Patents by Inventor Anant Madabhushi

Anant Madabhushi has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240057874
    Abstract: The present disclosure relates to a method. The method may be performed by accessing data derived from one or more routine clinical medical imaging scans including a lesion in which the lesion and associated vasculature are segmented in a three-dimensional segmentation. At least two features are extracted from the three-dimensional segmentation of the associated vasculature. The at least two features include at least one feature indicative of a morphology of the associated vasculature or a portion thereof, and at least one feature indicative of a function of the associated vasculature or a portion thereof. The at least two features, and/or one or more statistics of the at least two features, are provided to a machine learning model trained to make a prediction concerning the lesion. The prediction concerning the lesion is received from the machine learning model.
    Type: Application
    Filed: October 27, 2023
    Publication date: February 22, 2024
    Inventors: Anant Madabhushi, Nathaniel Braman
  • Patent number: 11896349
    Abstract: Embodiments discussed herein facilitate determination of a response to treatment and/or a prognosis for a tumor based at least in part on features of tumor-associated vasculature (TAV). One example embodiment is a method, comprising: accessing a medical imaging scan of a tumor, wherein the tumor is segmented on the medical imaging scan; segmenting tumor-associated vasculature (TAV) associated with the tumor based on the medical imaging scan; extracting one or more features from the TAV; providing the one or more features extracted from the TAV to a trained machine learning model; and receiving, from the machine learning model, one of a predicted response to a treatment for the tumor or a prognosis for the tumor.
    Type: Grant
    Filed: December 9, 2020
    Date of Patent: February 13, 2024
    Assignees: Case Western Reserve University, The United States Government as Represented by The Department of Veteran Affairs
    Inventors: Anant Madabhushi, Nathaniel Braman
  • Patent number: 11861836
    Abstract: The degree of differentiation of a cell in tissue is precisely determined. An estimating device (1) includes: a binarizing section (41) configured to generate binarized images from an image obtained by capturing an image of tissue; a Betti number calculating section (42) configured to calculate, for each binarized image, (i) the number of hole-shaped regions (b1) each surrounded by pixels of a first pixel value and each composed of pixels of a second pixel value, (ii) the number of connected regions each composed of the pixels of the first pixel value connected together, and (iii) a ratio (R) between (i) and (ii); a statistic calculating section (43) configured to calculate statistics of the calculated numbers (b1, b0) and ratio (R); and an estimating section (44) configured to feed input data including the calculated statistics to a trained estimating model to output the degree of differentiation of the cell in tissue.
    Type: Grant
    Filed: May 24, 2021
    Date of Patent: January 2, 2024
    Assignee: APSAM Imaging Corp.
    Inventors: Kazuaki Nakane, Chaoyang Yan, Xiangxue Wang, Yao Fu, Haoda Lu, Xiangshan Fan, Michael D. Feldman, Anant Madabhushi, Jun Xu
  • Patent number: 11817204
    Abstract: Embodiments discussed herein facilitate determination of whether lesions are benign or malignant. One example embodiment is a method, comprising: accessing medical imaging scan(s) that are each associated with distinct angle(s) and each comprise a segmented region of interest (ROI) of that medical imaging scan comprising a lesion associated with a first region and a second region; providing the first region(s) of the medical imaging scan(s) to trained first deep learning (DL) model(s) of an ensemble and the second region(s) of the medical imaging scan(s) to trained second DL model(s) of the ensemble; and receiving, from the ensemble of DL models, an indication of whether the lesion is a benign architectural distortion (AD) or a malignant AD.
    Type: Grant
    Filed: December 9, 2020
    Date of Patent: November 14, 2023
    Assignees: Case Western Reserve University, The United States Government as Represented by The Department of Veteran Affairs
    Inventors: Anant Madabhushi, Nathaniel Braman, Tristan Maidment, Yijiang Chen
  • Patent number: 11810292
    Abstract: Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.
    Type: Grant
    Filed: September 30, 2020
    Date of Patent: November 7, 2023
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Nathaniel Braman, Jeffrey Eben
  • Patent number: 11798179
    Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions. The computer readable medium is configured to cause a processor to access an image volume of a rectum comprising a rectal tumor. A forward mapping is generated based on non-rigidly registering a healthy rectal atlas to the image volume. The forward mapping is inverted to generate an inverse mapping from the image volume to the healthy rectal atlas. Based on the inverse mapping, a plurality of deformation vectors, associated with a deformation within a rectal wall of the rectum, are determined. Magnitude based deformation features and orientation based deformation features are computed from the plurality of deformation vectors. One or more of the magnitude based deformation features and one or more of the orientation based deformation features are utilized to determine a response of a patient to a chemoradiation treatment.
    Type: Grant
    Filed: September 22, 2021
    Date of Patent: October 24, 2023
    Assignee: Case Western Reserve University
    Inventors: Anant Madabhushi, Jacob Antunes, Zhouping Wei, Pallavi Tiwari, Satish E. Viswanath, Charlems Alvarez Jimenez
  • Publication number: 20230326582
    Abstract: The present disclosure, in some embodiments, relates to a method. The method includes using a first machine learning model to generate a first medical prediction associated with a lesion in a medical scan using one or more intra-lesional radiomic features associated with the lesion and the one or more peri-lesional radiomic features associated with a peri-lesional region around the lesion. A second machine learning model is used to generate a second medical prediction associated with the lesion using one or more pathomic features associated with the lesion. A combined medical prediction associated with the lesion is generated using the first medical prediction and the second medical prediction as inputs to a third model.
    Type: Application
    Filed: May 25, 2023
    Publication date: October 12, 2023
    Inventors: Pranjal Vaidya, Anant Madabhushi, Kaustav Bera
  • Patent number: 11676703
    Abstract: Embodiments discussed herein facilitate building and/or employing model(s) for determining tumor prognoses based on a combination of radiomic features and pathomic features. One example embodiment can perform actions comprising: providing, to a first machine learning model, at least one of: one or more intra-tumoral radiomic features associated with a tumor or one or more peri-tumoral radiomic features associated with a peri-tumoral region around the tumor; receiving a first predicted prognosis associated with the tumor from the first machine learning model; providing, to a second machine learning model, one or more pathomic features associated with the tumor; receiving a second predicted prognosis associated with the tumor from the second machine learning model; and generating a combined prognosis associated with the tumor based on the first predicted prognosis and the second predicted prognosis.
    Type: Grant
    Filed: October 12, 2020
    Date of Patent: June 13, 2023
    Assignee: Case Western Reserve University
    Inventors: Pranjal Vaidya, Anant Madabhushi, Kaustav Bera
  • Publication number: 20230148068
    Abstract: The present disclosure in some embodiments relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including obtaining one or more digitized endomyocardial biopsy (EMB) images from a patient having had a heart transplant; extracting a plurality of histological features from the one or more digitized EMB images; and applying a machine learning predictive model to operate on the plurality of histological features to generate a prediction for the patient. The prediction includes a grade or a clinical trajectory associated with the patient.
    Type: Application
    Filed: November 7, 2022
    Publication date: May 11, 2023
    Inventors: Anant Madabhushi, Sara Arabyarmohammadi, Cai Yuan, Eliot G. Peyster, Kenneth B. Margulies, Michael D. Feldman, Priti Lal
  • Publication number: 20230146428
    Abstract: Systems, methods, and apparatus are provided for generating an atlas image of a branched structure and predicting a likelihood of success of certain treatments based on the atlas image. In one example, a method includes registering a plurality of images of instances of a branched structure to generate an aligned image for a cohort, wherein the branched structure comprises a central structure and at least one primary branch connected to the central structure; for each primary branch of the branched structure, iteratively registering respective portions of a plurality of images containing the primary branch to generate an aligned image portion of the primary branch; and applying a control grid of the aligned image portion of the primary branch to respective image portions containing the central structure and the other primary branches prior to iteratively registering a next primary branch; and generating an atlas image for the cohort based on the aligned image portions.
    Type: Application
    Filed: November 10, 2022
    Publication date: May 11, 2023
    Inventors: Anant Madabhushi, Golnoush Asaeikheybari, Amogh Hiremath, Mina K. Chung, John Barnard
  • Patent number: 11645753
    Abstract: Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.
    Type: Grant
    Filed: September 25, 2020
    Date of Patent: May 9, 2023
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Catherine Jayapandian, Yijiang Chen, Andrew Janowczyk, John Sedor, Laura Barisoni
  • Patent number: 11610304
    Abstract: Embodiments discussed herein facilitate building and/or employing a clinical-radiomics score for determining tumor prognoses based on a combination of a radiomics risk score generated by a machine learning model and clinico-pathological factors. One example embodiment can perform actions comprising: accessing a medical imaging scan of a tumor; segmenting a peri-tumoral region around the tumor; extracting one or more intra-tumoral radiomic features from the tumor and one or more peri-tumoral radiomic features from the peri-tumoral region; providing the one or more intra-tumoral radiomic features and the one or more peri-tumoral radiomic features to a trained machine learning model; receiving a radiomic risk score (RRS) associated with the tumor from the machine learning model; determining a clinical-radiomics score based on the RRS and one or more clinico-pathological factors; and generating a prognosis for the tumor based on the clinical-radiomics score.
    Type: Grant
    Filed: October 12, 2020
    Date of Patent: March 21, 2023
    Assignee: Case Western Reserve University
    Inventors: Pranjal Vaidya, Anant Madabhushi, Kaustav Bera
  • Publication number: 20230059717
    Abstract: The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.
    Type: Application
    Filed: August 19, 2022
    Publication date: February 23, 2023
    Inventors: Anant Madabhushi, Andrew Janowczyk
  • Patent number: 11576640
    Abstract: Embodiments discussed herein facilitate determination of risk of recurrence of atrial fibrillation (AF) after ablation based on fractal features. One example embodiment is configured to generate a binary mask of at least a portion of a CT scan of a heart of a patient with AF; compute one or more radiomic fractal-based features from at least one of the binary mask or the portion of the CT scan; provide the one or more radiomic fractal-based features to a trained machine learning (ML) classifier; and receive a prediction from the trained ML classifier of whether or not the AF will recur after AF ablation, wherein the prediction is based at least in part on the one or more radiomic fractal-based features.
    Type: Grant
    Filed: September 28, 2020
    Date of Patent: February 14, 2023
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Marjan Firouznia, Mina K. Chung, Albert Feeny
  • Patent number: 11574404
    Abstract: Embodiments include controlling a processor to perform operations, the operations comprising accessing a digitized image of a region of tissue (ROT) demonstrating cancerous pathology; extracting a set of radiomic features from the digitized image, where the set of radiomic features are positively correlated with programmed death-ligand 1 (PD-L1) expression; providing the set of radiomic features to a machine learning classifier; receiving, from the machine learning classifier, a probability that the region of tissue will experience cancer recurrence, where the machine learning classifier computes the probability based, at least in part, on the set of radiomic features; generating a classification of the region of tissue as likely to experience recurrence or non-recurrence based, at least in part, on the probability; and displaying the classification and at least one of the probability, the set of radiomic features, or the digitized image.
    Type: Grant
    Filed: February 18, 2019
    Date of Patent: February 7, 2023
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Pranjal Vaidya, Kaustav Bera, Prateek Prasanna, Vamsidhar Velcheti
  • Patent number: 11555877
    Abstract: Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.
    Type: Grant
    Filed: May 3, 2019
    Date of Patent: January 17, 2023
    Assignee: Case Western Reserve University
    Inventors: Pallavi Tiwari, Anant Madabhushi, Prateek Prasanna
  • Publication number: 20230005145
    Abstract: The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.
    Type: Application
    Filed: September 8, 2022
    Publication date: January 5, 2023
    Inventors: Anant Madabhushi, Cristian Barrera, German Corredor, Eduardo Romero
  • Patent number: 11540796
    Abstract: Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.
    Type: Grant
    Filed: March 25, 2019
    Date of Patent: January 3, 2023
    Assignees: Case Western Reserve University, The Cleveland Clinic Foundation
    Inventors: Anant Madabhushi, Michael LaBarbera, Thomas Atta-Fosu, Mina Chung
  • Publication number: 20220405918
    Abstract: Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.
    Type: Application
    Filed: February 15, 2022
    Publication date: December 22, 2022
    Inventors: Anant Madabhushi, Sepideh Azarianpour Esfahani, Haider Mahdi
  • Publication number: 20220404364
    Abstract: The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4/6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4/6 inhibitor therapy for HR+ metastatic breast cancer.
    Type: Application
    Filed: November 23, 2021
    Publication date: December 22, 2022
    Inventors: Anant Madabhushi, Nathaniel Braman, Siddharth Kunte, Alberto Montero