Automatic Detection of Potential Pleural Effusion

Simple SummaryContent extracted from patent full text and abstract with AI.

This invention describes an automated method for detecting potential pleural effusions (fluid in the pleural cavity) in medical images of a patient's chest. By analyzing image data to identify and measure the boundaries of the rib cage, lungs, and mediastinum, the method can determine areas where a pleural effusion might be present, improving detection accuracy and enabling earlier intervention.

Use CasesContent extracted from patent full text and abstract with AI.

• Automatic analysis of chest X-rays or CT scans in hospitals and clinics to screen for pleural effusions.
• Integration into radiology software to aid radiologists in diagnosis.
• Use in telemedicine or remote diagnostics to triage patients with suspected respiratory issues.
• Deployment in emergency departments for rapid assessment of chest images.
• Supporting research studies that analyze the prevalence or risk of pleural effusion in certain populations.

BenefitsContent extracted from patent full text and abstract with AI.

• Increases accuracy and consistency in pleural effusion detection compared to manual review.
• Saves time for radiologists and reduces the workload.
• Enables earlier identification and treatment of pleural effusions, potentially improving patient outcomes.
• Can be integrated into existing imaging workflows and software platforms.
• Facilitates large-scale screening and monitoring of patient populations for respiratory illnesses.

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Inventors & Applicants

Patent Abstract

A method for automatically identifying a potential pleural effusion in medical image data of a thorax of a patient from a scan by means of a medical scanner is provided. It includes at least the steps of accepting rib cage detection data of the rib cage of the patient from the image data, which rib cage detection data include rib cage extent data of a rib cage extent of the interior of the rib cage, accepting lung detection data of the lung of the patient from the image data, which lung detection data comprise lung extent data of a lung extent of the external boundary of the lung, accepting mediastinum detection data of all organs of the mediastinum in the thorax (Th) of the patient from the image data, which mediastinum detection data comprise mediastinum extent data of a mediastinum extent of the external boundary of the mediastinum, and subtracting the lung extent and the mediastinum extent from the rib cage extent while forming pleural effusion identification data.