Pupillary curve morphology and diagnosis management

A system comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:
• capturing sensor data associated with pupillary activity of individuals;
• collecting datasets based on the sensor data, individual ones of the datasets including corresponding pupillary activity curves representing corresponding changes in pupil characteristics over time;
• generating classification models representing classifications of the pupillary activity curves based on physiological conditions associated with the pupillary activity curves, wherein generating the classification models comprises processing whole pupillary activity curves to identify patterns and characteristics for diagnosis of the physiological conditions without computing intermediate pupillary metrics prior to classification;
• generating baselines via consolidation of pupillary activity data comprising the pupillary activity curves;
• receiving current sensor data associated with current pupillary activity associated with a simultaneous scan of a pair of pupils of an individual in response to at least one of external or internal stimuli, wherein the simultaneous scan comprises binocular data collection for both pupils at a same time to identify how both pupils react to the at least one of external or internal stimuli;
• generating a current pupillary activity curve associated with the current sensor data, the current pupillary activity curve representing changes of both pupils in the pair of pupils;
• using a machine learning (ML) model to analyze the current pupillary activity curve, the ML model having been trained based on the baselines and the classifications;
• and
• outputting a physiological condition identifier associated with a result of the ML model being used to analyze the current pupillary activity curve.

One or more non transitory computer-readable media storing instructions executable by at least one processor, wherein the instructions, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

• capturing sensor data associated with pupillary activity of individuals;
• collecting datasets in a group of database files, individual ones of the datasets including corresponding pupillary activity curves representing corresponding changes in pupil characteristics over time;
• receiving current sensor data associated with current pupillary activity associated with a simultaneous scan of a pair of pupils of an individual in response to at least one of external or internal stimuli, wherein the simultaneous scan comprises binocular data collection for both pupils at a same time to identify how both pupils react to the at least one of external or internal stimuli;
• generating a current pupillary activity curve associated with the current sensor data, the current pupillary activity curve representing changes of both pupils in the pair of pupils;
• using one or more data algorithm models to analyze the current pupillary activity curve based on the pupillary activity curves;
• and
• outputting a physiological condition identifier associated with a result of the one or more data algorithm models being used to analyze the current pupillary activity curve.