Disclosed are a diagnostic information processing method and apparatus based on a medical image, and a storage medium, to achieve disease grading based on a medical image. The method includes: acquiring a first lung medical image of a subject; acquiring image parameters of an affected area in the first lung medical image; and determining, according to the image parameters of the affected area, a disease grade of lungs of the subject corresponding to information of the first lung medical image. Using the solution provided by the present invention, the image parameters of the affected area in the first lung medical image can be acquired, and then the disease grade of the lungs of the subject corresponding to the information of the first lung medical image can be determined according to the image parameters of the affected area, so that a disease can be graded based on a medical image.

A method for providing a prognosis data record includes receiving a first image data record relating to an examination region of an examination object, and receiving an operating parameter of a medical object that is arranged at the examination region of the examination object and positioning information of the medical object that is arranged at the examination region. The prognosis data record is created by applying a trained function to input data. The input data is based on the first image data record, the at least one operating parameter, and the positioning information of the medical object. At least one parameter of the trained function is based on a comparison with a first comparison image data record. As compared with the first image data record, the first comparison image data record includes changes influenced by the medical object at the examination region. The prognosis data record is provided.

Implementations illustrated herein discloses a method of controlling drug titration to a patient, the method including receiving, using a processor, a sequence of depth images, each depth image including depth information for at least a portion of the patient, determining, using the processor, a sequence of physiological signals for the patient based on the sequence of depth images, analyzing, using the processor, the sequence of physiological signals for the patient to determine a change in a condition of the patient, and generating a signal to a drug infusion pump in response to determining the change in the condition of the patient.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

A method and apparatus for calculating the blood flow rate in a coronary artery, an electronic device and a storage medium. The method for calculating the blood flow rate in a coronary artery comprises the following steps: S1, acquiring an angiography image of the coronary artery, segmenting the angiography image of the coronary artery by using deep learning, and obtaining segmented images of a main vessel (S1); S2, calculating the length of the main vessel in each segmented image frame on the basis of the segmented images of the main vessel (S2); and S3, obtaining the blood flow rate in the main vessel on the basis of the calculated change of the lengths of the main vessel with time (S3). By using the method and apparatus for calculating the blood flow rate in a coronary artery and the electronic device, the automation of the calculation of the blood flow rate in a coronary artery is achieved, the calculated blood flow rate in the coronary artery is more accurate, and the calculation method is simple.

Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).

A program causes a computer to perform processing of: acquiring an endoscopic image obtained by capturing a subject using an endoscope; extracting region-of-interest information from the acquired endoscopic image; acquiring a three-dimensional medical image obtained by capturing an inside of a body of the subject using at least one of X-ray CT, X-ray cone beam CT, MRI-CT, and an ultrasonic diagnostic device; deriving position information in a coordinate system of the three-dimensional medical image specified by the region-of-interest information and the three-dimensional medical image; and storing the region-of-interest information and the three-dimensional medical image in association with each other based on the derived position information and a capturing time point of each of the endoscopic image and the three-dimensional medical image.

Implementations are directed to generating corresponding three-dimensional (“3D”) rowview representation(s) of row(s) of an agricultural field at various time instance(s) to enable a human operator of the agricultural field to virtually traverse through the row(s) at the various time instance(s). In some implementations, the corresponding 3D rowview representation(s) can be generated based on corresponding vision data captured at the various time instance(s). The corresponding 3D rowview representation(s) can be generated based on processing the corresponding vision data. Further, the corresponding 3D rowview representation(s) can be provided to a client device of the human operator of the agricultural field to enable the human operator to virtually traverse through the row(s) of the agricultural field at the various time instance(s). In some implementations, the corresponding 3D rowview representation(s) can be annotated with inference(s) made with respect to the row(s) and/or corresponding non-vision data obtained for the various time instance(s).

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a clinical recommendation for medical treatment of a patient. In one aspect a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using a machine learning model, in accordance with values of a set of machine learning model parameters, to generate a patient classification that classifies the patient as being included in a patient category from a set of patient categories; determining an uncertainty measure that characterizes an uncertainty of the patient classification generated by the machine learning model; and generating a clinical recommendation for medical treatment of the patient based on: (i) the patient classification, and (ii) the uncertainty measure that characterizes the uncertainty of the patient classification.

Medical image(s) are input into a detection network to generate mask(s) identifying a set of regions within the medical image(s), where the detection network predicts that each region identified in the mask(s) includes a depiction of a tumor of one or more tumors within the subject. For each region, the region of the medical image(s) is processed using a tumor segmentation network to generate one or more tumor segmentation boundaries for the tumor present within the subject. For each tumor and by using a plurality of organ-specific segmentation networks, an organ is determined within which at least part of the tumor is located. An output is generated based on the one or more tumor segmentation boundaries and locations of the organs within which at least part of the one or more tumors are located.