Real-time sample aspiration fault detection and control
AssigneeSiemens Healthcare Diagnostics Inc.
InventorNarayanan Ramakrishnan
Life of this patent
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Abstract
Methods of early real-time detection or prediction of aspiration faults in an automated diagnostic analysis system include an artificial intelligence algorithm configured to use either cluster analysis or probabilistic graphical modeling based on an aspiration pressure measurement signal waveform. Aspiration faults may include short-volume aspiration and unwanted gel pick-up. These methods may allow for timely termination of an aspiration process so as to avoid or minimize possible detrimental downstream consequences such as faulty sample test results and/or instrument downtime for servicing and cleanup. Apparatus for early real-time detection or prediction of aspiration faults are provided as are other aspects.
Claims23
A method of detecting or predicting an aspiration fault in an automated diagnostic analysis system, the method comprising:
- performing aspiration pressure measurements via a pressure sensor as a liquid is being aspirated in the automated diagnostic analysis system;
- analyzing an aspiration pressure measurement signal waveform via a processor executing an artificial intelligence (AI) algorithm configured to perform:
- cluster analysis of the aspiration pressure measurement signal waveform, or
- probabilistic graphical modeling based on the aspiration pressure measurement signal waveform;
- and
- identifying and responding to an aspiration fault via the processor in response to the analyzing.
An automated aspirating and dispensing apparatus, comprising:
- a robotic arm;
- a probe coupled to the robotic arm;
- a pump coupled to the probe;
- a pressure sensor configured to perform aspiration pressure measurements as a liquid is being aspirated via the probe;
- and
- a processor configured to execute an artificial intelligence (AI) algorithm to detect or predict and respond to an aspiration fault during an aspiration process, the AI algorithm configured to analyze an aspiration pressure measurement signal waveform derived from the pressure sensor using cluster analysis or probabilistic graphical modeling.
A non-transitory computer-readable storage medium, comprising an artificial intelligence (AI) algorithm configured to detect or predict an aspiration fault based on analysis of an aspiration pressure measurement signal waveform using cluster analysis of the aspiration pressure measurement signal waveform or using probabilistic graphical modeling based on the aspiration pressure measurement signal waveform.
Dependencies are inferred from the claim text.