Image quality compliance tool
The technology relates to a methods and systems for improving medical imaging procedures. An example method includes receiving a first set of quality metrics for a plurality of medical images acquired at a first imaging facility; receiving a second set of quality metrics for a second plurality of medical images acquired at a second imaging facility; comparing the first set of quality metrics to the second set of quality metrics; based on the comparison of the first set of quality metrics to the second set of quality metrics, generating a benchmark for at least one metric in the first set of quality metrics and the second set of quality metrics; generating facility data based on the generated benchmark and the first set of quality metrics; and sending the facility data to the first imaging facility.
1. A method for improving medical imaging procedures, the method comprising:
receiving, by a central computer system from a first imaging facility, a first set of quality metrics for a plurality of medical images acquired at the first imaging facility;
receiving, by the central computer system from a second imaging facility, a second set of quality metrics for a second plurality of medical images acquired at the second imaging facility;
comparing, by the central computer system, the first set of quality metrics to the second set of quality metrics;
based on the comparison of the first set of quality metrics to the second set of quality metrics, generating, by the central computer system, a benchmark for at least one metric in the first set of quality metrics and the second set of quality metrics;
generating, by the central computer system, facility data based on the generated benchmark and the first set of quality metrics; and
sending, by the central computer system, the facility data to the first imaging facility.
2. The method of claim 1 , further comprising:
generating a training recommendation based on the generated benchmark and the first set of quality metrics;
receiving, from the first imaging facility, a subsequent set of quality metrics for a plurality of medical images acquired at the first facility after the sending of the generated training recommendation;
comparing the subsequent set of quality metrics to the first set of quality metrics; and
based on the comparison of the subsequent set of quality metrics to the first set of quality metrics, generating an effectiveness rating for the generated training.
3. The method of claim 2 , further comprising:
receiving, from the first imaging facility, a subsequent set of quality metrics for a plurality of medical images acquired at the first facility after the sending of the generated training recommendation;
comparing the subsequent set of quality metrics to the first set of quality metrics to determine a trend for at least one quality metric; and
based on determined trend for the at least one quality metric, generating a trend warning.
4. The method of claim 1 , wherein the quality metrics are based on positioning metrics generated from the plurality of medical images.
5. The method of claim 1 , further comprising providing the first set of quality metrics and the second set of quality metrics as inputs to an unsupervised machine learning algorithm to identify additional patterns within the sets of quality metrics.
6. The method of claim 1 , wherein the first set of quality metrics are received via a web application managed by the central computer system and the training is sent via the web application.
7. The method of claim 1 , wherein the quality metrics are based on patient movement.
8. The method of claim 7 , wherein at least one quality metric is based on a movement signal that is generated by the following operations:
generating, by a force sensor, a force signal indicating a measure of force applied superior to human tissue being compressed between a compression paddle and an imaging detector to capture an image of the human tissue; and
filtering, by a movement detection circuit, a movement signal from the force signal indicating a measure of movement of the compressed human tissue.
9. A central computer system, comprising:
at least one processing unit; and
memory operatively in communication with the at least processing unit, the memory storing instructions that, when executed by the at least one processing unit, are configured to cause the system to perform the following set of operations:
receiving, from a first imaging facility, a first set of quality metrics for a plurality of medical images acquired at the first imaging facility;
receiving, from a second imaging facility, a second set of quality metrics for a second plurality of medical images acquired at the second imaging facility;
comparing, by the central computer system, the first set of quality metrics to the second set of quality metrics;
based on the comparison of the first set of quality metrics to the second set of quality metrics, generating a benchmark for at least one metric in the first set of quality metrics and the second set of quality metrics;
generating a training recommendation based on the generated benchmark and the first set of quality metrics; and
sending the generated training recommendation to the first facility.
10. The system of claim 9 , wherein the set of operations further comprises:
receiving, from the first imaging facility, a subsequent set of quality metrics for a plurality of medical images acquired at the first facility after the sending of the generated training recommendation;
comparing the subsequent set of quality metrics to the first set of quality metrics; and
based on the comparison of the subsequent set of quality metrics to the first set of quality metrics, generating an effectiveness rating for the generated training.
11. The system of claim 9 , wherein the set of operations further comprises:
receiving, from the first imaging facility, a subsequent set of quality metrics for a plurality of medical images acquired at the first facility after the sending of the generated training recommendation;
comparing the subsequent set of quality metrics to the first set of quality metrics to determine a trend for at least one quality metric; and
based on determined trend for the at least one quality metric, generating a trend warning.
12. The system of claim 11 , wherein the trend warning is based on a rate of the determined trend.
13. The system of claim 9 , wherein the set of operations further comprises providing the first set of quality metrics and the second set of quality metrics as inputs to an unsupervised machine learning algorithm to identify additional patterns within the sets of quality metrics.
14. The system of claim 9 , wherein the first set of quality metrics are received via a web application managed by the central computer system and the training is sent via the web application.
15. The system of claim 9 , wherein the set of operations further comprise providing a dashboard via a web application to the first facility and the second facility.
16. The system of claim 15 , wherein the dashboard displays quality metrics received from the first facility compared to the benchmark.
17. The system of claim 9 , wherein receiving the first set of quality metrics for a plurality of medical images includes receiving identification information for the plurality of medical images.