# US 11,113,577 B1 · Systems and methods for detecting laterality of a medical image

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- Number: US 11,113,577 B1
- Document ID: US11113577B1
- Status: Granted
- Kind: B1
- Application number: 16/803,209
- Filed: 2020-02-27
- Published: 2021-09-02
- Granted: 2021-09-07
- Earliest claimed: 2020-02-27
- Assignees: GE Precision Healthcare LLC
- Applicants: GE Precision Healthcare LLC
- Inventors: Khaled Salem Younis; Ravi Soni; Katelyn Rose Nye; Gireesha Chinthamani Rao; John Michael Sabol; Yash N. Shah
- Web page: https://patentgrep.com/patent/US11113577B1

## CPC

- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G06F 18/2155: {characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling}
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/2413: based on distances to training or reference patterns
- G06F 18/2431: Multiple classes
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06T 7/0012: {Biomedical image inspection}
- G06V 10/764: using classification, e.g. of video objects
- G06V 10/7784: {based on feedback from supervisors}
- G06V 10/82: using neural networks
- G06V 20/00: Scenes; Scene-specific elements (control of digital cameras H04N23/60)
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 30/40: for processing medical images, e.g. editing
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G06N 3/045: Combinations of networks
- G06T 2207/10116: X-ray image
- G06T 2207/20081: Training; Learning
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/30061: Lung
- G06V 2201/03: Recognition of patterns in medical or anatomical images
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

An x-ray image laterality detection system is provided. The x-ray image laterality detection system includes a detection computing device. The processor of the computing device is programmed to execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs. The process is also programmed to receive an unclassified x-ray image, analyze the unclassified x-ray image using the neural network model, and assign a laterality class to the unclassified x-ray image. If the assigned laterality class is not target laterality, the processor is programmed to adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality and output the corrected x-ray image. If the assigned laterality class is the target laterality, the processor is programmed to output the unclassified x-ray image.

## Claims

### Claim 1

An x-ray image laterality detection system, comprising:
a detection computing device comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to:
execute a neural network model for analyzing x-ray images, wherein the neural network model is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs;
receive an unclassified x-ray image;
analyze the unclassified x-ray image using the neural network model;
assign a laterality class to the unclassified x-ray image based on the analysis;
if the assigned laterality class is not target laterality, the at least one processor is programmed to:
adjust the unclassified x-ray image to derive a corrected x-ray image having the target laterality; and
output the corrected x-ray image; and
if the assigned laterality class is the target laterality, the at least one processor is programmed to output the unclassified x-ray image.

### Claim 2 (depends on 1)

The system of claim 1, wherein the unclassified x-ray image is acquired without a lead marker.

### Claim 3 (depends on 1)

The system of claim 1, wherein the neural network model is a deep learning neural network model.

### Claim 4 (depends on 1)

The system of claim 1, wherein the neural network model includes a global average pooling layer.

### Claim 5 (depends on 1)

The system of claim 1, further comprising a metadata editor configured to:
update metadata associated with the unclassified x-ray image based on the detected laterality class of the unclassified x-ray image; and
generate metadata associated with the output x-ray image.

### Claim 6 (depends on 1)

The system of claim 1, wherein said at least one processor is further programmed to:
generate a digital marker indicating laterality of the output x-ray image; and
overlay the digital marker on the output x-ray image.

### Claim 7 (depends on 1)

The system of claim 1, further comprising a user interface manager configured to:
receive a user input; and
generate a user policy based on the user input,
wherein said at least one processor is further programmed to correct laterality of the unclassified x-ray image based on the user policy.

### Claim 8 (depends on 1)

The system of claim 1, wherein the unclassified x-ray image is preprocessed.

### Claim 9 (depends on 1)

The system of claim 1, wherein said at least one processor is configured to alert a user that the unclassified x-ray image does not have the target laterality and the output x-ray image is the corrected x-ray image.

### Claim 10 (depends on 1)

The system of claim 1, wherein said at least one processor is configured to provide an alert indicating the detected laterality of the unclassified x-ray image.