Systems, methods, and computer programs, for analyzing images of a portion of a person to detect a severity of a medical condition

A data processing system for detecting an occurrence of an auto-immune condition, comprising:

• one or more computers;
• and
• one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform the operations comprising:
• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• providing, by the one or more computers, the data representing the first image as an input to a machine learning model that has been trained to determine a likelihood that image data processed by the machine learning model depicts skin of a person having the auto-immune condition;
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the data representing the first image, the output data representing a likelihood that the first image depicts skin of a person having the auto-immune condition;
• and
• determining, by the one or more computers, whether the person has the auto-immune condition based on the obtained output data.

A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations comprising:

• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• providing, by the one or more computers, the data representing the first image as an input to a machine learning model that has been trained to determine a likelihood that image data processed by the machine learning model depicts skin of a person having the auto-immune condition;
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the data representing the first image, the output data representing a likelihood that the first image depicts skin of a person having the auto-immune condition;
• and
• determining, by the one or more computers, whether the person has the auto-immune condition based on the obtained output data.

A method for monitoring skin condition of a person, the method comprising:

• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• generating, by the one or more computers, a severity score that indicates a likelihood that the person is trending towards an increased severity of an auto-immune condition or trending towards a decreased severity of an auto-immune condition, wherein generating the severity score includes:
• providing, by the one or more computers, the data representing the first image as an input to a machine learning model that has been trained determine a likelihood that image data processed by the machine learning model depicts skin of a person having the auto-immune condition;
• and
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the data representing the first image, the output data representing a likelihood that the first image depicts skin of a person having the auto-immune condition, wherein the output data generated by machine learning model is the severity score;
• comparing, by the one or more computers, the severity score to a historical severity score, wherein the historical severity score is indicative of a likelihood that a historical image of the user depicts skin of a person having the auto-immune condition;
• and
• determining, by the one or more computers and based on the comparison, whether the person is trending towards an increased severity of the auto-immune condition or trending towards a decreased severity of the auto-immune condition.

A data processing system for monitoring skin condition of a person, comprising:

• one or more computers;
• and
• one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform the operations comprising:
• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• generating, by the one or more computers, a severity score that indicates a likelihood that the person is trending towards an increased severity of an auto-immune condition or trending towards a decreased severity of an auto-immune condition, wherein generating the severity score includes:
• providing, by the one or more computers, the data representing the first image as an input to a machine learning model that has been trained determine a likelihood that image data processed by the machine learning model depicts skin of a person having the auto-immune condition;
• and
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the data representing the first image, the output data representing a likelihood that the first image depicts skin of a person having the auto-immune condition, wherein the output data generated by machine learning model is the severity score;
• comparing, by the one or more computers, the severity score to a historical severity score, wherein the historical severity score is indicative of a likelihood that a historical image of the user depicts skin of a person having the auto-immune condition;
• and
• determining, by the one or more computers and based on the comparison, whether the person is trending towards an increased severity of the auto-immune condition or trending towards a decreased severity of the auto-immune condition.

A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations comprising:

• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• generating, by the one or more computers, a severity score that indicates a likelihood that the person is trending towards an increased severity of an auto-immune condition or trending towards a decreased severity of an auto-immune condition, wherein generating the severity score includes:
• providing, by the one or more computers, the data representing the first image as an input to a machine learning model that has been trained determine a likelihood that image data processed by the machine learning model depicts skin of a person having the auto-immune condition;
• and
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the data representing the first image, the output data representing a likelihood that the first image depicts skin of a person having the auto-immune condition, wherein the output data generated by machine learning model is the severity score;
• comparing, by the one or more computers, the severity score to a historical severity score, wherein the historical severity score is indicative of a likelihood that a historical image of the user depicts skin of a person having the auto-immune condition;
• and
• determining, by the one or more computers and based on the comparison, whether the person is trending towards an increased severity of the auto-immune condition or trending towards a decreased severity of the auto-immune condition.

A method for detecting an occurrence of a medical condition, the method comprising:

• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• identifying, by the one or more computers, a historical image that is similar to the first image;
• determining, by the one or more computers, one or more attributes of the historical image that are to be associated with the first image;
• generating, by the one or more computers, a vector representation of the first image that includes data describing the one or more attributes;
• providing, by the one or more computers, the generated vector representation of the first image as an input to the machine learning model that has been trained to determine a likelihood that image data processed by the machine learning model depicts skin of a person having the medical condition;
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the generated vectored representation of the first image;
• and
• determining, by the one or more computers, whether the person is associated with the medical condition based on the obtained output data.

A data processing system for detecting an occurrence of a medical condition, comprising:

• one or more computers;
• and
• one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform the operations comprising:
• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• identifying, by the one or more computers, a historical image that is similar to the first image;
• determining, by the one or more computers, one or more attributes of the historical image that are to be associated with the first image;
• generating, by the one or more computers, a vector representation of the first image that includes data describing the one or more attributes;
• providing, by the one or more computers, the generated vector representation of the first image as an input to the machine learning model that has been trained to determine a likelihood that image data processed by the machine learning model depicts skin of a person having the medical condition;
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the generated vectored representation of the first image;
• and
• determining, by the one or more computers, whether the person is associated with the medical condition based on the obtained output data.

A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform the operations comprising:

• obtaining, by one or more computers, data representing a first image, the first image depicting skin from at least a portion of a body of a person;
• identifying, by the one or more computers, a historical image that is similar to the first image;
• determining, by the one or more computers, one or more attributes of the historical image that are to be associated with the first image;
• generating, by the one or more computers, a vector representation of the first image that includes data describing the one or more attributes;
• providing, by the one or more computers, the generated vector representation of the first image as an input to the machine learning model that has been trained to determine a likelihood that image data processed by the machine learning model depicts skin of a person having the medical condition;
• obtaining, by the one or more computers, output data generated by the machine learning model based on the machine learning model processing the generated vectored representation of the first image;
• and
• determining, by the one or more computers, whether the person is associated with the medical condition based on the obtained output data.