Advance AI-powered Lung Cancer Diagnosis

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Credit: Clinician and research scientist evaluate lung nodules using Optellum software

Optellum and GE Healthcare signed a letter of intent to cooperatively develop and commercialize lung cancer diagnosis using AI. The companies will collaborate on the development and deployment of a diagnostic solution for lung cancer to improve early detection and patient outcomes. GE Healthcare will provide AI-based solutions to aid in the diagnosis of lung cancer.

The collaboration will help healthcare providers to determine the malignancy of lung nodules so they can expedite subsequent treatments.

Optellum’s Virtual Nodule Clinic is an AI-powered software that helps to identify and score malignancy probability of lung nodules. It can also be used to enhance the specificity and sensitivity of indeterminate nodule assessments.

This development will be a game changer for cancer diagnosis, as it will reduce the need to perform biopsies or surgical resection procedures, thereby accelerating diagnostic processes.

The Virtual Nodule Clinic is claimed to be the only AI-assisted diagnosis software approved by the US Food and Drug Administration (FDA) for early-stage lung cancer.

GE Healthcare and Optellum are collaborating to integrate AI solutions powered by GE Healthcare’s Edison platform into the Virtual Nodule Clinic. They will also work together to distribute the platform and bring Optellum’s Lung Cancer Prediction AI results into GE Healthcare’s existing workflow including Picture Archiving and Communication Systems (PACS) and Computed Tomography (CT).

GE Healthcare oncology solutions general manager Ben Newton said: “The precise diagnosis of lung cancer can greatly improve patient prognosis.

“The integration of imaging and medical device data from the Edison Platform with AI-enabled solutions like the one offered by the Optellum Virtual Nodule Clinic has the potential to streamline clinician workflows and advance our goal of making precision healthcare, taking the right action at the right time for every patient, at scale, as widely accessible as possible.”

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