Disclosed in a method, a user interface and a system for use in determining shade of a patient's tooth, wherein a digital 3D representation including shape data and texture data for the tooth is obtained. A tooth shade value for at least one point on the tooth is determined based on the texture data of the corresponding point of the digital 3D representation and on known texture values of one or more reference tooth shade values.

The present disclosure relates to a method and device for processing human body characteristic data. The method includes: receiving a set of human body characteristic information transmitted by a terminal; and according to requirements of an application to be run, extracting, from the set of human body characteristic information, human body characteristic data corresponding to the application to be run, and performing data reconstruction process on the extracted human body characteristic data.

Provided is an image processing device including a matching unit that performs matching processing between a predetermined pattern on a surface of a 3D model of a biological tissue including an operating site generated on the basis of a preoperative diagnosis image and a predetermined pattern on a surface of the biological tissue included in a captured image during surgery, a shift amount estimation unit that estimates an amount of deformation from a preoperative state of the biological tissue on the basis of a result of the matching processing and information regarding a three-dimensional position of a photographing region which is a region photographed during surgery on the surface of the biological tissue, and a 3D model update unit that updates the 3D model generated before surgery on the basis of the estimated amount of deformation of the biological tissue.

A long short-term memory (LSTM) model-based disease prediction method and apparatus, a computer device, and a storage medium are provided. The method includes: obtaining first medical data of a target object and second medical data of an associated object; inputting the first medical data and the second medical data into a first LSTM network in the LSTM model, to obtain a hidden state vector sequence in the first LSTM network; inputting the hidden state vector sequence into a second LSTM network for operation, to obtain a disease prediction result; selecting a predicted disease with an incidence rate higher than a preset threshold, and recording the predicted disease as a designated disease, and obtaining, based on a preset disease association network, an associated disease directly connected to the designated disease; and outputting the disease prediction result and the associated disease, thereby improving the prediction accuracy.

A long short-term memory (LSTM) model-based disease prediction method and apparatus, a computer device, and a storage medium are provided. The method includes: obtaining first medical data of a target object and second medical data of an associated object; inputting the first medical data and the second medical data into a first LSTM network in the LSTM model, to obtain a hidden state vector sequence in the first LSTM network; inputting the hidden state vector sequence into a second LSTM network for operation, to obtain a disease prediction result; selecting a predicted disease with an incidence rate higher than a preset threshold, and recording the predicted disease as a designated disease, and obtaining, based on a preset disease association network, an associated disease directly connected to the designated disease; and outputting the disease prediction result and the associated disease, thereby improving the prediction accuracy.

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

A method of pre-operatively developing a reverse shoulder arthroplasty plan can include receiving an image of a patient shoulder comprising a humerus and a glenoid. The image can be segmented to develop a 3D shoulder model. Virtual surgery can be performed on the 3D shoulder model to generate a modified shoulder model. The virtual surgery can include resecting and reaming a virtual humerus of the 3D shoulder model, and reaming a virtual glenoid of the 3D shoulder model. Selection of a humeral implant and selection of a glenoid implant can be received. A virtual representation of the humeral implant can be implanted on the virtual humerus and a virtual representation of the glenoid implant on the virtual glenoid to virtually update the modified shoulder model. A range of motion of the patient shoulder can be determined and a reverse shoulder arthroplasty can be finalized based on the range of motion.

Methods of the disclosure may include obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third brain volume (BV) values at the first time point based on two or more images from the first set of medical images and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first, second, third, or fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.