not account for microstructural heterogeneity including discrete melanocyte distribution, vascular networks, or adnexal structures. Second, melanin volume fractions were assigned based on population averages and may not reflect individual variation within phototype categories. Third, the simulation does not incorporate temporal dynamics of heat diffusion or thermal relaxation, which are critical determinants of actual tissue injury [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, ETRI and BRR should be interpreted as relative optical burden metrics rather than direct predictors of clinical injury. The relationship between simulated energy deposition and actual thermal damage depends on numerous factors not captured in the present model, including pulse duration, repetition rate, cooling methods, and individual tissue thermal properties. Future work incorporating thermal modeling and clinical validation would strengthen the translational relevance of these findings.\u003c/p\u003e \u003cp\u003eSeveral directions for future research emerge from this work. Integration of thermal modeling would enable prediction of temperature distributions and thermal damage thresholds. Expansion of the wavelength range to include additional clinically relevant wavelengths (e.g., 595 nm, 755 nm pulsed, 1540 nm) would broaden the applicability of the framework. Clinical validation studies correlating simulation predictions with treatment outcomes and adverse events would establish the predictive validity of the decision framework [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the multi-objective framework developed here could be extended to incorporate additional clinical objectives, such as target chromophore specificity or depth-specific energy delivery for different treatment indications. Such extensions would enhance the clinical utility of simulation-based decision support in dermatologic laser applications.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study presents a simulation-based multi-objective decision support framework for laser wavelength selection across Fitzpatrick skin phototypes. By integrating epidermal risk assessment with a novel benefit\u0026ndash;risk metric, we provide a quantitative basis for wavelength selection that considers both safety and treatment efficacy.\u003c/p\u003e\u003cp\u003eThe principal findings demonstrate that: (1) epidermal thermal burden varies substantially across wavelengths and phototypes, with an approximately 8-fold range in ETRI values; (2) the Benefit\u0026ndash;Risk Ratio provides a complementary efficacy metric that captures the efficiency of dermal energy delivery; (3) 1064 nm consistently achieves the highest composite scores across all phototypes; and (4) the decision support matrix offers a practical tool for clinical wavelength selection, with particular emphasis on avoiding shorter wavelengths in higher phototypes.\u003c/p\u003e\u003cp\u003eThese findings support a shift toward wavelength-driven rather than phototype-limited paradigms in laser treatment planning. The framework developed here may serve as a foundation for evidence-based parameter selection, particularly for practitioners treating diverse patient populations. Future clinical validation studies will be essential to establish the predictive value of simulation-based decision support in dermatologic laser practice.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003eNot applicable. This study is based on computational simulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003eThe simulation code and data are available at: https://github.com/omar-k2025/A-Simulation-Based-Decision-Support-Framework-for-Laser-Parameter-Selection-Across-Skin-Phototypes. DOI: 10.5281/zenodo.19427750\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u0026nbsp;\u003c/strong\u003eMonte Carlo simulation code (Python/Jupyter) available under MIT License at the repository above.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNE: Conceptualization, Methodology, Software, Writing\u0026ndash;Original Draft. \u0026Ouml;K: Validation, Investigation, Writing\u0026ndash;Review \u0026amp; Editing. KK: Supervision, Project Administration, Writing\u0026ndash;Review \u0026amp; Editing. 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J Am Acad Dermatol 49(1):1\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1067/mjd.2003.582\u003c/span\u003e\u003cspan address=\"10.1067/mjd.2003.582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelch A, van Gemert MJC (1995) Optical-Thermal Response of Laser-Irradiated Tissue\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Gemert MJ, Welch AJ (1989) Time constants in thermal laser medicine. Lasers Surg Med 9(4):405\u0026ndash;421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/lsm.1900090414\u003c/span\u003e\u003cspan address=\"10.1002/lsm.1900090414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lasers-in-medical-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lims","sideBox":"Learn more about [Lasers in Medical Science](https://link.springer.com/journal/10103)","snPcode":"10103","submissionUrl":"https://submission.springernature.com/new-submission/10103/3","title":"Lasers in Medical Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Laser therapy, Skin phototype, Monte Carlo simulation, Dermatology, Wavelength selection, Decision support system","lastPublishedDoi":"10.21203/rs.3.rs-9327225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9327225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eLaser parameter selection remains challenging in dermatology, particularly across diverse skin phototypes where inappropriate wavelength choice increases the risk of epidermal injury. This study aimed to develop a simulation-based decision support framework to guide wavelength selection based on phototype-dependent epidermal energy distribution.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA Monte Carlo light transport model was used to simulate photon propagation in a three-layer skin model across Fitzpatrick skin types I\u0026ndash;VI. Four commonly used wavelengths (532, 755, 808, and 1064 nm) were evaluated. Epidermal energy deposition was quantified using the Epidermal Thermal Risk Index (ETRI). A decision support framework was constructed by categorizing ETRI values into risk levels (low, moderate, high) to enable cross-condition comparison and clinical interpretation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eShorter wavelengths demonstrated significantly higher epidermal energy deposition, particularly in higher phototypes. At 532 nm, ETRI increased markedly with melanin content, indicating elevated epidermal thermal burden. In contrast, 1064 nm maintained consistently low ETRI values across all phototypes. The proposed framework enabled clear differentiation of wavelength suitability, with longer wavelengths demonstrating more favorable epidermis-to-dermis energy distribution in darker skin types.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study presents a simulation-based framework that translates optical modeling into clinically interpretable guidance for laser parameter selection. The findings support preferential use of longer wavelengths in higher phototypes and highlight the potential of model-based tools for improving safety and treatment planning in dermatologic laser applications.\u003c/p\u003e","manuscriptTitle":"A Simulation-Based Decision Support Framework for Laser Parameter Selection Across Skin Phototypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 11:04:23","doi":"10.21203/rs.3.rs-9327225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-05T00:31:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T15:59:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T15:01:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96709718217099381881847686488222543323","date":"2026-04-29T13:17:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T21:11:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250998639549125431472185730969456010884","date":"2026-04-24T05:51:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T20:00:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64526791910935360178486070149528240761","date":"2026-04-17T22:15:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47820942298521344707577743277515990028","date":"2026-04-17T20:59:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-15T22:06:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T02:51:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-10T10:55:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lasers in Medical Science","date":"2026-04-05T15:23:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lasers-in-medical-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lims","sideBox":"Learn more about [Lasers in Medical Science](https://link.springer.com/journal/10103)","snPcode":"10103","submissionUrl":"https://submission.springernature.com/new-submission/10103/3","title":"Lasers in Medical Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"00060ded-1505-4055-941e-9f3f5b0a810d","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-05T00:31:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T15:59:29+00:00","index":47,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T13:23:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 11:04:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9327225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9327225","identity":"rs-9327225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}