Hamer, Thomas Daniel

Disclosed herein is a computer-implemented method for adaptive radiotherapy. The method comprises receiving symptom data, the symptom data being indicative of the existence and/or severity of a symptom experienced by a radiotherapy patient undergoing treatment according to a first radiotherapy treatment plan; obtaining first treatment plan data associated with the first radiotherapy treatment plan; and generating a second radiotherapy treatment plan based on the obtained first treatment plan data and the symptom data.

Aspects include a computer-implemented method for processing digital data relating to a criminal investigation, the method comprising: obtaining multiple sources of digital input data including one or more from among: digital photographic images; and digital video. The method further comprising executing an image processing procedure on the digital input data including: segmenting the digital input data to obtain a set of segmented entity appearances; encoding the population of segmented entity appearances by mapping the population of segmented entity appearances to a set of defined semantic concepts; extracting from the digital input data a set of appearance relationships between segmented entity appearances; augmenting the set of defined semantic concepts by encoding, in accordance with the mapping, the set of appearance relationships as semantic relationships between the defined semantic concepts; based on an hypothesis received from an end user and relating to the defined semantic concepts and including at least one of natural language and an image of an entity; comparing the hypothesis with the augmented set of defined semantic concepts to identify, in accordance with the mapping, digital input data featuring at least one of segmented entity appearances and appearance relationships mapping to semantic concepts supporting or otherwise consistent with the hypothesis; outputting the identified digital input data to the end user in response to the received hypothesis.

A wearable computing device with bio-signal sensors and a feedback module provides an interactive mediated reality ("VR") environment for a user. The bio-signal sensors receive bio-signal data (for example, brainwaves) from the user and include bio-signal sensors embedded in a display isolator, having a deformable surface, and having an electrode extendable to contact the user's skin. The wearable computing device further includes a processor to: present content in the VR environment via the feedback module; receive bio-signal data of the user from the bio-signal sensor; process the bio-signal data to determine user states of the user, including brain states, using a user profile; modify a parameter of the content in the VR environment in response to the user states of the user. The user receives feedback indicating the modification of the content via the feedback module.

Provided in the present disclosure are a method and apparatus for generating a target deep learning model. The method comprises: acquiring an indication, which is input by a user, and raw data for generating a target deep learning model, wherein the indication comprises a task, which is expected to be executed by means of the target deep learning model; then, generating training data from the raw data; determining a first deep learning model corresponding to the task; and next, training the first deep learning model by using the training data, so as to obtain the target deep learning model.

Systems and methods are disclosed for monitoring and estimating an anatomic position of a human subject for a radiotherapy treatment session, based on use of an artificial intelligence (AI) model (e.g., a generative AI model comprising a Transformer deep learning neural network), are described. An example method of monitoring anatomic position with a trained AI model includes: receiving position information corresponding to observed positions of a tracked anatomical area of a patient, observed during the radiotherapy treatment session; providing the position information as an input to a trained model trained with temporal sequences of observed anatomical positions from training data; determining an estimated position of the tracked anatomical area of the patient at a future time, based on output of the trained model; and controlling the radiotherapy treatment session based on the estimated position of the tracked anatomical area of the patient.

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