Systems and methods for processing electronic images to predict lesions

Abstract

Systems and methods are disclosed for predicting the location, onset, or change of coronary lesions from factors like vessel geometry, physiology, and hemodynamics. One method includes: acquiring, for each of a plurality of individuals, a geometric model, blood flow characteristics, and plaque information for part of the individual's vascular system; training a machine learning algorithm based on the geometric models and blood flow characteristics for each of the plurality of individuals, and features predictive of the presence of plaque within the geometric models and blood flow characteristics of the plurality of individuals; acquiring, for a patient, a geometric model and blood flow characteristics for part of the patient's vascular system; and executing the machine learning algorithm on the patient's geometric model and blood flow characteristics to determine, based on the predictive features, plaque information of the patient for at least one point in the patient's geometric model.

1 - 29 . (canceled) 
     
     
         30 . A method for prognosis management based on medical information of a patient, comprising:
 receiving the medical information including at least a medical image of the patient reflecting a morphology of an object associated with the patient at a first time;   predicting, by a processor, a progression condition of the object at a second time based on the medical information of the first time, wherein the progression condition is indicative of a prognosis risk, wherein the second time is after the first time;   generating, by the processor, a prognosis prediction at the second time reflecting the morphology of the object at the second time based on the medical information of the first time; and   providing the progression condition of the object at the second time and the prognosis prediction at the second time to an information management system for presentation to a user.   
     
     
         31 . The method of  claim 30 , wherein the medical information further includes non-image clinical data associated with a progression of the object. 
     
     
         32 . The method of  claim 31 , wherein the non-image clinical data associated with the progression of the object includes at least one of gender, age, a time period from onset to a first inspection, a diabetes history, a smoking history, a drinking history, a blood pressure, or a history of cardiovascular disease of the patient. 
     
     
         33 . The method of  claim 30 , further comprising:
 presenting, by the information management system, an extent over time between the first time and the second time in an associated manner with at least one of the progression condition of the object at the second time or the prognosis image at the second time.   
     
     
         34 . The method of  claim 30 , further comprising:
 presenting volume, subtype and location of the object associated with the medical image of the patient at the first time.   
     
     
         35 . The method of  claim 30 , wherein the object includes a lesion, and the prognosis risk includes an enlargement risk of a lesion, and the first time is after onset of the lesion. 
     
     
         36 . The method of  claim 30 , wherein the prognosis risk includes at least one of an enlargement risk of the object, a deterioration risk of the object, an expansion risk of the object, a metastasis risk of the object, a recurrence risk of the object, a location of the object, a volume of the object, or a subtype of the object. 
     
     
         37 . The method of  claim 30 , wherein generating the prognosis prediction at the second time based on the medical information of the first time further includes:
 generating the prognosis prediction at the second time using a machine learning algorithm, based on the medical information of the first time and a time interval between the first time and the second time.   
     
     
         38 . A system for prognosis management based on medical information of a patient, comprising:
 an interface configured to receive the medical information including at least a medical image of the patient reflecting a morphology of an object associated with the patient at a first time; and   a processor configured to:
 predict a progression condition of the object at a second time based on the medical information of the first time, wherein the progression condition is indicative of a prognosis risk, wherein the second time is after the first time; 
 generate a prognosis prediction at the second time reflecting the morphology of the object at the second time based on the medical information of the first time; and 
 provide the progression condition of the object at the second time and the prognosis prediction at the second time for presentation to a user. 
   
     
     
         39 . The system of  claim 38 , further comprising:
 an information management system configured to present an extent over time between the first time and the second time in an associated manner with at least one of the progression condition of the object at the second time or the prognosis image at the second time.   
     
     
         40 . The system of  claim 39 , wherein the information management system is further configured to:
 present a medical age of the patient at the first time;   present non-image clinical data associated with a progression of the object of the patient at the first time; and   present the prognosis image of the patient at the second time.   
     
     
         41 . The system of  claim 40 , wherein the object includes a lesion, and the prognosis risk includes an enlargement risk of the lesion, and the first time is after onset of the lesion. 
     
     
         42 . The system of  claim 38 , wherein to generate the prognosis image at the second time based on the medical information, the processor is further configured to generate the prognosis image at the second time using a machine learning model, based on the medical information and a time interval between the first time and the second time. 
     
     
         43 . A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by at least one processor, performs a method for prognosis management based on medical information of a patient, comprising:
 receiving the medical information including at least a medical image of the patient reflecting a morphology of an object associated with the patient at a first time;   predicting a progression condition of the object at a second time based on the medical information of the first time, wherein the progression condition is indicative of a prognosis risk, wherein the second time is after the first time;   generating a prognosis prediction at the second time reflecting the morphology of the object at the second time based on the medical information of the first time; and   providing the progression condition of the object at the second time and the prognosis prediction at the second time to an information management system for presentation to a user.   
     
     
         44 . The non-transitory computer-readable storage medium of  claim 43 , wherein the medical information further includes non-image clinical data associated with a progression of the object. 
     
     
         45 . The non-transitory computer-readable storage medium of  claim 44 , wherein the non-image clinical data associated with the progression of the object includes at least one of gender, age, a time period from onset to a first inspection, a diabetes history, a smoking history, a drinking history, a blood pressure, or a history of cardiovascular disease of the patient. 
     
     
         46 . The non-transitory computer-readable storage medium of  claim 43 , wherein the method further includes:
 presenting, by the information management system, an extent over time between the first time and the second time in an associated manner with at least one of the progression condition of the object at the second time or the prognosis image at the second time.   
     
     
         47 . The non-transitory computer-readable storage medium of  claim 43 , wherein the method further includes:
 presenting volume, subtype and location of the object associated with the medical image of the patient at the first time.   
     
     
         48 . The non-transitory computer-readable storage medium of  claim 43 , wherein:
 the object includes a lesion, and the prognosis risk includes an enlargement risk of a lesion, and the first time is after onset of the lesion; and   the prognosis risk includes at least one of an enlargement risk of the object, a deterioration risk of the object, an expansion risk of the object, a metastasis risk of the object, a recurrence risk of the object, a location of the object, a volume of the object, or a subtype of the object.   
     
     
         49 . The non-transitory computer-readable storage medium of  claim 43 , wherein generating the prognosis prediction at the second time based on the medical information of the first time further includes:
 generating the prognosis prediction at the second time using a machine learning algorithm, based on the medical information of the first time and a time interval between the first time and the second time.