The present disclosure provides a system and method for image data acquisition. The method may include acquiring physiological data of a subject. The physiological data may correspond to a motion of the subject over time. The method may include obtaining a trained machine learning model configured to detect feature data represented in the physiological data. The method may include determining, based on the physiological data, an output result of the trained machine learning model that is generated based on the feature data. The method may include acquiring, based on the output result, image data of the subject using an imaging device.

A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

A processor inputs a first training image having a first feature to a generator, which is a generative model and generates a training virtual image having a second feature. The processor derives a plurality of types of conversion training images with different observation conditions by performing a plurality of types of observation condition conversion processing on a second training image. The processor derives a plurality of types of conversion training virtual images with the different observation conditions by performing the plurality of types of observation condition conversion processing on the training virtual image. The processor trains the generative model using evaluation results regarding the plurality of types of conversion training images and the plurality of types of conversion training virtual images.

A processor inputs a first training image having a first feature to a generator, which is a generative model and generates a training virtual image having a second feature. The processor derives a plurality of types of conversion training images with different observation conditions by performing a plurality of types of observation condition conversion processing on a second training image. The processor derives a plurality of types of conversion training virtual images with the different observation conditions by performing the plurality of types of observation condition conversion processing on the training virtual image. The processor trains the generative model using evaluation results regarding the plurality of types of conversion training images and the plurality of types of conversion training virtual images.

An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

An analysis result acquisition unit acquires an analysis result indicating a certainty factor indicating that an abnormality is included in a medical image by analyzing the medical image. A priority deriving unit derives a higher priority as the certainty factor becomes closer to a median value between a maximum value and a minimum value of the certainty factor.

A method to perform automatic corneal topography or tomography difference mapping includes receiving one or more corneal topography or tomography data files and/or a corneal image for an examined patient from a corneal topography or tomography system; receiving personal identification parameters from captured user personal data communicated from the corneal topography or tomography system; and comparing received patient identification parameters to existing patient identification parameters in a database to identify if there are existing topography or tomography data files for a same patient in the database. The method may further include retrieving a prior topography or tomography data file for the patient from the database; and performing difference mapping by comparing the received topography or tomography data files to the prior topography or tomography data file retrieved from the database to generate a topography or tomography difference map.

A method to perform automatic corneal topography or tomography difference mapping includes receiving one or more corneal topography or tomography data files and/or a corneal image for an examined patient from a corneal topography or tomography system; receiving personal identification parameters from captured user personal data communicated from the corneal topography or tomography system; and comparing received patient identification parameters to existing patient identification parameters in a database to identify if there are existing topography or tomography data files for a same patient in the database. The method may further include retrieving a prior topography or tomography data file for the patient from the database; and performing difference mapping by comparing the received topography or tomography data files to the prior topography or tomography data file retrieved from the database to generate a topography or tomography difference map.

The present disclosure provides a system for predicting diabetic retinopathy progression. The system includes an image-capturing module and a processing unit. The image-capturing module is configured to capture a first fundus image of a user at a first time and a second fundus image of the user at a second time different from the first time. The processing unit is configured to receive the first fundus image and the second fundus image, compare the first fundus image and the second fundus image and indicate a difference between the first fundus image and the second fundus image. The processing unit is also configured to provide a prediction in a diabetic retinopathy progression of the user based on the difference. A method for predicting diabetic retinopathy progression is also provided in the present disclosure.