Position estimation of an anatomical landmark by text inputs

A computer system for estimating a position of an anatomical landmark by text inputs, comprising:

• a non-transitory memory device for storing computer readable program code;
• and
• a processor in communication with the non-transitory memory device, the processor being operative with the computer readable program code to create an artificial intelligence (AI)-system by performing steps including
• providing a context-set comprising a list of names of anatomical landmarks, providing a position-list comprising position-tokens being expressions referring to relative positions,
• generating a plurality of question-prompts asking for a relative position of a landmark by using varying combinations of the anatomical landmarks and the position-tokens of the context-set and the position-list,
• generating target-landmarks to each question-prompt by inputting the question-prompts in a large language model to obtain an answer, parsing the answer to find landmarks and defining the found landmarks as the target-landmarks,
• forming a plurality of training-datasets, wherein each training-dataset comprises the landmark, a position-token from a question-prompt and a target- landmark from the answer to this question-prompt, and
• training the AI-system with a training-dataset and additional spatial coordinates of a part of the anatomical landmarks of the context-set.

One or more non-transitory computer-readable media embodying instructions executable by machine to perform steps for creating an artificial intelligence (AI)-system for estimating a position of an anatomical landmark by text inputs, comprising:

• providing a context-set comprising a list of names of anatomical landmarks;
• providing a position-list comprising position-tokens being expressions referring to relative positions;
• generating a plurality of question-prompts asking for a relative position of a landmark by using varying combinations of the anatomical landmarks and the position-tokens of the context-set and the position-list;
• generating target-landmarks to each question-prompt by inputting the question-prompts in a large language model to obtain an answer, parsing the answer to find landmarks and defining the found landmarks as the target-landmarks;
• forming a plurality of training-datasets, wherein each training-dataset comprises the landmark, a position-token from a question-prompt and a target-landmark from the answer to this question-prompt;
• and
• training the AI-system with a training-dataset and additional spatial coordinates of a part of the anatomical landmarks of the context-set.