Supervised and unsupervised learning schemes may be used to automatically label medical images for use in deep learning applications. Large labeled datasets may be generated from a small initial training set using an iterative snowball sampling scheme. A machine learning powered automatic organ classifier for imaging datasets, such as CT datasets, with a deep convolutional neural network (CNN) followed by an organ dose calculation is also provided. This technique can be used for patient-specific organ dose estimation since the locations and sizes of organs for each patient can be calculated independently.

A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

At least one embodiment of a method for training automatic tooth charting systems, the method comprising: obtaining, through a communication network, a plurality of electronic dental charts generated by the plurality of tooth charting systems, the plurality of electronic dental charts being related to a plurality of patients; for each of the obtained electronic dental charts: extracting at least a portion of an image representing a tooth or a region of interest and obtaining at least a corresponding item of information characterizing the represented tooth or the represented region of interest; storing the extracted at least a portion of the image and the corresponding item of information in a training data set, training the automatic dental charting system with the training data set.

A computer-executed method implementing a deep learning technique is carried out to perform on canine thoracic radiographic images an automated diagnosis of left atrial enlargement as an early sign of myxomatous mitral valve insufficiency.

A computer-executed method implementing a deep learning technique is carried out to perform on canine thoracic radiographic images an automated diagnosis of left atrial enlargement as an early sign of myxomatous mitral valve insufficiency.

A method, computer program product, and computing system for visual diarization of an encounter is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is processed to: associate a first portion of the encounter information with a first encounter participant, and associate at least a second portion of the encounter information with at least a second encounter participant. A visual representation of the encounter information is rendered. A first visual representation of the first portion of the encounter information is rendered that is temporally-aligned with the visual representation of the encounter information. At least a second visual representation of the at least a second portion of the encounter information is rendered that is temporally-aligned with the visual representation of the encounter information.

A method, computer program product, and computing system for visual diarization of an encounter is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is processed to: associate a first portion of the encounter information with a first encounter participant, and associate at least a second portion of the encounter information with at least a second encounter participant. A visual representation of the encounter information is rendered. A first visual representation of the first portion of the encounter information is rendered that is temporally-aligned with the visual representation of the encounter information. At least a second visual representation of the at least a second portion of the encounter information is rendered that is temporally-aligned with the visual representation of the encounter information.

A mobile device, computer readable storage medium and method diagnose COVID-19 from at least one Chest X-Ray image. The method can include imaging, by a chest x-ray machine, a person's chest area to obtain the at least one chest x-ray image, performing image segmentation of a human lung in the at least one Chest X-Ray image; extracting radiomics features from the segmented lung, selecting a subset of the radiomics features for classification ability between two classes of COVID-19 and non-COVID-19 including other lung diseases, classifying between COVID-19 and non-COVID-19 using an ensemble bagged model having a plurality of classifiers and outputting, an indication of whether the patient is infected with COVID-19. The method can detect COVID-19 early and rapidly from chest X-ray images in presence of other lung diseases including viral/bacterial pneumonia and is robust to different severity levels of the diseases.

A mobile device, computer readable storage medium and method diagnose COVID-19 from at least one Chest X-Ray image. The method can include imaging, by a chest x-ray machine, a person's chest area to obtain the at least one chest x-ray image, performing image segmentation of a human lung in the at least one Chest X-Ray image; extracting radiomics features from the segmented lung, selecting a subset of the radiomics features for classification ability between two classes of COVID-19 and non-COVID-19 including other lung diseases, classifying between COVID-19 and non-COVID-19 using an ensemble bagged model having a plurality of classifiers and outputting, an indication of whether the patient is infected with COVID-19. The method can detect COVID-19 early and rapidly from chest X-ray images in presence of other lung diseases including viral/bacterial pneumonia and is robust to different severity levels of the diseases.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for pulmonary procedures. In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in guiding a surgical device(s) toward and within a lung of the patient to perform a pulmonary procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the lung of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.