AI-based video analysis of cataract surgery for dynamic anomaly recognition and correction

An operation assistance system for recognizing deviations from plan parameter values during a cataract operation, the operation assistance system comprising:

• memory that stores program code;
• and
• one or more processors that are connected to the memory and that, when they execute the program code, prompt the operation assistance system to control;
• a video sequence storage apparatus for providing video sequences of cataract operations, the video sequences having been recorded using one or more image recording apparatuses;
• a training control system for training a machine learning system using, as training input data, the video sequences and, for each cataract operation:
• a planned refractive power of an intraocular lens to be inserted during the cataract operation;
• a target refraction value following the cataract operation;
• and
• an associated prediction result comprising an actual refraction value following the cataract operation,
• wherein the machine learning system is trained to form a machine learning model for dynamically predicting, during a cataract operation, the actual refraction value following the cataract operation, and predicting a deviation value between a target refraction value following the cataract operation and a predicted actual refraction value following the cataract operation;
• and
• parameter value memory for persistently storing parameter values of the trained machine learning system.

A computer-readable storage medium storing a computer program product for recognizing deviations from plan parameters during a cataract operation, wherein the computer program product, when executed by one or more computers or control units, cause the one or more computers or control units to perform operations comprising:

• providing video sequences of cataract operations, the video sequences having been recorded using one or more image recording apparatuses,
• training a machine learning system using as training input data:
• the video sequences, and
• for each cataract operation:
• a planned refractive power of an intraocular lens to be inserted during the cataract operation;
• a target refraction value following the cataract operation;
• and
• an associated prediction result comprising an actual refraction value following the cataract operation,
• wherein the machine learning system is trained to form a machine learning model for dynamically predicting, during a cataract operation, the actual refraction value following the cataract operation, and predicting a deviation value between a target refraction value following the cataract operation and a predicted actual refraction value following the cataract operation;
• and
• persistently storing parameter values of the trained machine learning system.