CT Polyp Detection Using Dual CNN Architecture for Medical Imaging
Summary
USPTO published patent application US20260096796A1 for a CT polyp detection system employing dual convolutional neural network (CNN) architectures. The method receives CT scan image data, segments colon portions using a first CNN to identify candidate polyps, then validates candidates through multiplanar analysis using a second model to classify each as a polyp or non-polyp. The four named inventors are Aly Farag, Mohamed Yousuf, Samir Harb, and Asem Ali. The application was filed October 8, 2025 and published April 9, 2026.
About this source
GovPing monitors USPTO Patent Applications - Health Informatics (G16H) for new healthcare & life sciences regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 146 changes logged to date.
What changed
USPTO published a patent application for a computer-implemented method of detecting colorectal polyps in CT scan imagery using two sequential CNN models. The first CNN segments CT images and identifies candidate polyps from axial views; the second CNN analyzes multiplanar views (axial, sagittal, coronal) to classify whether each candidate is a polyp. The invention also generates a user interface displaying classified results.
Medical device manufacturers and healthcare AI developers in the medical imaging space should note this intellectual property filing, which covers a specific workflow for automated polyp detection. Competitors developing similar CADe (computer-aided detection) systems may wish to review the claim scope for freedom-to-operate purposes. The patent does not impose regulatory obligations but represents prior art for subsequent similar inventions.
Archived snapshot
Apr 23, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
SYSTEMS AND METHODS OF FEATURE DETECTION WITHIN MEDICAL IMAGES
Application US20260096796A1 Kind: A1 Apr 09, 2026
Inventors
Aly Farag, Mohamed Yousuf, Samir Harb, Asem Ali
Abstract
A method including receiving image data including a plurality of CT scan images of at least a portion of a subject; segmenting the CT scan images to identify portions of each image corresponding to the subject's colon; analyze axial views of the segmented CT scan images to identify a candidate polyp using a first CNN; analyzing at least two of axial views, sagittal views, and coronal views CT scan images corresponding to the candidate polyp using a second model to classify the candidate polyp as a polyp or not a polyp; and generating a user interface that includes the classified candidate polyp.
CPC Classifications
A61B 6/5217 A61B 6/032 A61B 6/50 A61B 6/5294 G06T 7/0012 G06T 7/11 G16H 30/20 G06T 2207/10081 G06T 2207/20081 G06T 2207/20084 G06T 2207/30032 G06V 10/26 G06V 10/764 G06V 10/7715 G06V 10/82 G06V 2201/031
Filing Date
2025-10-08
Application No.
19353005
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