Systems and methods for skin damage detection and prevention

Abstract

Described herein is a skin detection and prevention system comprising an image converter module for receiving a first photograph and converting the first photograph to one or more new photographs, an image analyzer module for analyzing the one or more new photographs to identify one or more damaged skin areas of the new photographs, and a scoring module for receiving the analyzed second photograph and generating a skin score based on the one or more types of skin damage and the severity present in the one or more new photographs.

Description

SYSTEMS AND METHODS FOR SKIN DAMAGE DETECTION AND PREVENTIONCROSS-REFERENCE TO PRIOR APPLICATIONS

[0001] The present application claims priority and benefit of U.S. Provisional Patent Application No. 63 / 707,832 (filed 10 / 16 / 2024), which application is incorporated by reference to the extent permitted by applicable law.FIELD OF THE INVENTION

[0002] The present invention relates to a system for detection of skin damage, and more particularly to facial skin damage detection and prevention.BACKGROUND OF THE INVENTION

[0003] Skin pigmentation issues like freckles and sunspots are well-known to be linked to skin damage and more importantly skin cancer. Therefore, early detection systems are desirable to minimize and prevent further damaging of the skin cells in identified areas.

[0004] However, traditional facial skin detection and analyzing systems require special equipment and expensive procedures. These barriers prevent skin care from being accessible and affordable for everyday use. Therefore, a new skin damage detection and prevention system that is simple to use, does not require hardware, and is inexpensive is desired.SUMMARY OF THE INVENTION

[0005] The present invention provides a skin damage detection and prevention system including an image converter module, an image analyzer module, a scoring module, and a treatment module. In some embodiments, the image converter module receives a first photograph in a first format and converts the first photograph to one or more new photographs. In some embodiments, the one or more new photographs are configured into a format that is selected from the group consisting of UV, cross-polarized, and parallel polarized. In some embodiments, the image analyzer module receives the one or more new photographs and analyzes the one or more new photographs to identify one or more damaged skin areas of the new photographs. In some embodiments, the image analyzer module classifies each of the one or more damaged skin areas according to the type of skin damage present in the area. In some embodiments, the scoring module receives the analyzed second photograph and generates a skin score based on the one or more types of skin damage, and the severity present in the secondphotograph. In some embodiments, the treatment module receives the skin score from the scoring module and generates a treatment plan based on the one or more types of skin damage that was identified by the image analyzer module, the severity of the one or more damaged skin areas, and the overall skin score. In some embodiments, the treatment module includes a combination of cometic products and LLLT light therapy.

[0006] In some embodiments, the first format of the first photograph is a RGB image.In some embodiments, the one or more new photographs are in the format selected from the group consisting of UV, cross-polarized, and parallel polarized.

[0007] In some embodiments, the first photograph is generated from a camera of a smartphone.

[0008] In some embodiments, the type of skin damage present in the identified one or more damaged skin areas are selected from the group consisting of irregular pigmentation, redness, rosacea, broken capillaries, spider veins, sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas.

[0009] In some embodiments, the image analyzer module annotates the one or more new photographs to highlight each damaged skin area.

[0010] In some embodiments, the annotation for the one or more new photographs is selected from the group consisting of altering pixel contrast and adding a shape outline.

[0011] In some embodiments, the different types of skin damage identified by the image analyzer module are annotated in different colors.

[0012] In some embodiments, the different colors for each type of skin damage are shown on a gradient scale to depict severity of the highlighted skin damage area.

[0013] In some embodiments, the scoring module assesses factors selected from the group consisting of a user’s age, pigmentation, wrinkle depth, redness index, and pore size.

[0014] In some embodiments, the severity is assessed based on factors selected from the group consisting of density of skin damage type, wrinkle depth, intensity, and location.

[0015] In some embodiments, the system further comprises a treatment module for receiving the skin score from the scoring module and generating a treatment plan based on the one or more types of skin damage identified by the image analyzer module.

[0016] In some embodiments, the treatment plan is developed based on factors selected from the group consisting of skin score, severity of the one or more damaged skin areas, location of skin damage, type of skin damage, and the overall skin score.

[0017] In some embodiments, the treatment plan includes a combination of cosmetic products and LLLT therapy.

[0018] In an alternate embodiment, a computer-implemented method for detecting and assessing skin damage for a user on a mobile device includes receiving, from the user, a first photograph, of the user’ s skin, in a first format; converting the first photograph, received in the first format, to one or more new photographs each having a different format than the first format; analyzing the one or more new photographs to identify one or more damaged skin areas; classifying the one or more identified damaged skin areas based on the type of skin damage present in the one or more damaged skin areas; generating a skin score based upon the one or more identified damaged skin areas and the types of skin damage present in the one or more areas; and displaying, the skin score to the user on the mobile device.

[0019] In some embodiments, recommending a treatment plan to the user is based on the one or more types of skin damage identified.

[0020] In some embodiments, the first format of the first photograph is RGB.

[0021] In some embodiments, the one or more new photographs are in the format selected from the group consisting of UV, cross-polarized, and parallel polarized.

[0022] In some embodiments, the type of skin damage present in the identified one or more damaged skin areas are selected from the group consisting of irregular pigmentation, redness, rosacea, broken capillaries, spider veins, sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas.

[0023] In some embodiments, the generated skin score is based on factors selected from the group consisting of a user’s age, pigmentation, wrinkle depth, redness index, and pore size.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The features of the exemplary embodiments of the present invention will be described with reference to the following drawings, where like elements are labeled similarly, and in which:

[0025] FIG. l is a flow chart of a first embodiment of a skin damage detection and prevention system according to the present disclosure;

[0026] FIG. 2 is a flow diagram of the image converter module of the present disclosure;

[0027] FIG. 3 is a schematic diagram of the image converter module of the present disclosure; and

[0028] FIG. 4 is a schematic diagram of the image analyzer module of the present disclosure;

[0029] FIG. 5 is an embodiment of the user interface of the scoring module of the present disclosure; and

[0030] FIG. 6 is a flow chart of the skin damage detection and prevention system according to the present disclosure.

[0031] All drawings are schematic and not necessarily to scale. Parts given a reference numerical designation in one figure may be considered to be the same parts where they appear in other figures without a numerical designation for brevity unless specifically labeled with a different part number and described herein.DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The features and benefits of the invention are illustrated and described herein by reference to exemplary embodiments. This description of exemplary embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. Accordingly, the disclosure expressly should not be limited to such exemplary embodiments illustrating some possible non-limiting combination of features that may exist alone or in other combinations of features.

[0033] In the description of embodiments disclosed herein, any reference to direction or orientation is merely intended for convenience of description and is not intended in any way to limit the scope of the present invention. Relative terms such as "lower," "upper," “horizontal,” “vertical,”, “above,” “below,” “up,” “down,” “top” and “bottom” as well as derivative thereof (e.g., “horizontally,” “downwardly,” “upwardly,” etc.) should be construed to refer to the orientation as then described or as shown in the drawing under discussion. These relative terms are for convenience of description only and do not require that the apparatus be constructed or operated in a particular orientation. Terms such as “attached,” “affixed,” “connected,” “coupled,” “interconnected,” and similar refer to a relationship wherein structures are secured or attached to one another either directly or indirectly through intervening structures, as well as both movable or rigid attachments or relationships, unless expressly described otherwise.

[0034] Figure 1 depicts an exemplary embodiment of a skin detection and prevention system 100 for receiving a photograph generated on a user’s smartphone and analyzing the photograph to identify skin damage, skin cancer, and to provide a recommended custom treatment plan based on a generated skin score for the user. Unlike traditional methods of analyzing a user’s skin to determine skin damage, the present disclosure does not require expensive hardware or equipment. Furthermore, the skin detection and prevention system 100 of the present disclosuredoes not require the user to use or purchase any external hardware outside of using a smartphone with a camera. This configuration of the system 100 allows for users to monitor their skin’s health on a daily or weekly basis, versus traditional means of quarterly or annually. This increase in frequency of inspection promotes healthier skin, less wrinkles, and increased overall health for the user.

[0035] The skin damage detection and prevention system 100 of FIG. 1 requires a user to first take a photograph of the skin area they would like to have inspected. In some embodiments, the skin area of interest is a user’s face, however in other embodiments, the skin damage detection and prevention system 100 can be used to analyze skin anywhere on the body, as long as a high-resolution photograph of the area can be provided. This differs from traditional systems which are designed specifically to receive and scan the user’s face.

[0036] Instead of requiring expensive hardware that generates individual images in different formats to detect different forms of skin damage, such as an individual machine for generating a UV photograph of the user, another machine or a separate filter to generate a cross-polarized photograph, and another machine or additional filter to generate a parallel-polarized photograph, the present disclosure only requires one photograph in the traditional RGB format.

[0037] After uploading the photograph 10 to the system 100, the image converter module 200 will convert the photograph 10, which is formatted as an RGB photograph, into one or more formats (as shown for example in FIG. 2). For example, in some embodiments, the image converter module 200 will convert the photograph 10 into a cross-polarized photograph 16. In some embodiments, the image converter module 200 will convert the photograph 10 into a parallel-polarized photograph 18. In some embodiments, the image converter module 200 will convert the photograph into both a cross-polarized photograph 16 and a parallel-polarized photograph 18. In some embodiments, a UV photograph 14 will be generated from the received RGB photograph or in some embodiments, using the cross-polarized photograph 16. In some embodiments, to obtain for example a UV photograph, the image converter module 200 will convert the received RGB photograph 10 into a cross-polarized photograph 16, then the module 200 will run the cross-polarized photograph 16 through a conditional generative adversarial network (CGAN) 220 and / or a Residual Neural Network (RESNET) 230 to generate a UV photograph 14. In some embodiments, the cross-polarized photograph 16 and the parallel polarized photograph 18 will be ran through a CGAN deep learning model 220 to generate synthetic images to represent various damaged skin areas that are present in the initially received photograph 10. For example, in some embodiments, the CGAN 220 will be trained to identify UV damage, however in other embodiments, the CGAN 220 can be trained to detectand generate a synthetic image that represents surface spots 20, brown spots 21, redness 22 / 23, wrinkles 24, pore size 25, and skin texture 26; and wherein some embodiments, the CGAN is trained to detect and generate a combination of the above identified categories. Therefore, in some embodiments, the CGAN 220 allows a user to view and detect damaged skin areas that are otherwise unable to be viewed without expensive equipment. To further increase the accuracy of the system 100, in some embodiments, the synthetic images generated by the CGAN 220 are also ran through a RESNET 230, where the RESNET is used to overcome any deficiencies identified in the CGAN in order to generate the full synthetic image (as shown for example in FIG. 3).

[0038] Each of the different formats, UV, cross-polarized, and parallel-polarized, help to characterize and display different types of skin damage. Therefore, in some embodiments of the present disclosure, the skin damage detection and prevention system 100 will generate all 3 types of photographs from the initially received RGB photograph. Thereby allowing the system 100 to detect pigmentation problems, early signs of skin damage, vascular conditions such as redness, rosacea, broken capillaries, spider veins, and vascular lesions, sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas on the skin.

[0039] After one or more of the photographs 14, 16, 18 have been generated, the image analyzer module 300 receives the converted photographs 14, 16, 18 from the image converter module 200, and begins to analyze the photographs 14, 16, 18 for their respective damaged skin areas (as shown for example in FIG. 4). In some embodiments, the skin analyzer module 300 will analyze the UV photograph to detect pigmentation problems and / or early signs of skin damage. In some embodiments, the skin analyzer module 300 will analyze the cross-polarized image to detect blood vessels and vascular conditions such as redness, rosacea, broken capillaries, spider veins, and vascular lesions. In some embodiments, the cross-polarized image 16 can also be used to detect sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles and fine lines related to collagen loss, and uneven skin tones. In some embodiments, the skin analyzer module 300 will analyze the parallel-polarized photograph 18 to detect surface texture irregularities such as fine lines, pore sizes, texture imperfections, surface wrinkles and oily areas on the skin. However, a person having ordinary skin in the art would understand that each photograph 14, 16, 18 may be used to detect and identify additional types of skin damage than those identified above.

[0040] While analyzing each of the different types of photographs 14, 16, 18, the image analyzer module 300 will then identify and amend the photographs 14, 16, 18 to highlight theidentified damaged skin areas 302. As shown in FIG. 3, the damaged skin areas, 302 can be highlighted through increasing the contrast of the pixels related or a shape can be drawn on the photograph 14, 16, 18 to signify the damaged areas 302. In some embodiments, the highlighted damaged areas 302 may be marked and identified based on the type of skin damage that is present in that area. For example, in some embodiments, the image analyzer module 300 will distinguish between damaged skin areas 302 relating to wrinkles versus damaged skin areas 302 relating to hyperpigmentation. A person having ordinary skill in the art would understand that the skin analyzer module 300 will distinguish and uniquely highlight damaged areas 302 that display all types of skin damage described above and including but not limited to pigmentation problems, early signs of skin damage, vascular conditions such as redness, rosacea, broken capillaries, spider veins, and vascular lesions, sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas on the skin. In some embodiments, as shown for example in FIG. 3, the damaged skin areas 302 are outlined in a contrasting color, the individual pixels are contrasted, or in some embodiments, a combination of both. In some embodiments, areas 302 may be colored to represent the severity of the damage identified. In some embodiments, the colors may be a gradient scale. Once the photographs 14, 16, 18 have been analyzed and annotated to identify the respective damaged skin areas 302, the newly annotated photographs 14, 16, 18 will be sent to the scoring module 400.

[0041] The skin scoring module 400 will receive the annotated photographs 14, 16, 18 from the image analyzer module 300 and the data associated with each photograph 14, 16, 18 relating to the damaged skin areas 302, and then the skin scoring module 400 will begin to generate a skin score 402. The skin score 402 is a weighted scoring system that considers the following factors: a user’s age, pigmentation score, wrinkle depth, redness index, pore size, and severity of each category. In some embodiments, the pigmentation score is calculated based on the number of pixels that displayed hyperpigmentation, sun damage, freckles, melasma, and aging spots. For example, in some embodiments, the scoring module 400 can assign a lower score (indicating more damage) if larger areas of uneven pigmentation are detected, or if there are clusters of intense hyperpigmentation. In some embodiments, the wrinkle depth is calculated based on the average depth of wrinkles present in key areas of the face (including but not limited to around the eyes, forehead, and mouth). For example, key areas that include deeper and more widespread wrinkles would lead to a higher wrinkle depth score. In some embodiments, the redness index would assess the degree of redness in the skin, indicating inflammation, irritation, or vascular issues. In some embodiments, the redness index iscalculated by measuring the area and intensity of red patches. For example, in some embodiments, the more severe redness over larger areas would increase the damage score. In some embodiments, the scoring module 400 can include a weighted multiplier on the redness index based on if the redness is temporary (due to irritation) or chronic. In some embodiments, the pore size is calculated based on the size and density of pores on the skin of the photographed area. For example, in some embodiments, a higher score would indicate larger or more visible pores, often associated with skin damage or issues such as acne.

[0042] In some embodiments, the severity category can be included in the skin scoring module 400 as weighted system to account for categories that need immediate attention by the user. For example, in some embodiments, the severity for the pigmentation score would factor in the number of dark spots, their size, and intensity (quantity over area). In some embodiments, the severity for wrinkle depth would factor average depth and coverage areas of all wrinkles identified. In some embodiments, redness index would factor in the intensity (quantity per area) and which areas are affected (giving priority to key areas such as nose, forehead, and cheeks. In some embodiments, the severity for pore size would factor the average size and number of enlarged pores that exceed a threshold size. After calculating scores for each of the identified categories and factoring in the severity of each category, the skin scoring module 400 would calculate a composite skin health score 402. As shown in FIG. 5, the system 100 will then provide the skin health score 402 to the user and attach an overall skin health descriptor 404 with the skin health score 402. In some embodiments, the overall skin health descriptor 404 will include ranges of skin health scores from 1-100 where 1-25 is described as severe, 26-50 is described as moderate, 51-75 is described as mild, and 76-100 is described as great. In some embodiments, the amount of skin health descriptors will be 10 and the ranges of skin scores 402 included with each descriptor will include increments of 10 points. In addition to the overall skin health score 402, in some embodiments, the skin scoring module 400 will provide a detailed breakdown 406 of each of the skin score categories identified and described above. For example, in some embodiments, the detailed breakdown of the pigmentation score might include the pigmentation score and a description of any areas that need attention or heavily affected the score (e.g. high pigmentation detected in the cheek area).

[0043] In some embodiments, after the skin score 402 has been generated, a custom treatment plan 502 will be generated by the treatment module 500. In some embodiments, the treatment module 500 communicates with a database 700 that includes one or more treatment plans 730. In some embodiments, database 700 includes a catalogue of skin care and cosmetic products, their associated ingredients, and correlations between each of the products within the catalogto one or more treatments for the above identified types of skin damages. In some embodiments, the one or more treatment plans 730 include treatments that are designed to treat each of the identified types of skin damage described above. For example, if the skin score 402 describes the user as having a poor pigmentation score due to high pigmentation detected in the cheek area, the treatment module 500 will communicate with database 700 to identify possible treatments for high pigmentation, or in some embodiments, high pigmentation in the cheek area. The treatment module 500 will then return a tailored treatment plan 502 for the user based on the skin score generated by the scoring module 400. For example, in some embodiments, the recommended treatment for a user with high pigmentation might include skin care products for pigmentation correction or light therapy devices, along with regime details for applying the products or the light therapy. In some embodiments, the light therapy is low-level laser therapy (LLLT). In some embodiments, the treatment module 500 will conduct these steps for each of the identified categories in the skin score 402 that need attention.

[0044] In some embodiments, the skin damage detection and prevention system 100 further includes a training module 600 for further increasing the accuracy of detection, identification, and recommended treatment for a user. In some embodiments, the training module 600 utilizes machine learning and artificial intelligence for further refining the image converter module 200, the image analyzer module 300, the scoring module 400, and the treatment module 500. In some embodiments, the training module 600 can receive a plurality of training images that are pre-converted into the specified formats (UV, cross-polarized, and parallel-polarized) and where the damaged skin areas are already marked and highlighted such that the training module can learn to associate the provided photographs with each of the identified types of skin damage. In some embodiments, artificial intelligence and machine learning are used to further the interpretation of the converted photographs 14, 16, 18 received by the image converter module 200 such that more accurate detection of skin damage can be achieved. In some embodiments, artificial intelligence (Al) and machine learning (ML) is used in the image converter module 200 to allow the system 100 to accommodate lower resolution photographs that a user may submit. For example, if a user submits a lower resolution photograph and there are areas of the photograph that the existing image converter module 200 cannot convert to the new format (UV, cross-polarized, and parallel-polarized), the training module 600 will utilize ML or Al to compare training photographs and previously converted images to extrapolate the newly received photograph into the new format (UV, cross-polarized, and parallel-polarized).

[0045] In some embodiments, the skin damage detection and prevention system 100 further includes a mobile application or API for a user to upload the initial photograph 10, view theskin score 402, and provide the treatment plan 502. In some embodiments, the mobile application or API provides a user interface that provides a detailed breakdown of the skin score 402 along with product recommendations that can be used to treat the problem areas identified in the skin score 402.

[0046] In some embodiments, the skin damage detection and prevention system 100 further includes one or more accessories that can be added to the system to boost accuracy of the system 100. For example, in some embodiments, the system 100 can include a polarizing filter for reducing glare on the initial photograph 10. The reduced glare allows the system 100 to capture clearer images of the underlying skin structure, making it easier to detect damage areas such as pigmentation, wrinkles, and pore size. In some embodiments, the system 100 can include a macro lens for increasing the clarity and details of the initial photograph 10. In some embodiments, the macro lens can allow the system 100 to more accurately detect fine wrinkles, pores, and other minute skin features by zooming in on the skin’ s surface without losing clarity. In some embodiments, the system 100 can include external lighting for providing even illumination of the skin area, reducing shadows, and minimizing glare. In some embodiments, the external lighting can allow the system to more consistently analyze the skin across multiple sessions, ensuring accurate detection of changes over time.

[0047] An example of the skin damage detection and prevention system 100 of the present disclosure includes the following steps and is shown and described in FIG. 6: First, (110) a user takes a photograph 10 of a designated skin area that they would like to be analyzed. Then, (120) the initial photograph 10 is uploaded to the system 100, and (210) the image converter module 200 receives the photograph 10. Then, (220) the image converter module 200 converts the initial photograph 10 into one or more of the following formats, UV 14, cross-polarized 16, and parallel-polarized 18. Then, (310) the image analyzer module 300 receives the one or more photographs 14, 16, 18 from the image converter module 200. After receiving the photographs, (320) the image analyzer module 300 analyzes the newly generated photographs 14, 16, 18 for their respective skin damage types. Then, (330) the image analyzer module 300 identifies the damaged skin areas 302, and (340) classifies the damaged skin areas 302 according to the types of damage present and annotates and / or highlights the damaged skin areas 302 based on the types of damage identified in the photographs. Once all of the photographs have been analyzed, (410) the scoring module 400 receives the annotated photographs 14,16, 18 and the associated data from the image analyzer module 300. After receiving the photographs and the data, the skin scoring module 400 analyzes the data, identified damaged skin areas, severity based on the classifications identified and (420) generates a skin score 402 based on the data analyzed.Lastly, (430) the skin score 402 is presented to the user. In some embodiments, (510) the skin score 402 and associated data is then received by the treatment module 500, and the treatment module 500 analyzes the data and skin score 402, and compares the skin and associated data with data from database 700. Then, (520) treatment module 500 generates a custom treatment plan based on the skin score data associated with each identified damaged skin area 302. Lastly, (610) the training module 600 updates database 700 to further include the results generated by the scoring module 400 and treatment module 500 for more accurate results on the next user session. Although the following example provided a step-by-step procedure that the present disclosure may take to provide more accurate skin analysis to a user without the requirement of hardware, a person having ordinary skill in the art would understand that some of the steps identified above may be replaced, modified, removed, or rearranged to better optimize the system or to tailor to a specific area of the body.

[0048] While the foregoing description and drawings represent exemplary embodiments of the present disclosure, it will be understood that various additions, modifications and substitutions may be made therein without departing from the spirit and scope and range of equivalents of the accompanying claims. In particular, it will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, sizes, and with other elements, materials, and components, without departing from the spirit or essential characteristics thereof. In addition, numerous variations in the methods / processes described herein may be made within the scope of the present disclosure. One skilled in the art will further appreciate that the embodiments may be used with many modifications of structure, arrangement, proportions, sizes, materials, and components and otherwise, used in the practice of the disclosure, which are particularly adapted to specific environments and operative requirements without departing from the principles described herein. The presently disclosed embodiments are therefore to be considered in all respects as illustrative and not restrictive. The appended claims should be construed broadly, to include other variants and embodiments of the disclosure, which may be made by those skilled in the art without departing from the scope and range of equivalents.

Claims

CLAIMSWhat is claimed is:

1. A skin damage detection and prevention system comprising: an image converter module for receiving a first photograph having a first format and converting the first photograph to at least one or more new photographs, wherein the format for the one or more new photographs is different from the first format; an image analyzer module for receiving the one or more new photographs and analyzing the one or more new photographs to identify one or more damaged skin areas within each of the one or more new photographs, and wherein the image analyzer module classifies each of the one or more damaged skin areas according to the type of skin damage present in the area; and a scoring module for receiving the analyzed one or more new photographs and generating a skin score based on the one or more types of skin damage and the severity present in the one or more new photographs.

2. The skin damage detection and prevention system of claim 1, wherein the first format of the first photograph is RGB.

3. The skin damage detection and prevention system of claim 1, wherein the one or more new photographs are in the format selected from the group consisting of UV, cross-polarized, and parallel polarized.

4. The skin damage detection and prevention system of claim 1, wherein the first photograph is generated from a camera of a smartphone.

5. The skin damage detection and prevention system of claim 1, wherein the type of skin damage present in the identified one or more damaged skin areas are selected from the group consisting essentially of irregular pigmentation, redness, rosacea, broken capillaries, spider veins, sun damage, hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas.

6. The skin damage detection and prevention system of claim 1, wherein the image analyzer module annotates the one or more new photographs to highlight each damaged skin area.

7. The skin damage detection and prevention system of claim 6, wherein the annotation for the one or more new photographs is selected from the group consisting of altering pixel contrast and adding a shape outline.

8. The skin damage detection and prevention system of claim 7, wherein the different types of skin damage identified by the image analyzer module are annotated in different colors, and wherein the different colors for each type of skin damage are shown on a gradient scale to depict severity of the highlighted skin damage area.

9. The skin damage detection and prevention system of claim 1, further comprising a training module for further increasing the accuracy of detection, identification, and recommended treatment; and wherein the training module employs a trained neural network model configured to process pixel-level data of the one or more new photographs and to identify patterns associated with the different types of skin damage, and wherein the training module is trained using a dataset of annotated skin images to improve accuracy in classifying, identifying, and quantifying the one or more damaged skin areas.

10. The skin damage detection and prevention system of claim 1, wherein the scoring module assesses factors selected from the group consisting of a user’ s age, pigmentation, wrinkle depth, redness index, and pore size.

11. The skin damage detection and prevention system of claim 1, wherein the severity is assessed based on factors selected from the group consisting of density of skin damage type, wrinkle depth, intensity, and location.

12. The skin damage detection and prevention system of claim 1, further comprising: a treatment module for receiving the skin score from the scoring module and generating a treatment plan based on the one or more types of skin damage identified by the image analyzer module.

13. The skin damage detection and prevention system of claim 12, wherein the treatment plan is developed based on factors selected from the group consisting of skin score, severity of the one or more damaged skin areas, location of skin damage, type of skin damage, and the overall skin score.

14. The skin damage detection and prevention system of claim 13, wherein the treatment plan is displayed to the user on a graphical interface of a mobile device15. A computer-implemented method for detecting and assessing skin damage for a user on a mobile device, the computer implemented method comprising: receiving, from the user, a first photograph, of the user’ s skin, in a first format; converting the first photograph, received in the first format, to one or more new photographs each having a different format than the first format; analyzing the one or more new photographs to identify one or more damaged skin areas; classifying the one or more identified damaged skin areas based on the type of skin damage present in the one or more damaged skin areas; generating a skin score based upon the one or more identified damaged skin areas and the types of skin damage present in the one or more areas; and displaying, the skin score to the user on the mobile device.

16. The computer-implemented method of claim 15, further comprising: recommending a treatment plan to the user based on the one or more types of skin damage identified.

17. The computer-implemented method of claim 15, wherein the first format of the first photograph is RGB.

18. The computer-implemented method of claim 15, wherein the one or more new photographs are in the format selected from the group consisting of UV, cross-polarized, and parallel polarized.

19. The computer-implemented method of claim 15, wherein the type of skin damage present in the identified one or more damaged skin areas are selected from the group consisting of irregular pigmentation, redness, rosacea, broken capillaries, spider veins, sun damage,hyperpigmentation, freckles, melasma, aging spots, porphyrins, deep wrinkles, uneven skin tone areas, surface texture, pore size, fine lines, surface wrinkles, and oily areas.

20. The computer-implemented method of claim 15, wherein the generated skin score is based on factors selected from the group consisting of a user’s age, pigmentation, wrinkle depth, redness index, and pore size.

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