Assessing developmental disorders via eye tracking

A computer-implemented method for using timestamped eye-tracking gaze coordinate data collected by tablet computing devices, the computer-implemented method comprising:

• receiving, at a network-connected server in communication with multiple patient-side portable tablet computing devices, session data of multiple eye-tracking sessions, the session data of each session comprising timestamped eye-tracking gaze coordinate data of a corresponding patient collected by a corresponding one of the multiple patient-side portable tablet computing devices during presentation of a set of videos in the session;
• simultaneously recalibrating, at the network-connected server, the timestamped eye-tracking gaze coordinate data of each session of the multiple eye-tracking sessions to a predefined resolution or coordinate system for providing processed session data for each session of the multiple eye-tracking sessions;
• and
• simultaneously comparing, at the network-connected server, the processed session data of each session of the multiple eye-tracking sessions to pre-loaded model data such that, for each session of the multiple eye-tracking sessions, the network-connected server:
• obtains a comparison result from a trained artificial intelligence model at the network-connected server that uses both the processed session data of the session and corresponding reference eye-tracking data of patients from a same age range as the corresponding patient and that outputs multiple numeric index scores indicative of a severity of a developmental condition, wherein said simultaneously comparing the processed session data of each session of the multiple eye-tracking sessions to pre-loaded model data includes the network-connected server loading multiple session containers for parallel analysis so that one comparison result is obtained from the trained artificial intelligence model for each one of the multiple session containers, wherein each one of the multiple session containers stores the processed session data of one patient and the pre-loaded model data corresponding to said one patient, and
• stores at a web portal of the network-connected server an assessment report for the corresponding patient that displays said multiple numeric index scores indicative of the severity of the developmental condition.

A computer-implemented method for using timestamped eye-tracking gaze coordinate data collected by tablet computing devices, the computer-implemented method comprising:

• receiving, at a network-connected server in communication with multiple patient-side portable tablet computing devices, session data of multiple eye-tracking sessions, the session data of each session comprising timestamped eye-tracking gaze coordinate data of a corresponding patient collected by a corresponding one of the multiple patient-side portable tablet computing devices during presentation of a set of videos in the session, wherein said receiving the session data of the multiple sessions comprises:
• receiving, through a web portal of the network-connected server, the session data of the multiple sessions from the multiple patient-side portable tablet computing devices at multiple remote sites, and storing session data from one or more patient-side portable tablet computing devices associated with each remote site in a respective isolated repository that includes information of one or more operators or operator-side computing devices associated with the remote site, wherein the respective isolated repository is isolated from one or more other isolated repositories at the network-connected server such that the respective isolated repository is inaccessible by one or more other remote sites having access to the web portal;
• simultaneously recalibrating, at the network-connected server, the timestamped eye-tracking gaze coordinate data of each session of the multiple eye-tracking sessions to a predefined resolution or coordinate system for providing processed session data for each session of the multiple eye-tracking sessions;
• and
• simultaneously comparing, at the network-connected server, the processed session data of each session of the multiple eye-tracking sessions to pre-loaded model data such that, for each session of the multiple eye-tracking sessions, the network-connected server:
• obtains a comparison result from a trained artificial intelligence model at the network-connected server that uses both the processed session data of the session and corresponding reference eye-tracking data of patients from a same age range as the corresponding patient and that outputs multiple numeric index scores indicative of a severity of a developmental condition, and
• stores at the web portal of the network-connected server an assessment report for the corresponding patient that displays said multiple numeric index scores indicative of the severity of the developmental condition.