Systems, methods, and computer-readable media for determining a likely presence of a genetic disorder

1. An electronic system for determining from a series of pixels in an image of external cranio-facial soft tissue whether a subject is likely to be affected by a medical condition, the system comprising:

at least one memory for storing computer-executable instructions; and

at least one processor configured to execute the stored instructions to:

receive first electronic information reflective of first values corresponding to pixels of the cranio-facial external soft tissue image of the subject, wherein the first values correspond to relationships between at least one group of pixels in the cranio-facial soft tissue image of the subject;

analyze the first sets of values in the first electronic information for one or more dysmorphologies present in the cranio-facial external soft tissue image of the subject;

identify a first dysmorphology and a medical feature;

determine whether a first association exists between the first dysmorphology and the medical condition, and whether a second association exists between the medical feature and the medical condition;

determine a first strength of the first dysmorphology as a predictor of the medical condition based on a commonality of the first dysmorphology with a general population of individuals who do not have the medical condition, and a second strength of the medical feature as a predictor of the medical condition based on a commonality of the medical feature amongst the general population of individuals who do not have the medical condition; and

calculate a likelihood that the subject is affected by the medical condition by weighting the first dysmorphology as a function of the first strength of the first dysmorphology, and by weighting the first dysmorphology and the medical feature as a function of the second strength of the first dysmorphology and the medical feature.

2. The electronic system of claim 1 , wherein the first strength of the first dysmorphology is higher than the second strength of the medical feature when the first dysmorphology is less common than the medical feature amongst the general population of individuals who do not have the medical condition.

3. The electronic system of claim 2 , wherein the at least one processor is further configured to assign a severity score to the first dysmorphology and the medical feature.

4. The electronic system of claim 3 , wherein calculating the likelihood that the subject is affected by the medical condition further includes weighting the first dysmorphology and medical feature as a function of respective severity scores for the first dysmorphology and medical feature.

5. A computer-implemented method for determining from a series of pixels in an image of external cranio-facial soft tissue whether a subject is likely to have a medical condition, the computer-implemented method comprising:

receiving, with processing circuitry, first electronic information reflective of first values corresponding to pixels of the cranio-facial external soft tissue image of the subject, wherein the first values correspond to relationships between at least one group of pixels in the cranio-facial soft tissue image of the subject;

analyzing, with the processing circuity, the first sets of values in the first electronic information for one or more dysmorphologies present in the cranio-facial external soft tissue image of the subject;

identifying, with the processing circuitry, a first dysmorphology and a medical feature;

determining, with the processing circuitry, whether a first association exists between the first dysmorphology and the medical condition, and whether a second association exists between the medical feature and the medical condition;

determining, with the processing circuitry, a first strength of the first dysmorphology as a predictor of the medical condition based on a commonality of the first dysmorphology with a general population of individuals who do not have the medical condition, and a second strength of the medical feature as a predictor of the medical condition based on a commonality of the medical feature amongst the general population of individuals who do not have the medical condition; and

calculating, with the processing circuitry, a likelihood that the subject is affected by the medical condition by weighting the first dysmorphology as a function of the first strength of the first dysmorphology, and by weighting the first dysmorphology and the medical feature as a function of the second strength of the first dysmorphology and the medical feature.

6. The computer-implemented method of claim 5 , wherein the first strength of the first dysmorphology is higher than the second strength of the medical feature when the first dysmorphology is less common than the medical feature amongst the general population of individuals who do not have the medical condition.

7. The computer-implemented method of claim 6 , further comprising assigning, with the processing circuitry, a severity score to the first dysmorphology and the medical feature.

8. The computer-implemented method of claim 7 , wherein calculating the likelihood that the subject is affected by the medical condition further includes weighting the first dysmorphology and the medical feature as a function of the respective severity scores of the first dysmorphology and the medical feature.

9. A non-transitory computer-readable medium for determining from a series of pixels in an image of external cranio-facial soft tissue whether a subject is likely to be affected by a medical condition, which comprises instructions that, when executed by at least one processor, cause the at least one processor to perform operations including:

receiving first electronic information reflective of first values corresponding to pixels of the cranio-facial external soft tissue image of the subject, wherein the first values correspond to relationships between at least one group of pixels in the cranio-facial soft tissue image of the subject;

analyzing the first sets of values in the first electronic information for one or more dysmorphologies present in the cranio-facial external soft tissue image of the subject;

identifying a first dysmorphology and a medical feature;

determining whether a first association exists between the first dysmorphology and the medical condition, and whether a second association exists between the medical feature and the medical condition;

determining a first strength of the first dysmorphology as a predictor of the medical condition based on a commonality of the first dysmorphology with a general population of individuals who do not have the medical condition, and a second strength of the medical feature as a predictor of the medical condition based on a commonality of the medical feature amongst the general population of individuals who do not have the medical condition; and

calculating a likelihood that the subject is affected by the medical condition by weighting the first dysmorphology as a function of the first strength of the first dysmorphology, and by weighting the first dysmorphology and the medical feature as a function of the second strength of the first dysmorphology and the medical feature.

10. The non-transitory computer-readable medium of claim 9 , wherein the first strength of the first dysmorphology is higher than the second strength of the medical feature when the first dysmorphology is less common than the medical feature amongst the general population of individuals who do not have the medical condition.

11. The non-transitory computer-readable medium of claim 10 , wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform additional operations including assigning a severity score to the first dysmorphology and the medical feature.

12. The non-transitory computer-readable medium of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the at least one processor to perform additional operations including calculating the likelihood that the subject is affected by the medical condition further includes weighting the first dysmorphology and the medical feature as a function of respective severity scores of the first dysmorphology and the medical feature.