Dartmouth-hitchcock Clinic

Mary Hitchcock Memorial Hospital, for Itself and on Behalf of Dartmouth-hitchcock Clinic

Mary Hitchcock Memorial Hospital

Louis J Vaickus

Joshua J Levy

Matthew LeBoeuf

Christopher R Jackson

This invention provides a system for automation of a pathology process, which includes a processor having trained artificial intelligence (AI) modules operating in association therewith, adapted to receive image data from camera images of whole tissue acquired by a camera assembly and whole slide images (WSIs) of inked and segmented tissue samples. A mask produces image results for tissue with holes and free of holes, and a filter provides filtered image results to the AI modules, detecting tumors and macroarchitecture features. A quality assessment process produces quality score outputs for tumors and macroarchitecture features. A report generator provides reports with one or more parameters to a user via an interface. More particularly, the report generator automatically creates a pathology report, having a written description and pictorial diagram relative to the gross images of the tissue integrating the outputs of the AI modules used to analyze the whole slide digital images.

System and method for automation of surgical pathology processes using artificial intelligence

This invention provides a histologic system and method for rapidly and accurately assessing tumor margins for the presence or absence of tumor using machine learning algorithms. This affords a rapid and accurate histologic tumor readout and increase process efficiency and decreases the chance for human error. Advantageously and uniquely, the system and method allows for analyzing the tissue section as complete or incomplete as the first criteria to determine whether a tissue section is clear of tumor. A machine learning process receives whole slide images (WSI) of tissue and determines (a) if each image of the WSI contains complete/incomplete tissue samples and (b) if each image of the WSI contains tumorous tissue or an absence thereof. A reconstruction process generates a model of the tissue that maps types of tissue therein, and a display process provides results of the model or report for use and manipulation by a user.

System and method for rapid and accurate histologic analysis of tumor margins using machine learning

This provides a system and method for analyzing and diagnosing conditions, such as cancer, based upon stained tissue sample slides prepared by users from patient tissue taken, for example, in a biopsy procedure. A convolutional neural network (CNN) is generated at training time. Slide images are acquired/scanned, and individual cell nuclei from the images are annotated, using IHC. H&E images are acquired/scanned prior to a washout intermediate step from the same slides/tissue samples. IHC is performed on the same tissue layer, which was scanned and used to retrospectively annotate the H&E WSI. IHC is used to annotate a ground truth mask for machine learning. The resultant process achieves a high degree of spatial resolution in detecting individual IHC positive nuclei, yielding process that is almost entirely automated, employing materials that are commonly available in clinical practice. In runtime, users access the CNN to perform analysis on patient slides.

System and method for providing a rapid virtual diagnostic companion for use in diagnosis of cancer and related conditions using immunohistochemistry based upon a neural network

A system and method for delivering diagnostic information to a user in an automated manner. An image processing module reads magnified raw image data from whole slides containing tissue cells. A background deletion process identifies and isolates cell images. A neural network, which is trained based upon image data, classifies the cell images. The classification uses specialist criteria to categorize/segment the cell images into a plurality of discrete cell types. A display process provides the diagnostic information to the user. The neural network can be trained using a plurality of cell types each having the specialist criteria. The diagnostic information includes filtered and/or reorganized images of the tissue cells. Additionally, the user can request the diagnostic information based upon an account that provides payment according to a predetermined formula associated with at least one of a type, format, and timing of information delivered to the user.

Growing community of inventors

Etna, NH, United States of America

Louis J Vaickus