A pneumonia area detecting system includes an information obtaining module, an image preprocessing module, a calculating module, and a pneumonia area detecting model. The information obtaining module obtains medical images and a frame size of an initial pneumonia area. The image preprocessing module processes the medical images to obtain a standard format image. The calculating module processes the frame size of the initial pneumonia area through a preset algorithm and the standard format image, to obtain a frame size of a standard format pneumonia area. The pneumonia area detecting model obtains an output pneumonia area according to the standard format image and the frame size of the standard format pneumonia area. A pneumonia area detecting method and an electronic device are also provided in the present disclosure.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.

A portable skin condition measuring apparatus according to an embodiment of the present disclosure is capable of complexly measuring and analyzing various skin condition indexes of a user by coming into contact with a user's face, body, or scalp. The portable skin condition measuring apparatus may solve inconvenience in measuring a user's skin condition and improve efficiency of measurement, determination, and care of the user's skin condition by measuring and analyzing various skin condition indexes of the user in a non-face-to-face manner.

Various embodiments of the present disclosure are directed towards a method for predicting a response to treatment of small cell lung cancer (SCLC). The method includes generating a radiomic risk score (RRS) for the patient based on a plurality of radiomic features, wherein the RRS is prognostic of overall survival (OS) of the patient. The RRS is provided to a machine learning classifier that is trained to predict a response of the patient to a SCLC chemotherapy treatment based, at least in part, on the RRS. The machine learning classifier provides a classification of the patient into either a responder group (RG) or a non-responder group (NRG), where the NRG indicates the patient will not respond to the SCLC chemotherapy treatment and the RG indicates that the patient will respond to the SCLC chemotherapy treatment.

A system, method, and computer readable media are provided for obtaining a first set of skin data from an image capture system including at least one ultraviolet (UV) image of a user's skin. Performing a correction on the skin data using a second set of skin data associated with the user. Quantifying a plurality of skin parameters of the user's skin based on the first skin data, including quantifying a bacterial load. Quantifying the bacterial load by applying a brightness filter to isolate portions of the at least one UV image containing fluorescence, applying a dust filter, identifying portions of the at least one UV image that contain fluorescence due to bacteria, and determining a quantity of bacterial load in the users skin. Determining, using a machine learning model, an output associated with a normal skin state of the user and a current skin state of the user.

A technique of generating surgical information from intra-operatively acquired image data of vertebrae and pre-operatively acquired image data of the vertebrae is presented. A method implementation includes obtaining first image segments each containing a different vertebra, and second image segments each containing a different vertebra. The first image segments have been derived by processing the pre-operatively acquired image data, and the second image segments have been derived by processing the inter-operatively acquired image data. The method includes identifying one of the second image segments and one of the first image segments that contain the same vertebra, and determining a transformation that registers the identified first image segment and the identified second image segment. The method includes generating surgical information based on the transformation and the identified first image segment.

In an aspect, a method for imaging a target comprises steps of: performing optical coherence tomography (OCT) scanning on the target with one or more beams of source light, the one or more beams of source light comprising a plurality of wavelengths; wherein performing OCT scanning comprises: providing the source light to a reference optical path and to a sample optical path, wherein providing the source light to a sample optical path comprises illuminating the target with the source light; and recording interference data corresponding to an interaction of a light from the reference optical path and a light from the sample optical path; processing the interference data; and identifying blood or one or more blood-features in the target based on an optical attenuation of light in or associated with the sample optical path by the blood or the one or more blood-features.

An image processing device disclosed in the present specification includes a display control unit configured to display, on a display unit, an image obtained by visualizing functional information related to optical characteristics of a subject based on a photoacoustic signal obtained by receiving a photoacoustic wave generated in the subject by irradiating the subject with light, and a correction unit configured to correct the functional information of the image displayed on the display unit based on a first image value of the functional information of the image displayed on the display unit and a second image value of the functional information.

A computing system generates, based on medical imaging data of bones of a joint of a patient, bony landmark data that characterizes relationships between two or more landmarks on one or more of the bones of the joint of the patient. Additionally, the computing system applies a classifier algorithm that has been trained using training data to select a class associated with the patient from among a plurality of classes. The classifier algorithm takes the bony landmark data of the patient as input.

In a facial skin detection method performed by a terminal device having a front-facing camera, a first image including a face is obtained and feature information of pores in a target area of the face is extracted. The pores in the target area are classified into pore categories based on the feature information of the pores in the target area. Feature information of pores of each pore category is input into at least one pore detection model to obtain pore detection data of each pore category and a skin detection result of the face is determined based on pore detection data of the pore categories.