Methods and systems for automatically selecting an implant for a patient

• US 10,275,876 B2
• Filed: 06/10/2016
• Issued: 04/30/2019
• Est. Priority Date: 06/12/2015
• Status: Active Grant

• Current Assignee: Merative US LP
• Original Assignee: International Business Machines Corporation
• Inventors: Reicher, Murray A., DeVries, Jon T., Ferro, Michael W., Sati, Marwan
• Primary Examiner(s): Bali, Vikkram
• Application Number:
  US15/179,457
• Publication Number:
  US 20160361121A1
• Time in Patent Office: 1,054 Days
• Field of Search: None
• CPC Class Codes: A61B 10/02 Instruments for taking cell samples or for biopsy A61B10/0038 and A61B10/0045 take precedence; needle locating or guiding means A61B17/3403; samplers for enzymology or microbiology C12M1/26; sampling or preparing biological specimens G01N33/48
• CPC Class Codes: A61B 2034/108 Computer aided selection or customisation of medical implants or cutting guides
• CPC Class Codes: A61B 2090/373 using light, e.g. by using optical scanners
• CPC Class Codes: A61B 2090/374 NMR or MRI
• CPC Class Codes: A61B 2090/3762 using computed tomography systems [CT]
• CPC Class Codes: A61B 2090/378 using ultrasound
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/015 By temperature mapping of body part
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/4312 Breast evaluation or disorder diagnosis A61B5/0091 takes precedence
• CPC Class Codes: A61B 5/441 Skin evaluation, e.g. for skin disorder diagnosis
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2411 based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• CPC Class Codes: G06F 18/285 Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
• CPC Class Codes: G06F 3/0482 : Interaction with lists of selectable items, e.g. menus
• CPC Class Codes: G06F 3/0488 : using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06F 40/169 : Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/205 : Parsing
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06T 11/008 : Specific post-processing after tomographic reconstruction, e.g. voxelisation, metal artifact correction
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10088 : Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30004 : Biomedical image processing
• CPC Class Codes: G06T 2207/30008 : Bone
• CPC Class Codes: G06T 2207/30016 : Brain
• CPC Class Codes: G06T 2207/30052 : Implant; Prosthesis
• CPC Class Codes: G06T 2207/30061 : Lung
• CPC Class Codes: G06T 2207/30068 : Mammography; Breast
• CPC Class Codes: G06T 2207/30088 : Skin; Dermal
• CPC Class Codes: G06T 7/0012 : Biomedical image inspection
• CPC Class Codes: G06T 7/0014 : using an image reference approach
• CPC Class Codes: G06V 10/764 : using classification, e.g. of video objects
• CPC Class Codes: G06V 2201/031 : of internal organs
• CPC Class Codes: G16H 10/20 : for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/10 : relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 30/20 : for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems for automatically selecting an implant for a patient. One system includes a server including an electronic processor and an interface for communicating with at least one data source. The electronic processor is configured to receive at least one image of the patient from the data source over the interface. The electronic processor is also configured to receive an intended location of the implant with reference to the at least one image. The electronic processor is also configured to automatically determine an anatomical structure based on the at least one image. The electronic processor is also configured to determine a preference. The electronic processor is also configured to automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference. The electronic processor is also configured to display the one or more suggested implants through a graphical user interface.

Claims

1. A system for automatically selecting an implant for a patient planning to undergo a procedure involving placement of the implant, the system comprising:

an electronic processor and an interface for communicating with at least one data source, the electronic processor configured to

receive at least one image of the patient from the data source over the interface,

receive an intended location of the implant with reference to the at least one image,

automatically determine an anatomical structure based on the at least one image,

determine a preference,

automatically select one or more suggested implants based on the intended location, the anatomical structure, and the preference,

automatically determine, using a model, a configuration and a position of at least one of the one or more suggested implants based on the intended location and the anatomical structure, wherein the model is generated via machine learning using training information, wherein the training information includes a plurality of images associated with implants, and the electronic processor is configured to use the model to automatically select the one or more suggested implants based on the intended location, the anatomical structure, and the preference,

display the one or more suggested implants through a graphical user interface by automatically generating and displaying a modified image based on the at least one image, the modified image including the anatomical structure and an overlay representing at least one of the one or more suggested implants based on the configuration and the position,

receive at least one adjustment to the modified image through one or more user input devices, and

update the model based on the at least one adjustment.

2. The system of claim 1, wherein the preference includes a preference of a physician performing the procedure.

3. The system of claim 1, wherein the preference includes a preference for a particular manufacturer of implants or a particular supplier of implants.

4. The system of claim 1, wherein the preference includes a preference for the procedure.

5. The system of claim 1, wherein the preference includes an available implant.

6. The system of claim 1, wherein the preference includes an available supplier or an available manufacturer.

7. The system of claim 1, wherein the preference includes a preference of an organization.

8. The system of claim 1, wherein the preference includes a preference of the patient.

9. The system of claim 1, wherein the electronic processor is further configured to generate a second graphical user interface for receiving and modifying the preference.

10. The system of claim 1, wherein the intended location includes at least one anatomical landmark.

11. The system of claim 1, wherein the electronic processor is further configured to automatically determine the intended location.

12. The system of claim 1, wherein the electronic processor is further configured to receive approval of the automatically-determined intended location through one or more peripheral devices.

13. The system of claim 1, wherein the electronic processor is configured to receive post-procedure data associated with at least one of the one or more suggested implants and automatically update the model based on the post-procedure data.

14. The system of claim 13, wherein the post-procedure data includes at least one selected from a group consisting of an image, a laboratory report, and a patient questionnaire.

15. The system of claim 1, wherein the electronic processor is further configured to receive a selection of one of the one or more suggested implants through one or more user input devices and automatically update the model based on the selection.

16. The system of claim 1, wherein the electronic processor is further configured to receive a selection of one of the one or more suggested implants through one or more user input devices and automatically electronically transmit instructions to a manufacturer system for manufacturing the implant based on the selection.

17. The system of claim 1, wherein the electronic processor is further configured to generate at least one statistic associated with at least one of the one or more suggested implants.

18. The system of claim 17, wherein the at least one statistic includes a usage rate of the implant and a success rate of the implant.