公开(公告)号：US20250148773A1

公开(公告)日：2025-05-08

申请号：US18935742

申请日：2024-11-04

Applicant: Qure.ai Technologies Private Limited

Inventor： Preetham Putha ,  Swetha Tanamala ,  Sahil Sahil ,  Ravi Kumar Kushawaha

IPC: G06V10/82 ,  G06V10/26 ,  G06V10/764 ,  G06V40/10 ,  G16H15/00

Abstract: The present disclosure relates to a system (100) and method for providing bounding box to fractured body parts in musculoskeletal X-ray images. The system (100) comprises a memory unit (203) and processor (201) with a data collection module (205) gathering input X-ray data (101). Additionally, it includes a fracture detection module (206) consisting of a segmentation model (207) and an object detection model (208). The segmentation model (207) determines segmentation scores to each pixel in the X-ray image and generates a segmentation mask (404) by comparing the segmentation score of each pixel (403) with a segmentation threshold of a fracture. Simultaneously, the object detection model (208) determines the rectangular coordinates of one or more body parts within the X-ray image (101). The fracture detection module (206) then overlaps the segmentation mask (404) with the body part coordinates, resulting in bounding boxes for each fractured body part.

公开(公告)号：US20210327055A1

公开(公告)日：2021-10-21

申请号：US16889412

申请日：2020-06-01

Applicant: Qure.ai Technologies Private Limited

Inventor： Preetham Putha ,  Manoj Tadepalli ,  Bhargava Reddy ,  Tarun Raj ,  Ammar Jagirdar ,  Pooja Rao ,  Prashant Warier

IPC: G06T7/00 ,  G06T7/70 ,  G06K9/62 ,  G06T7/11 ,  G06K9/32 ,  G06F40/20 ,  A61B6/00 ,  C12Q1/689 ,  G16H10/40 ,  G16H30/40 ,  G16H50/20 ,  G16H50/50 ,  G16H50/70 ,  G16H50/80

Abstract: This disclosure generally pertains to systems and methods for detection of infectious respiratory diseases by implementation of an automated X-rays-based triage approach alongside algorithmic clinical sample pooling for molecular diagnosis. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in chest X-ray imaging data. The chest X-ray imaging data is used to guide the pooling strategy of clinical samples for a molecular test.

公开(公告)号：US12299887B1

公开(公告)日：2025-05-13

申请号：US18935765

申请日：2024-11-04

Applicant: Qure.ai Technologies Private Limited

Inventor： Preetham Putha ,  Swetha Tanamala ,  Sahil Sahil ,  Ravi Kumar Kushawaha

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/82 ,  G06V40/10

Abstract: An invention relates to a system (100) for classifying presence of fracture and body parts on musculoskeletal X-ray. The system (100) is based on an artificial intelligence for targeting fracture classification and body part. Further, the system comprises a data collection module (205) and a fracture classification module (206). Furthermore, the fracture classification module (206) may comprise a classification model (207) and a segmentation model (208). The classification model (207) may be configured to generate a classification score and the segmentation model (208) may be configured to generate segmentation score. The fracture classification module (206) may be configured to generate a fracture score (403) and a body part score (404). The fracture classification module (206) may be configured to compare said scores (403, 404) against threshold values to classify fracture presence and classify body parts. The system (100) may enhance the efficiency and accuracy of fracture diagnosis in X-ray images.

公开(公告)号：US12148158B2

公开(公告)日：2024-11-19

申请号：US18371460

申请日：2023-09-22

Applicant: Qure.ai Technologies Private Limited

Inventor： Prashant Warier ,  Rohan Sahu ,  Ashish Mittal ,  Kautuk Trivedi ,  Preetham Putha ,  Manoj Tadepalli

IPC: G06T7/00 ,  G06T7/11 ,  G16H50/20

Abstract: The present subject matter discloses a system and method for automatically detecting and quantifying a plaque/stenosis in a vascular ultrasound scan data in real time using Deep learning models. The system receives a video data and selects one or more frames/images for further processing to detect and quantify the plaque in the artery. Based on the selected one or more frames, the system detects a region of interest (ROI) and further processes the ROI. The system selects end points of a deposits of the plaque by taking a maximum length of the plaque in the artery/plaque boundary and determines the orientation of the vascular ultrasound scan. Based on the orientation and the selected end points, the system determines a vessel/artery boundary to identify a size of the plaque. Based on the determined vessel boundary and the orientation, the system determines plaque segments and measures parameters of the plaque.

公开(公告)号：US11521321B1

公开(公告)日：2022-12-06

申请号：US17457465

申请日：2021-12-03

Applicant: Qure.ai Technologies Private Limited

Inventor： Prashant Warier ,  Ankit Modi ,  Preetham Putha ,  Prakash Vanapalli ,  Vikash Challa

IPC: G06T7/00 ,  G06T7/62 ,  G06T5/00 ,  G06T7/11 ,  G06V10/25 ,  G06V10/26 ,  G06V10/82 ,  G06V10/75 ,  A61B6/03 ,  A61B6/00 ,  G16H30/40 ,  G16H50/30 ,  G16H50/20 ,  G06T7/20 ,  G06T7/40

Abstract: Disclosed is a system and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.

公开(公告)号：US10733727B2

公开(公告)日：2020-08-04

申请号：US16268694

申请日：2019-02-06

Applicant: Qure.AI Technologies Private Limited

Inventor： Preetham Putha ,  Manoj Tadepalli ,  Bhargava Reddy ,  Tarun Nimmada ,  Pooja Rao ,  Prashant Warier

IPC: G06K9/00 ,  G06T7/00 ,  G06T7/73 ,  A61B6/00 ,  G06N3/08 ,  G06F40/30

Abstract: This disclosure generally pertains to methods and systems for processing electronic data obtained from imaging or other diagnostic and evaluative medical procedures. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems configured to detect and localize medical abnormalities on medical imaging scans by a deep learning algorithm.

公开(公告)号：US20240331872A1

公开(公告)日：2024-10-03

申请号：US18623075

申请日：2024-04-01

Applicant: Qure.ai Technologies Private Limited

Inventor： Charu Arora ,  Preetham Putha ,  Manoj Tadepalli

IPC: G16H50/30 ,  G06V10/25 ,  G06V10/26 ,  G06V10/70 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20

CPC classification number: G16H50/30 ,  G06V10/25 ,  G06V10/26 ,  G06V10/70 ,  G16H30/20 ,  G16H30/40 ,  G16H50/20 ,  G06V2201/031

Abstract: A system and a method for detection of a heart failure risk is disclosed. The system may comprise a processor and a memory. The system (101) may receive one or more target chest X-ray image of a user. The system (101) may analyze one or more target chest X-ray image to identify and enhance one or more visual parameters of one or more RoI's. The system (101) may perform an anatomical segmentation on the one or more ROI's to detect one or more medical abnormalities from a set of medical abnormalities using the trained artificial intelligence model. The system (101) may calculate a confidence score of the heart failure risk in real time using a set of parameters corresponding to the detected one or more medical abnormalities from the set of medical abnormalities and further detect the heart failure risk for the user based on the confidence score.

公开(公告)号：US11967079B1

公开(公告)日：2024-04-23

申请号：US18379194

申请日：2023-10-12

Applicant: Qure.ai Technologies Private Limited

Inventor： Shubham Kumar ,  Arjun Agarwal ,  Satish Kumar Golla ,  Swetha Tanamala ,  Preetham Putha ,  Sasank Chilamkurthy ,  Prashant Warier

IPC: G06T7/00 ,  G06T5/50 ,  G06T7/11 ,  G06V10/25 ,  G06V10/764

CPC classification number: G06T7/0012 ,  G06T5/50 ,  G06T7/11 ,  G06V10/25 ,  G06V10/764 ,  G06T2207/10081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06T2207/30016 ,  G06T2207/30101

Abstract: The present subject matter discloses a system and method for detecting Large Vessel Occlusion (LVO) on a Computational Tomography Angiogram (CTA) automatically. the system comprises a vascular-territory-segmentation module, an ICV segmentation module, MCA-LVO classifier and ICA-LVO classifier. The vascular territory segmentation module is configured to receive a set of CTA images and to mark a territory of vascular segments in the ICV region for each slice of the ROI. The ICV segmentation module is configured to process each slice of the ROI. The processed slices of the ROI are combined to develop a CTA image after application of MIP and the developed CTA image is segmented into a Middle Cerebral Artery (MCA) region and an Internal Cerebral Artery (ICA) region. The MCA-LVO and ICA-LVO classifiers determine presence of the LVO on the received MCA and ICA region using Deep Learning techniques and accordingly the presence of the LVO is reported.

公开(公告)号：US20220245795A1

公开(公告)日：2022-08-04

申请号：US17207598

申请日：2021-03-19

Applicant: Qure.ai Technologies Private Limited

Inventor： Preetham Putha ,  Manoj Tadepalli ,  Bhargava Reddy ,  Tarun Raj ,  Ammar Jagirdar ,  Pooja Rao ,  Prashant Warier

IPC: G06T7/00 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: This disclosure generally pertains to methods and systems for automatically detecting acquisition errors in a medical image using machine learning. Certain embodiments relate to methods for the development of deep learning algorithms that perform machine recognition of specific features and conditions in imaging and other medical data. Another embodiment provides systems for detecting acquisition errors in an X-ray image, the system comprising a non-transitory computer-readable medium storing a preprocessing quality control module that, when executed by at least one electronic processor, is configured to generate associated classifications identifying characteristics of the medical image.

公开(公告)号：US11367191B1

公开(公告)日：2022-06-21

申请号：US17457470

申请日：2021-12-03

Applicant: Qure.ai Technologies Private Limited

Inventor： Prashant Warier ,  Ankit Modi ,  Preetham Putha ,  Prakash Vanapalli ,  Pradeep Kumar Thummala ,  Vijay Senapathi ,  Kunjesh Kumar

IPC: G06T7/00 ,  G16H30/40 ,  G16H15/00 ,  G06V10/25 ,  G06V10/764

Abstract: Disclosed is a system and a method for adapting a report of nodules in computed tomography (CT) scan image. A CT scan image may be resampled into a plurality of slices. A plurality of region of interests may be identified on each slice using an image processing technique. Subsequently, a plurality of nodules may be detected in each region of interest using the deep learning. Further, a plurality of characteristics associated with each nodule may be identified. The plurality of nodules may be classified into AI-confirmed nodules and AI-probable nodules based on a malignancy score. Further, feedback associated with the AI-confirmed nodules and the AI-probable may be received form a radiologist. Furthermore, data may be adapted based on the feedback. Finally, a report comprising adapted data may be generated.