# US 10,504,227 B1 · Application of deep learning for medical imaging evaluation

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- Number: US 10,504,227 B1
- Document ID: US10504227B1
- Status: Granted
- Kind: B1
- Application number: 16/546,412
- Filed: 2019-08-21
- Granted: 2019-12-10
- Earliest claimed: 2018-11-14
- Assignees: Qure.ai Technologies Private Limited
- Applicants: Qure.AI Technologies Private Limited
- Inventors: Sasank Chilamkurhy; Rohit Ghosh; Swetha Tanamala; Pooja Rao; Prashant Warier
- Web page: https://patentgrep.com/patent/US10504227B1

## CPC

- G06V 10/82: using neural networks
- A61B 5/7264: {Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems}
- G06F 18/24143: {Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]}
- G06F 18/2433: Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06N 20/00: Machine learning
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/764: using classification, e.g. of video objects
- G06T 2207/10081: Computed x-ray tomography [CT]
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30008: Bone
- G06T 2207/30016: Brain
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## 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.

## Claims

### Claim 1

A method for streamlining a head scan interpretation workflow by automating an initial screening and triage process, comprising:
receiving and processing images from CT head scan imaging procedure data;
detecting and localizing medical abnormalities of the images using a deep learning system,
wherein the deep learning system carried out by a computer is developed by the steps of:
selecting medical imaging scans and extracting the medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
predicting confidences for a presence of each type of medical abnormalities; and
generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities; and
prioritizing an assignment of a medical evaluation to an evaluator based on the score generated for the images.

### Claim 2 (depends on 1)

The method of claim 1, wherein the CT head scan imaging procedure data are transmitted to a Picture Archiving and Communication System (PACS) via Digital Imaging and Communications in Medicine (DICOM).

### Claim 3 (depends on 2)

The method of claim 2, wherein DICOM images are anonymized on a local server.

### Claim 4 (depends on 3)

The method of claim 3, wherein anonymized DICOM images are transferred to a cloud server.

### Claim 5 (depends on 4)

The method of claim 4, wherein detecting and localizing medical abnormalities of the images and prioritizing the assignment of a medical evaluation to an evaluator are carried out in the cloud server to obtain a prioritization status for DICOM images.

### Claim 6 (depends on 5)

The method of claim 5, wherein the prioritization status for DICOM images is transferred to the local server and a Health Level Seven (HL7) message is constructed in response to the prioritization status for DICOM images.

### Claim 7 (depends on 6)

The method of claim 6, wherein the HL7 message is transmitted to a Radiology Information System (RIS).

### Claim 8 (depends on 1)

The method of claim 1, wherein the deep learning algorithm comprises a convolutional neural network architecture.

### Claim 9 (depends on 8)

The method of claim 8, wherein the architecture is modified by using multiple parallel fully connected layers.

### Claim 10 (depends on 1)

The method of claim 1, wherein the confidences at the slice-level are combined across slices using a fully connected layer to predict a scan-level confidence for the presence and types of the medical abnormalities.

### Claim 11 (depends on 1)

The method of claim 1, wherein the medical abnormalities comprise intracranial hemorrhage and each of the 5 types of intracranial hemorrhages including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), subdural intracranial hemorrhage (SDH), epidural hemorrhage (EDH), and subarachnoid hemorrhage (SAH); midline shift; mass effect; skull fractures, and calvarial fractures.

### Claim 12 (depends on 1)

The method of claim 1, wherein the head CT scans are preprocessed by windowing to three separate windows.

### Claim 13 (depends on 12)

The method of claim 12, wherein the three separate windows comprise brain window, bone window and subdural window.

### Claim 14 (depends on 1)

The method of claim 1, wherein an accuracy of the deep learning algorithm for detecting the medical abnormalities is validated by comparing with radiologist reports.

### Claim 15 (depends on 1)

The method of claim 1, wherein the medical imaging procedure data comprise image data captured by an imaging modality and order data provided from a request for a radiological image read.

### Claim 16

An automated head CT scan screening and triage system for streamlining a head scan interpretation workflow, comprising:
a deep learning system carried out by a computer to detect and localize medical abnormalities on non-contrast head CT scans, wherein the deep learning system is developed by the steps of:
selecting medical imaging scans and extracting medical abnormalities using natural language processing (NLP) algorithms, wherein each type of the medical abnormalities is annotated at scan, slice and pixel levels;
training a slice-wise deep learning algorithm with the selected medical imaging scans to segment pixel-level annotated scans;
training the deep learning algorithm with the selected medical imaging scans to generate slice-level confidences;
predicting confidences for a presence of each type of medical abnormalities; and
generating a score that corresponds to a level of recognition of the medical abnormalities and outputting a mask representing the precise location and extent of the medical abnormalities;
wherein the automated head CT scan screening and triage system is configured to receive and process images from CT head scan imaging procedure data;
wherein the automated head CT scan screening and triage system is configured to detect and localize the medical abnormalities of the images using the deep learning system; and
wherein the automated head CT scan screening and triage system is configured to prioritize an assignment of a medical evaluation to an evaluator based on the score generated for the images.

### Claim 17 (depends on 16)

The system of claim 16, wherein the CT head scan imaging procedure data are transmitted to a Picture Archiving and Communication System (PACS) via Digital Imaging and Communications in Medicine (DICOM).

### Claim 18 (depends on 17)

The system of claim 17, wherein DICOM images are anonymized on a local server.

### Claim 19 (depends on 18)

The system of claim 18, wherein anonymized DICOM images are transferred to a cloud server.

### Claim 20 (depends on 19)

The system of claim 19, wherein detecting and localizing medical abnormalities of the images and prioritizing the assignment of a medical evaluation to an evaluator are carried out in the cloud server to obtain a prioritization status for DICOM images.

### Claim 21 (depends on 20)

The system of claim 20, wherein the prioritization status for DICOM images is transferred to the local server and a Health Level Seven (HL7) message is constructed in response to the prioritization status for DICOM images.

### Claim 22 (depends on 21)

The system of claim 21, wherein the HL7 message is transmitted to a Radiology Information System (RIS).

### Claim 23 (depends on 16)

The system of claim 16, wherein the deep learning algorithm comprises a convolutional neural network architecture.

### Claim 24 (depends on 23)

The system of claim 23, wherein the architecture is modified by using multiple parallel fully connected layers.

### Claim 25 (depends on 16)

The system of claim 16, wherein the confidences at the slice-level are combined across slices using a fully connected layer to predict the scan-level confidence for the presence and types of the medical abnormalities.

### Claim 26 (depends on 16)

The system of claim 16, wherein the medical abnormalities comprise intracranial hemorrhage and each of the 5 types of intracranial hemorrhages including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), subdural intracranial hemorrhage (SDH), epidural hemorrhage (EDH), and subarachnoid hemorrhage (SAH); midline shift; mass effect; skull fractures and calvarial fractures.

### Claim 27 (depends on 16)

The system of claim 16, wherein the head CT scans are preprocessed by windowing to three separate windows.

### Claim 28 (depends on 27)

The system of claim 27, wherein the three separate windows comprise brain window, bone window and subdural window.

### Claim 29 (depends on 16)

The system of claim 16, wherein an accuracy of the deep learning algorithm for detecting the medical abnormalities is validated by comparing with the radiologist reports.

### Claim 30 (depends on 16)

The system of claim 16, wherein the medical imaging procedure data comprise image data captured by an imaging modality and order data provided from a request for a radiological image read.