Abstract: A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image data model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image data model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

Abstract: Systems and methods for designing and implementing patient-specific surgical procedures and/or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

Abstract: In an method for training artificial intelligence entities (AIE) for abnormality detection, medical imaging data of the human organ is provided as training data having training samples, the medical imaging data including imaging results from different types of imaging techniques for each training sample of the training data, a pre-trained or randomly initialized AIE is provided, and the AIE is trained using the provided training samples. The training may include, for at least one training sample, a first loss function for a sub-structure of the AIE is calculated independently of a first spatial region of the human organ, and, for a training sample, a second loss function for a sub-structure of the AIE is calculated independently of a second spatial region of the human organ. The AIE may be trained using the calculated first loss function and the calculated second loss function.

Abstract: Systems and methods are disclosed for grouping cells in a slide image that share a similar target, comprising receiving a digital pathology image corresponding to a tissue specimen, applying a trained machine learning system to the digital pathology image, the trained machine learning system being trained to predict at least one target difference across the tissue specimen, and determining, using the trained machine learning system, one or more predicted clusters, each of the predicted clusters corresponding to a subportion of the tissue specimen associated with a target.

Abstract: This application relates to an image recognition technology in the field of computer vision of artificial intelligence, and provides an image classification method and apparatus. An example method includes obtaining an input feature map of a to-be-processed image, and then performing feature extraction processing on the input feature map based on a feature extraction kernel of a neural network to obtain an output feature map, where each of a plurality of output sub-feature maps is determined based on the corresponding input sub-feature map and the feature extraction kernel, at least one of the output sub-feature maps is determined based on a target matrix obtained after an absolute value is taken, and a difference between the target matrix and the input sub-feature map corresponding to the target matrix is the feature extraction kernel. The to-be-processed image is classified based on the output feature map to obtain a classification result of the to-be-processed image.

Abstract: The present disclosure is for a system and a method for artificial intelligence (AI) based review of radiologic images as they are being read by a radiologist. The AI review is performed through the use of screen sharing and screen capture approaches allowing an AI model to analyze images without direct access to the raw image files. The AI review may compare user documented findings with AI model-based findings in order to identify discrepancies between the two. The discrepancies can be used as feedback for retraining the model and/or as feedback to the user to aid in education or track and/or improve an individual's review characteristics.

Abstract: Methods, systems, and apparatus for an imaging-based MicroOrganoSphere drug assay. In one aspect, a method includes obtaining image data of a well plate comprising a plurality of MicroOrganoSpheres; in response to applying a machine learning model configured to identify instances of at least some of the plurality of MicroOrganoSpheres in the image data, obtaining (i) indications indicative of each instance of the MicroOrganoSpheres and (ii) attributes of each instance of the MicroOrganoSpheres; and normalizing, based on the indications and the attributes, a well-to-well variation in the well plate.

Abstract: Methods, systems, and apparatus for an imaging-based MicroOrganoSphere drug assay. In one aspect, a method includes obtaining image data of a well plate comprising a plurality of MicroOrganoSpheres; in response to applying a machine learning model configured to identify instances of at least some of the plurality of MicroOrganoSpheres in the image data, obtaining (i) indications indicative of each instance of the MicroOrganoSpheres and (ii) attributes of each instance of the MicroOrganoSpheres; and normalizing, based on the indications and the attributes, a well-to-well variation in the well plate.

Abstract: In accordance with embodiments of this disclosure, a computational simulation platform comprises a computer-implemented method that includes: generating a mesh or meshless three-dimensional (3D) reconstruction of a vessel lumen and a surface of the vessel lumen based on invasive or non-invasive imaging; assigning material properties to the 3D reconstructed surface of the vessel lumen based on the invasive or non-invasive imaging; performing balloon pre-dilation, stenting and balloon post-dilation computational simulations with the 3D reconstructed vessel lumen and surface of the vessel lumen; and assessing stent and vessel morphometric and biomechanical measures based on the computational simulations.

Abstract: Surgical planning systems that automatically identify one or a plurality of different candidate trajectories to a defined intrabody treatment region. The systems can rank the identified candidate trajectories in an order of hierarchy based on defined parameters such as distance from a critical no-go location and whether a single or multiple different candidate trajectories are needed to provide coverage of the defined intrabody treatment region. The surgical planning systems are also configured to provide a User Interface that defines a workflow for an image-guided surgical procedure and allows a user to select one or more of the identified candidate trajectories steps in the workflow.

Abstract: A method for capturing digital data for fabricating a dental splint involves displaying a GUI on a display of a smartphone that provides an alignment feature for a user to align a camera of the smartphone to a first position that captures teeth of a person, receiving digital video of the teeth, overlaying the alignment feature on the digital video of the teeth on the display to show alignment between the teeth and the alignment feature, capturing digital image information of the teeth while the alignment feature overlaps with the teeth, the captured digital image information including depth information, and transmitting the captured digital image information, including the depth information, from the smartphone for use in fabricating a dental splint.

Abstract: An apparatus for visualization of digitized glass slides belonging to a patient case having a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive an image data set having a plurality of images of one or more specimen and metadata of the plurality of images of the one or more specimen, identify one or more constituent virtualization components for each image of the plurality of images within image data set, determine a relationship between the one or more constituent visualization components as a function of the image data set, construct a plurality of virtual images as a function of the image data set and the relationship between the one or more virtual constituent components, wherein each of plurality of the images includes at least one virtual constituent component generate a consolidated virtual image as a function of the plurality of virtual images and display the consolidated virtual image.

Abstract: Systems and methods for rails and obstacles detection based on forward-looking electrooptical imaging, novel system architecture and novel scene analysis and image processing are disclosed. The processing solution utilizes a deep learning semantic scene segmentation approach based on a rails and switches states detection neural network that determines the railway path of the train in the forward-looking imagery, and an objects and obstacles detection and tracking neural network that analyzes the vicinity of the determined railway path and detects impending obstacles.

Abstract: Systems and methods for generating a 3D model of a dentition are disclosed herein. A method includes generating a first 3D model of a dentition. The dentition includes a model tooth in a first position. The method includes receiving a digital representation comprising a patient tooth. The patient tooth corresponds to the model tooth of the first 3D model. The method includes determining a virtual camera parameter associated with the digital representation. The method includes adjusting the virtual camera parameter to determine a 3D position of the corresponding patient tooth. The method includes moving the model tooth of the first 3D model from the first position to a second position. The second position corresponds to the 3D position of the corresponding patient tooth. The method includes generating a second 3D model comprising the model tooth of the first 3D model in the second position.

Abstract: Methods, non-transitory computer readable media, and arthroscopic video analysis apparatuses and systems that facilitate improved analysis of videos of arthroscopic procedures are disclosed. With this technology, analytical data related to the video feed of an arthroscopic surgery can be obtained using machine learning models and associated with the video feed. The generated videos can be output in real-time to provide contextual information related to the surgical procedure, or can be saved for playback for training or informational purposes.

Abstract: A system for customizing an implant is provided. The system includes a processor configured to: i) obtain one or more medical image stacks of a joint; ii) obtain a three-dimensional image representation of the joint based on at least one of said medical image stacks; iii) determine damage to the joint by analyzing said medical image stacks; iv) select an implant template from a predefined set of implant templates having predetermined types and sizes; v) generate a 3D model, in which the marked damage is visualized together with the selected implant template in a proposed position; vi) display the 3D model; vii) receive an approval for said selected implant template in said proposed position; and viii) determine the final shape and dimensions of a customized implant based on said selected implant template and said proposed position.

Abstract: The disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods for selecting training sets for the development of classifier models. The disclosed selection embodiments can allow for the re-training of classifier models using training examples that have been subjected to the same or similar incubation conditions as target samples. These selection embodiments can reduce the amount of human effort required to specify the training examples. The disclosed embodiments also include the classification of individual cells based on metrics determined for the cells using phase contrast imagery and defocused brightfield imagery. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery.

Abstract: A first display control unit displays a first tomographic image of a three-dimensional image consisting of a plurality of tomographic images on a display unit, and superimposes and displays, as a first cross-section correction instruction region, a cross-section of a three-dimensional correction instruction region on the first tomographic image, which is used to correct a boundary of a region of interest extracted from the three-dimensional image, on the first tomographic image. A second display control unit displays at least one second tomographic image adjacent to the first tomographic image, which includes the three-dimensional correction instruction region, on the display unit, and superimposes and displays, as a second cross-section correction instruction region, a cross-section of the three-dimensional correction instruction region on the second tomographic image, on the second tomographic image.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing medical chest images such as chest X-ray (CXR) images. Segmentation models derived via a deep learning process are used to segment the chest images and obtain a rib segmentation result and a lung segmentation result for each image. The rib segmentation result may include a rib sequence identified in the image while the lung segmentation result may include one or more lung fields identified in the image. The quality of each chest image (e.g., whether the image reflects a breath-holding state of the patient) may then be determined based on whether a sufficient number of ribs in the rib segmentation result overlap with the lung fields in the lung segmentation result. The segmentation results may be obtained in a coarse-to-fine manner, e.g., by first determining a large rib area and then further segmenting the large rib area to identify each individual rib.

Abstract: A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.