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FDA Clears AI Software for Liver Attenuation on Abdominal CT


The AI-enabled HealthFLD software demonstrated a 77.8 percent sensitivity rate and a 93.2 percent specificity rate for diagnosing moderate hepatic steatosis on contrast-enhanced CT scans in a recent study of over 2,900 patients.

The Food and Drug Administration (FDA) has granted 510(k) clearance for HealthFLD, an artificial intelligence (AI)-powered software, which provides automated liver attenuation analysis based on contrast and non-contrast computed tomography (CT) scans.1

HealthFLD reportedly provides adjunctive support in detecting fatty liver, the initial stage of hepatic steatosis, which may be an early indicator of metabolic dysfunction-associated steatotic liver disease (MASLD), a disease that reportedly affects 24 percent of adults in the United States, according to Nanox, the developer of HealthFLD.

In a 2023 retrospective study that examined the use of HealthFLD for detecting moderate hepatic steatosis on contrast-enhanced CT scans, researchers noted a 77.8 percent sensitivity rate and a 93.2 percent specificity rate at less than 80 HU.2

“We are proud to offer HealthFLD as the third product of Nanox AI’s suite of cutting-edge, AI-powered population health solutions designed to confront chronic diseases of great public health concern head-on and potentially improve health outcomes,” said Erez Meltzer, the chief executive officer of Nanox.


1. Nanox. Nanox receives FDA clearance for HealthFLD, an advanced AI-based software empowering clinicians in assessment of fatty liver. GlobeNewswire. Available at: https://www.globenewswire.com/news-release/2024/02/13/2828223/0/en/Nanox-Receives-FDA-Clearance-for-HealthFLD-an-Advanced-AI-Based-Software-Empowering-Clinicians-in-Assessment-of-Fatty-Liver.html . Published February 13, 2024. Accessed February 13, 2024.

2. Pickhardt PJ, Blake GM, Kimmel Y, et al. Detection of moderate hepatic steatosis on portal venous phase contrast-enhanced CT: evaluation using an automated artificial intelligence tool. AJR Am J Roentgenol. 2023;221(6):748-758.

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