Systems and methods to deliver point of care alerts for radiological findings
Apparatus, systems, and methods to improve imaging quality control, image processing, identification of findings, and generation of notification at or near a point of care are disclosed and described. An example imaging apparatus includes a processor to at least: evaluate first image data with respect to an image quality measure; when the first image data satisfies the image quality measure, process the first image data using a trained learning network to generate a first analysis of the first image data; identify a clinical finding in the first image data based on the first analysis; compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and, when comparing identifies a change between the first analysis and the second analysis, trigger a notification at the imaging apparatus regarding the clinical finding to prompt a responsive action.
1. An imaging apparatus comprising:
a memory including first image data obtained in a first image acquisition and instructions; and
a processor to execute the instructions to at least:
evaluate the first image data with respect to an image quality measure;
when the first image data satisfies the image quality measure, process the first image data using a trained learning network to generate a first analysis of the first image data;
identify a clinical finding in the first image data based on the first analysis;
compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and
when comparing identifies a change between the first analysis and the second analysis, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the first image data.
2. The apparatus of claim 1 , wherein first image acquisition is associated with a protocol, and wherein the processor is to verify that an anatomy associated with the protocol is in the first image data.
3. The apparatus of claim 2 , wherein the processor is to verify that a position of the anatomy in the image is in compliance with the protocol.
4. The apparatus of claim 1 , wherein the trained learning network is to process the first image data using patient data from an electronic medical record.
5. The apparatus of claim 1 , wherein the processor is to obtain the second analysis from a cloud-based system.
6. The apparatus of claim 5 , wherein the imaging apparatus includes an edge device to connect to the cloud-based system.
7. The apparatus of claim 1 , further including a broker to connect the imaging apparatus to a health information system.
8. The apparatus of claim 7 , wherein the health information system includes a radiology information system, and wherein the processor is to generate a prioritization message for the radiology information system to prioritize an exam related to the patient as the responsive action in response to the clinical finding.
9. A non-transitory computer-readable storage medium in an imaging apparatus including instructions which, when executed, cause at least one processor in the imaging apparatus to at least:
evaluate the first image data with respect to an image quality measure;
when the first image data satisfies the image quality measure, process the first image data using a trained learning network to generate a first analysis of the first image data;
identify a clinical finding in the first image data based on the first analysis;
compare the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and
when comparing identifies a change between the first analysis and the second analysis, trigger a notification at the imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the first image data.
10. The non-transitory computer-readable storage medium of claim 9 , wherein first image acquisition is associated with a protocol, and wherein the instructions, when executed, cause the at least one processor to verify that an anatomy associated with the protocol is in the first image data.
11. The non-transitory computer-readable storage medium of claim 10 , wherein the instructions, when executed, cause the processor to verify that a position of the anatomy in the image is in compliance with the protocol.
12. The non-transitory computer-readable storage medium of claim 9 , wherein the trained learning network is to process the first image data using patient data from an electronic medical record.
13. The non-transitory computer-readable storage medium of claim 9 , wherein the instructions, when executed, cause the processor to obtain the second analysis from a cloud-based system.
14. The non-transitory computer-readable storage medium of claim 13 , wherein the imaging apparatus includes an edge device to connect to the cloud-based system.
15. The non-transitory computer-readable storage medium of claim 9 , wherein the instructions, when executed, cause the at least one processor to communicate with a broker to connect the imaging apparatus to a health information system.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the health information system includes a radiology information system, and wherein the instructions, when executed, cause the processor to generate a prioritization message for the radiology information system to prioritize an exam related to the patient as the responsive action in response to the clinical finding.
17. A computer-implemented method comprising:
evaluating, by executed an instruction with at least one processor, the first image data with respect to an image quality measure;
when the first image data satisfies the image quality measure, processing, by executing an instruction with the at least one processor, the first image data using a trained learning network to generate a first analysis of the first image data;
identifying, by executing an instruction with the at least one processor, a clinical finding in the first image data based on the first analysis;
comparing, by executing an instructing with the at least one processor, the first analysis to a second analysis, the second analysis generated from second image data obtained in a second image acquisition; and
when comparing identifies a change between the first analysis and the second analysis, triggering, by executing an instruction using the at least one processor, a notification at an imaging apparatus to notify a healthcare practitioner regarding the clinical finding and prompt a responsive action with respect to a patient associated with the first image data.
18. The method of claim 17 , wherein first image acquisition is associated with a protocol, and wherein the method further includes verifying that an anatomy associated with the protocol is in the first image data.
19. The method of claim 18 , further including verifying that a position of the anatomy in the image is in compliance with the protocol.
20. The method of claim 17 , further including generating a prioritization message for a radiology information system to prioritize an exam related to the patient as the responsive action in response to the clinical finding.