Lung screening assessment system

• US 10,553,311 B2
• Filed: 08/30/2017
• Issued: 02/04/2020
• Est. Priority Date: 05/25/2017
• Status: Active Grant

• Current Assignee: Enlitic, Inc.
• Original Assignee: Enlitic, Inc.
• Inventors: Lyman, Kevin, Bernard, Devon, Yao, Li, Covington, Ben, Almeida, Diogo, Basham, Brian, Howard, Jeremy, Upton, Anthony, Zedlewski, John
• Primary Examiner(s): Tsai, Tsung Yin
• Application Number:
  US15/690,786
• Publication Number:
  US 20180338741A1
• Time in Patent Office: 888 Days
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 6/032 Transmission computed tomography [CT]
• CPC Class Codes: A61B 6/4233 using matrix detectors
• CPC Class Codes: A61B 6/463 characterised by displaying multiple images or images and diagnostic data on one display
• CPC Class Codes: A61B 6/468 allowing annotation or message recording
• CPC Class Codes: A61B 6/50 specially adapted for specific body parts; specially adapted for specific clinical applications
• CPC Class Codes: A61B 6/503 for diagnosis of the heart
• CPC Class Codes: A61B 6/505 for diagnosis of bone
• CPC Class Codes: A61B 6/5217 extracting a diagnostic or physiological parameter from medical diagnostic data
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: A61B 6/5294 involving using additional data, e.g. patient information, image labeling, acquisition parameters
• CPC Class Codes: A61B 6/563 involving image data transmission via a network
• CPC Class Codes: A61B 8/468 allowing annotation or message recording
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/565 involving data transmission via a network
• CPC Class Codes: G01T 1/247 Detector read-out circuitry for processing gain or off-set correction H04N
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06F 3/048 : Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/167 : Audio in a user interface, e.g. using voice commands for navigating, audio feedback
• CPC Class Codes: G06F 40/169 : Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/197 : Version control for software G06F8/71
• CPC Class Codes: G06F 40/247 : Thesauruses; Synonyms
• CPC Class Codes: G06F 40/279 : Recognition of textual entities
• CPC Class Codes: G06F 40/30 : Semantic analysis
• CPC Class Codes: G06F 40/56 : Natural language generation
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/04 : Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/084 : Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/10 : Office automation; Time management
• CPC Class Codes: G06Q 10/103 : Workflow collaboration or project management
• CPC Class Codes: G06Q 50/22 : Social work or social welfare, e.g. community support activities or counselling services
• CPC Class Codes: G06T 11/003 : Reconstruction from projections, e.g. tomography
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10081 : Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/10104 : Positron emission tomography [PET]
• CPC Class Codes: G06T 2207/10116 : X-ray image
• CPC Class Codes: G06T 2207/10132 : Ultrasound image
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30061 : Lung
• CPC Class Codes: G06T 2207/30068 : Mammography; Breast
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/0016 : involving temporal comparison
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06V 10/751 : Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/761 : Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 10/98 : Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns
• CPC Class Codes: G06V 2201/03 : Recognition of patterns in medical or anatomical images
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: H04L 67/01 : Protocols
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04N 23/30 : for generating image signals from X-rays
• CPC Class Codes: H04N 5/32 : Transforming X-rays cameras or camera modules for generating image signals from X-rays H04N23/30; circuitry of SSIS for transforming X-rays into image signals H04N25/30

Abstract

A lung screening assessment system is operable to receive a chest computed tomography (CT) scan that includes a plurality of cross sectional images. Nodule classification data of the chest CT scan is generated by utilizing a computer vision model that is trained on a plurality of training chest CT scans to identify a nodule in the plurality of cross sectional images and determine an assessment score. A lung screening report that includes the assessment score of the nodule classification data is generated for display on a display device associated with a user of the lung screening assessment system.

Claims

1. A lung screening assessment system, comprising:

a processing system that includes a processor; and

a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations comprising;

receiving a first chest computed tomography (CT) scan that includes a plurality of cross sectional images;

generating nodule classification data of the first chest CT scan by utilizing a computer vision model to identify a first nodule in the plurality of cross sectional images and determine an assessment score, wherein the computer vision model is trained on a plurality of training chest CT scans;

generating a first subset of chest CT scans from the plurality of training chest CT scans by applying a similarity function to determine that a set of nodules included in the first subset of chest CT scans compare favorably to the first nodule;

retrieving a set of patient records corresponding to the first subset of chest CT scans wherein each of the set of patient records includes a plurality of record entries and a corresponding plurality of dates;

identifying a subset of the plurality of dates in the each of the set of patient records that are more recent than a date associated with the one of the plurality of chest CT scans corresponding to the each of the set of patient records;

identifying a subset of the plurality of record entries of the each of the set of patient records that correspond to the subset of the plurality of dates;

generating longitudinal data corresponding to each of the set of patient records based on each corresponding subset of the plurality of record entries;

generating a lung screening report for display on a display device associated with a user of the lung screening assessment system, wherein the lung screening report includes the assessment score of the nodule classification data, data from at least one patient record in the set of patient records and the longitudinal data; and

selecting at least one cross sectional image from each chest CT scan of the first subset of chest CT scans for display on the display device in conjunction with the lung screening report.

2. The lung screening assessment system of claim 1, wherein generating the nodule classification data includes calculating at least one of:

a Fleischner score or a Lung-RADS score, and wherein the assessment score indicates the at least one of;

the Fleischner score or the Lung-RADS score.

3. The lung screening assessment system of claim 1, wherein generating the lung screening report includes:

retrieving first patient risk factor data corresponding to the chest CT scan;

retrieving patient risk factor data corresponding to each of the set of patient records; and

selecting a subset of patient records by identifying corresponding patient risk factor data that compares favorably to the first patient risk factor data;

wherein the lung screening report includes only data from patient records in the subset of patient records.

4. The lung screening assessment system of claim 1, wherein the operations further comprise:

generating diagnosis prediction data corresponding to the first chest CT scan based on the longitudinal data, wherein the lung screening report includes the diagnosis prediction data.

5. The lung screening assessment system of claim 1, wherein generating the longitudinal data includes:

calculating a set of longitudinal quality scores corresponding to each patient record in the set of patient records;

generating a ranking of the set of patient records based on the set of longitudinal quality scores; and

removing at least one patient record from the set of patient records that corresponds to at least one lowest ranking;

wherein the lung screening report includes only data from remaining patient records in the set of patient records.

6. The lung screening assessment system of claim 5, wherein calculating the set of longitudinal quality scores is based on a number of subsequent chest CT scans included in the each of the set of patient records.

7. The lung screening assessment system of claim 5, wherein calculating the set of longitudinal quality scores is based on determining a duration of time between a first scan date of each chest CT scan in the first subset of chest CT scans and a second scan date of a most recent subsequent chest CT scan included in the corresponding patient record in the set of patient records.

8. The lung screening assessment system of claim 5, wherein a first longitudinal quality score in the set of longitudinal quality scores corresponds to a first patient record, wherein a second longitudinal quality score in the set of longitudinal quality scores corresponds to a second patient record, and wherein the first longitudinal quality score is more favorable than a second longitudinal quality score in response to determining that the first patient record includes biopsy data and the second patient record does not include biopsy data.

9. A method for execution by a lung screening assessment system that includes a processor, the method comprising:

receiving a first chest computed tomography (CT) scan that includes a plurality of cross sectional images;

generating nodule classification data of the first chest CT scan by utilizing a computer vision model to identify a first nodule in the plurality of cross sectional images and determine an assessment score, wherein the computer vision model is trained on a plurality of training chest CT scans;

generating a first subset of chest CT scans from the plurality of training chest CT scans by applying a similarity function to determine that a set of nodules included in the first subset of chest CT scans compare favorably to the first nodule;

retrieving a set of patient records corresponding to the first subset of chest CT scans wherein each of the set of patient records includes a plurality of record entries and a corresponding plurality of dates;

identifying a subset of the plurality of dates in the each of the set of patient records that are more recent than a date associated with the one of the plurality of chest CT scans corresponding to the each of the set of patient records;

identifying a subset of the plurality of record entries of the each of the set of patient records that correspond to the subset of the plurality of dates;

generating longitudinal data corresponding to each of the set of patient records based on each corresponding subset of the plurality of record entries;

generating a lung screening report for display on a display device associated with a user of the lung screening assessment system, wherein the lung screening report includes the assessment score of the nodule classification data, data from at least one patient record in the set of patient records and the longitudinal data; and

selecting at least one cross sectional image from each chest CT scan of the first subset of chest CT scans for display on the display device in conjunction with the lung screening report.

10. The method of claim 9, wherein generating the nodule classification data includes calculating at least one of:

a Fleischner score or a Lung-RADS score, and wherein the assessment score indicates the at least one of;

the Fleischner score or the Lung-RADS score.

11. The method of claim 9 wherein generating the lung screening report includes:

retrieving first patient risk factor data corresponding to the chest CT scan;

retrieving patient risk factor data corresponding to each of the set of patient records; and

selecting a subset of patient records by identifying corresponding patient risk factor data that compares favorably to the first patient risk factor data;

wherein the lung screening report includes only data from patient records in the subset of patient records.

12. The method of claim 9, wherein generating the longitudinal data includes:

calculating a set of longitudinal quality scores corresponding to each patient record in the set of patient records;

generating a ranking of the set of patient records based on the set of longitudinal quality scores; and

removing at least one patient record from the set of patient records that corresponds to at least one lowest ranking;

wherein the lung screening report includes only data from remaining patient records in the set of patient records.

13. The method of claim 12, wherein calculating the set of longitudinal quality scores is based on determining a duration of time between a first scan date of each chest CT scan in the first subset of chest CT scans and a second scan date of a most recent subsequent chest CT scan included in the corresponding patient record in the set of patient records.

14. An article of manufacture that includes a tangible storage medium that stores operational instructions, that when executed by a processor, causes the processor to:

receive a first chest computed tomography (CT) scan that includes a plurality of cross sectional images;

generate nodule classification data of the first chest CT scan by utilizing a computer vision model to identify a first nodule in the plurality of cross sectional images and determine an assessment score, wherein the computer vision model is trained on a plurality of training chest CT scans;

generate a first subset of chest CT scans from the plurality of training chest CT scans by applying a similarity function to determine that a set of nodules included in the first subset of chest CT scans compare favorably to the first nodule;

retrieve a set of patient records corresponding to the first subset of chest CT scans wherein each of the set of patient records includes a plurality of record entries and a corresponding plurality of dates;

identify a subset of the plurality of dates in the each of the set of patient records that are more recent than a date associated with the one of the plurality of chest CT scans corresponding to the each of the set of patient records;

identify a subset of the plurality of record entries of the each of the set of patient records that correspond to the subset of the plurality of dates;

generate longitudinal data corresponding to each of the set of patient records based on each corresponding subset of the plurality of record entries;

generate a lung screening report for display on a display device, wherein the lung screening report includes the assessment score of the nodule classification data, data from at least one patient record in the set of patient records and the longitudinal data; and

select at least one cross sectional image from each chest CT scan of the first subset of chest CT scans for display on the display device in conjunction with the lung screening report.