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Skin blemish API

Skin blemishes are a general term for any mark or discoloration on the skin. A variety of factors, including melasma, acne, sun damage, and aging can cause them. This API estimates and names the colour of the blemishes on the skin. The API crops the central 256*256 window from the image for processing.

The estimate is approximate and this is useful only for cosmetic purposes.

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Image requirements:

Submit a close-up image with neutral lighting centred on the area to be assessed. If the image is larger than 512*512, only the central 512*512 pixels will be processed. Try it out a few times to get it right. Visit RapidAPI for instructions.

Output

Example response: {“colour”: {“dimgray”: 27.59, “khaki”: 17.24, “rosybrown”: 13.79, “moccasin”: 13.79}}

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Hair density API for LASER

Measuring hair density is a common way to assess the effectiveness of laser hair removal in reducing unwanted hair. Hair density is typically measured before and after a series of laser hair removal sessions to evaluate the extent of hair reduction achieved. Visual assessments involve examining the treated area and comparing it to baseline photographs taken prior to treatment. This API applies a series of morphological operations to the image to generate a mask containing the hairs and counts the number of pixels in the mask. The API crops the central 256*256 window from the image for processing.

The estimate is approximate and this is useful only for cosmetic purposes.

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Image requirements:

Submit an image with only skin and hair. Take a close-up image with neutral lighting centred on the area to be assessed. If the image is larger than 256*256, only the central 256*256 pixels will be processed. Try it out a few times to get it right. Visit RapidAPI for instructions.

Output

Example response: {“hair”: 3756, “no_hair”: 61780, “total”: 65536, “score”: 5.73}

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Skin Movement API for BOTOX

Tl;dr: Here is an API for measuring skin/muscle movement in key areas. Share this with your technical team so that they can integrate it with your website/mobile app or electronic medical record systems.

As we age, our skin loses elasticity and collagen, which are essential components that keep the skin firm and smooth. As a result, repeated facial movements, such as squinting, frowning, and smiling, can cause wrinkles and lines to form on the skin’s surface. These wrinkles and lines are most commonly found around the eyes (crow’s feet), forehead and frown lines. They are often referred to as dynamic wrinkles, as they are caused by the repeated movement of facial muscles.

A smile, particularly one that involves the muscles around the eyes (also known as crow’s feet), can create creases and wrinkles on the sides of the eyes. This is because smiling causes the skin to bunch up around the eyes, eventually leading to the development of fine lines and wrinkles over time. Raising eyebrows can create horizontal lines on the forehead, which can deepen with repeated use of this expression. This is because raising the eyebrows involves the use of the frontalis muscle, which pulls the skin upwards and creates the appearance of horizontal lines. Frowning, on the other hand, can create vertical lines or furrows between the eyebrows. This is because frowning involves the use of the corrugator muscles, which pull the eyebrows downwards and create the appearance of vertical lines between the eyebrows.

Botulinum toxin injection, commonly known as Botox, is a medical treatment that involves injecting a small amount of botulinum toxin into specific facial muscles. This toxin works by temporarily paralyzing the muscles, which can reduce the appearance of wrinkles and lines caused by facial movement. Botox is commonly used to treat dynamic wrinkles, such as frown lines between the eyebrows, forehead wrinkles, and crow’s feet around the eyes. It is a popular cosmetic procedure because it can provide quick results with minimal downtime. However, it is important to note that Botox is a temporary solution and the effects typically last for three to six months. Additionally, there are potential risks and side effects associated with the procedure, such as bruising, swelling, and muscle weakness.

If you are considering Botox, it is important to consult with a qualified and experienced medical professional who can evaluate your individual needs and help you make an informed decision about whether the treatment is right for you.

Instructions

This API can measure the distance between key points around your eyes, forehead and frown area.

First, submit a picture to get the baseline values. Then submit pictures with a smile (crow’s feet), raised eyebrows (forehead) and frown. If there is a noticeable movement of the skin, the corresponding values will decrease. For eyes (crow’s feet) and forehead, a decrease of more than 2-3 units is substantial, while a decrease of 1-2 units is a significant frown.

This may be useful for assessing the requirement of BOTOX and grading the effect. The estimate is approximate and this is useful only for cosmetic purposes.

Connect on RapidAPI

Image requirements:

Submit an close-up image facing the camera straight on with neutral lighting. Try it out a few times to get it right. Visit RapidAPI for instructions.

Output

Output includes the distance between facial key points.


{"left_eye": 26.399, "right_eye": 24.617, "left_forehead": 28.811, "right_forehead": 28.152, "frown": 30.727}

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Acne Grading API

[General Instructions for submitting images to SkinHelpDesk]

Important: Try it out a few times to find an appropriate sensitivity (The ValueCode in request. It defaults to 0.5) for your use case and image acquisition system.

Acne is the most commonest ‘disease’, but severe forms are associated with considerable morbidity. Dermatologists grade acne according to severity to decide when to intervene and treat it. As acne is extremely common, computer-assisted acne grading is a popular machine-learning task.

So far the popular approach has been to create CNN-based classification models. It is difficult to train and deploy these models and the clinical utility is limited. There have also been regression-based approaches as the grades are ‘ranks’ than ‘classes’. We adopt an object detection-based approach that categorizes acne lesions into four basic types: comedones, papulopustular, nodulocystic and scars.

After characterizing and counting these subtypes, we use weighted counting to reduce it to a single numeric grade between 0-8. The API returns the submitted image, along with the lesion counts and this single numeric grade.

This API is suitable for cosmetic dermatology service providers to document progress and screen clients to identify severe cases. The detection sensitivity can be adjusted according to the needs.

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Limitation

The scar detection is suboptimal. We will try to improve this in future versions. The backend improvements may not require any changes in the frontend.

Image requirements:

Submit a close-up image of the side of the face with neutral lighting. Important: Try it out a few times to find an appropriate sensitivity (The ValueCode in request. It defaults to 0.5) for your use case and image acquisition system. Visit RapidAPI for instructions.

Output

Output includes the image with the detected lesions boxed. Additionally, the counts of the four lesion subclasses and an overall numeric grade are returned.

Sample output

{"function": "AcneGrading", "image": "data:image/jpeg;base64, /9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUD9k=", "comedone": 17, "papulopustular": 9, "nodulocystic": 0, "scar": 0, "acne_grade": 0}

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Skin Tone API

[General Instructions for submitting images to SkinHelpDesk]

Skin tone, the genetically endowed amount of melanin in the outer skin layer, has got enormous significance both culturally and cosmetically. It is interesting that some consider less to be better while others strive for more. Fitzpatrick’s skin type, though primarily not designed for grading skin tone, is the widely accepted gold standard.

Measuring the amount of melanin is arduous, and estimates from digital image analysis are extremely error-prone. The estimate may vary significantly dependent on lighting and image quality.

This API estimates approximate tone by a pixel counting method after converting the submitted image to an appropriate colour space. The estimate is approximate and this is useful only for cosmetic purposes.

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Image requirements:

Submit an image with only (or predominantly) skin pixels. Take a close-up image with neutral lighting. Try it out a few times to get it right. Visit RapidAPI for instructions.

Output

Output includes the skin tone in ITA. Read more about ITA here. In addition to skin tone, the API also provides an approximate estimate of erythema (redness) and cyanosis (blueness). Please note that both are NOT appropriate for clinical purposes.

Response: In the ‘Result‘ array, look for the ‘Title‘, ‘Detail‘, and the ‘ValueCompute‘ fields.

{"Service":"shdtone","Result":[{"Title":"ita","Detail":"40","Min":-180,"Max":180,"ValueCompute":40},{"Title":"tone","Detail":"Intermediate","Min":0,"Max":0,"ValueCompute":0},{"Title":"erythema","Detail":"8","Min":0,"Max":15,"ValueCompute":8},{"Title":"cyanosis","Detail":"0","Min":0,"Max":15,"ValueCompute":0}],"ValueCode":35}