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PHP SDK for integrate with the Dermatology API offered by Legit.Health

Official SDK for integrate with the Dermatology API offered by Legit.Health 🩺🤖

Instructions

If you want to start sending requests to Legit.Health's Dermatology API, you have to create an instance of the class LegitHealth\Dapi\MediaAnalyzer. It requires two arguments:

• The URL of the API. The preproduction enviroment uses the following value: https://ai-pre.legit.health.
• The API Key we have provided to you. Without it, you won't be able to send requests to the API.

The class MediaAnalyzer exposes two methods:

• diagnosisSupport, to send a diagnosis support request to the API, in case you need to analyze a set of images to obtain the probability of the detected pathologies.

• severityAssessment, to send a severity assessment up request to get the evolution information about a diagnosed image.

Diagnosis support requests

The diagnosisSupport method of our MediaAnalyzer class receives one argument of the class LegitHealth\Dapi\MediaAnalyzerArguments\DiagnosisSupportArguments. The constructor of this class receives an object of the class LegitHealth\Dapi\MediaAnalyzerArguments\DiagnosisSupportData, in which you can specify the image itself and information about the patient or the body site:

Once you've created a DiagnosisSupportData object, you can send the request in this way:

The response object contains several properties with the information returned by the API about the analyzed image:

• preliminaryFindings is an object of the class LegitHealth\Dapi\MediaAnalyzerResponse\PreliminaryFindingsValue with the probability of the different suspicions that the algorithm has about the image.

• metrics contains the sensitivity and specificity values.

• conclusions is an array of Conclusion objects with the detected pathologies and its probability. The total probability is distributed among each of the pathologies detected.

• observations is an array of Observation objects with the conclusions of the algorithm for each image including its related metrics and preliminary findings.

• iaSeconds is the time spent by the algorithms analyzying the image.

Severity assessment requests

The severityAssessment method of our MediaAnalyzer class receives one argument of the class LegitHealth\Dapi\MediaAnalyzerArguments\SeverityAssessmentArguments. The constructor of this class receives an object of the class LegitHealth\Dapi\MediaAnalyzerArguments\SeverityAssessmentData, in which can specify the image itself and information about a well known condition.

Example. Severity assessment request for psoriasis

Let's see how to send a severity assessment request for a patient diagnosed with psoriasis.

Firstly, we will create the different objects that represents the questionnaires used to track the evolution of psoriasis:

Then, we will create an object of the class LegitHealth\Dapi\MediaAnalyzerArguments\SeverityAssessmentData:

Unlike diagnostic support requests, severity assessment requests supports the following additional arguments:

• previousMedias is an array of objects of the class PreviousMedia with a list of previous images taken of the current pathology.

• scoringSystems is an array of strings with the name of the scoring systems to be evaluated. It supports the following values:

• questionnaires is an object of the class LegitHealth\Dapi\MediaAnalyzerArguments\Questionnaires\Questionnaires with the values of the scoring systems to be evaluated.

Once you've created a SeverityAssessmentData object, you can send the request in this way:

The response object contains several properties with the information returned by the API about the analyzed image:

• preliminaryFindings is an object of the class LegitHealth\Dapi\MediaAnalyzerResponse\PreliminaryFindingsValue with the probability of the different suspicions that the algorithm has about the image.

• modality is the modality of the image detected.

• mediaValidity is an object that contains information about whether the image sent contains relevant dermatological information

• metricsValue contains the sensitivity and specificity values.

• iaSeconds is the time spent by the algorithms analyzying the image.

Besides, it contains two extra properties:

• explainabilityMedia, with the image containing the surface of the injury detected by our AI algorithms.

• scoringSystemsValues, an object of the class LegitHealth\Dapi\MediaAnalyzerResponse\ScoringSystem\ScoringSystemValues.php with the values calculated for each scoring system included in the array scoringSystems of the arguments.

The ScoringSystemValues object

The ScoringSystemValues contains all the information about a scoring system, for example, APASI_LOCAL.

You can access to the value of one scoring system using the method getScoringSystemValues:

Once you have one object of the class ScoringSystemValues, you can access to the value of each facet using the method getFacetCalculatedValue(string $facetCode).

By invoking the method getFacets you will get an array of facets. Each element in this list is an array with three keys:

• facet. The facet code.
• value. The calculated value for the facet. This value will be inside the allowed range for the facet.
• intensity. It represents the intensity of that facet in a scale from 0 to 100.

Finally, you can access to the score of the scoring system through its property score. It is an object with three properties:

• category, which represents the severity of the score.
• calculatedScore, for those scoring systems whose calculation depends on facets that are computed by our AI algorithm: APASI_LOCAL, APULSI, ASCORAD_LOCAL and AUAS_LOCAL.
• questionnaire, for those scoring systems whose calculations not depends on facet computed by our AI algorithm, for example, DLQI.

Full example:

Detecting faces

In some cases, you may want to enable the feature of the algorithm capable of detecting faces. In this case, a metric called pxToCm is returned allowing to get the ratio of conversion from pixels to centimeters. This feature works for both diagnosis support and severity assessment requests.

For example, if you are working with a DiagnosisSupportData object, you can send the request in this way:

If the algorithm detects a face and can calculate the ratio from pixels to centimeters, the property metrics of the explainabilityMedia will get a value different of null