Methods and systems for identifying a region of interest in breast tissue use artificial intelligence to confirm that a target lesion identified during initial imaging the breast tissue has been identified in a subsequent imaging session. A computing system operating a lesion matching engine uses a machine learning classifier algorithm trained on cases of initial and subsequent two-dimensional or three-dimensional images of lesions. The lesion matching engine analyzes a target lesion identified with initial and a potential lesion identified with current imaging to determine a likelihood that the target lesion is the same as the potential lesion. A confidence level indicator for the lesion match is presented on a display of a computing device to aid a healthcare provider in locating a lesion in breast tissue.

A method of diagnosing a condition of a subject, comprises: receiving image data of an anterior of an eye of the subject, and analyzing the image data to detect at least one of: flow of individual blood cells in libmal or conjunctival blood vessels of the eye, morphology of limbal or conjunctival blood vessels. The method also comprises determining the condition of the subject based on the detection(s).

A method for preventing wrong-patient errors includes receiving a selection of a current imaging subject. The current imaging subject is selected for a current image acquisition session comprising capturing one or more current images of the current imaging subject utilizing at least a first image sensor system of a first imaging modality. The method includes accessing one or more previous images of a previous imaging subject. The one or more previous images depict the previous imaging subject according to at least a second imaging modality that is different from the first imaging modality. The method includes presenting the one or more previous images on a display system and, in response to determining that the previous imaging subject matches the current imaging subject based upon the one or more previous images, performing the current image acquisition session.

According to the present application, provided is a computer-implemented method of predicting a clinical activity score for conjunctival hyperemia. The method described in the present application includes: training a conjunctival hyperemia prediction model using a training set; acquiring a first image include at least one eye of a subject and an outer region of an outline of the at least one eye; outputting, by the conjunctival hyperemia prediction model executing on a processor, a first predicted value for a conjunctival hyperemia, a first predicted value for the conjunctival edema, a first predicted value for an eyelid redness, a first predicted value for an eyelid edema, and a first predicted value for a lacrimal edema; to and generating a score for the conjunctival hyperemia based on the selected first predicted value for a conjunctival hyperemia.

The system for cardiovascular parameter data quality determination can include a user device and a computing system, wherein the user device can include one or more sensors, the computing system, and/or any suitable components. The computing system can optionally include a data quality module, a cardiovascular parameter module, a storage module, and/or any suitable modules. The method for cardiovascular parameter data quality determination can include acquiring data and determining a quality of the data. The method can optionally include processing the data, and/or determining a cardiovascular parameter, training a data quality module, any suitable steps.

An image processing apparatus comprises processing circuitry configured to: obtain first medical image data captured at a first time and second medical image data captured at a second time different from the first time, the first medical image data and the second medical image data including data representing a bolus of contrast material in a tubular anatomical structure, wherein the bolus of contrast material has moved between the first time and the second time; determine an expected motion of the bolus of contrast material through the tubular anatomical structure between the first time and the second time; and. perform a registration process to obtain a registration of the first medical image data and the second medical image data based at least in part the expected motion of the bolus of contrast material through the tubular anatomical structure.

A system and process use artificial intelligence to evaluate the toxicity of drugs on cells. In some embodiments, a convolutional neural network is trained to identify features in cells and thereafter identify when structural changes in cells are signs of damage from exposure to a drug. Some embodiments use a 2-class deep neural network, comparing drug-treated cells to controls, to learn which images may show signs of toxicity as a result of the drug. In some applications, the system may capture images from a time-lapse experiment to determine from the cell cultures how a drug affects a cell type over time.

A radio-frequency parameter configuration method, apparatus, and system, and a computer-readable storage medium are provided. The method includes acquiring radio-frequency operation data of multiple historical radio-frequency tasks; analyzing the radio-frequency operation data, to obtain and store respective radio-frequency parameter configuration schemes corresponding to various types of ablation objects in a preset database; acquiring, in response to a triggered parameter configuration instruction, a target type of a target operation object indicated by the parameter configuration instruction; and querying the database according to the target type, to obtain a target parameter configuration scheme, and configuring the radio-frequency parameter of a target radio-frequency operation system indicated by the parameter configuration instruction. By the present disclosure, automatic configuration of parameters for a radio-frequency operation system based on big data is realized, with simplified parameter configuration operation, saved configuration time, and improved configuration efficiency.

A radio frequency ablation data processing method and apparatus, a server, and a computer-readable storage medium are provided. The method includes: acquiring radio frequency ablation data of each of the ablation tasks performed in a preset period of time, wherein the radio frequency ablation data includes configured parameter data for each device in a radio frequency ablation system when each of the ablation tasks is performed, and characteristic data of an ablation object associated with each of the ablation tasks; and analyzing the radio frequency ablation data, to obtain and output respective ablation parameter configuration schemes corresponding to various types of ablation objects. By using the radio frequency ablation data processing method and apparatus, the server and the computer-readable storage medium, analysis of radio frequency ablation data based on big data is realized, and reference for ablation parameter configuration is provided for various types of ablation objects.

A surgical system for detecting perfusion includes at least one surgical camera and a computing device. The at least one surgical camera is configured to obtain image data of tissue at a surgical site including first image data and second image data that is temporally-spaced relative to the first image data. The computing device is configured to receive the image data from the at least one surgical camera and includes a non-transitory computer-readable storage medium storing instructions configured to cause the computing device to detect differences between the first and second image data, determine a level of perfusion in the tissue based on the detected differences between the first and second image data, and provide an output indicative of the determined level of perfusion in the tissue.