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510(k) Data Aggregation
K Number
K221552Device Name
EFAI ChestSuite XR Pneumothorax Assessment System
Manufacturer
Date Cleared
2022-11-08
(161 days)
Product Code
Regulation Number
892.2080Why did this record match?
Applicant Name (Manufacturer) :
Ever Fortune AI Co., Ltd.
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdparty
Intended Use
EFAI PNXXR is a software workflow tool designed to aid the clinical assessment of adult (22 years of age or older) Posteroanterior (PA) view Chest X-Ray cases with features suggestive of pneumothorax in the medical care environment. EFAI PNXXR analyzes cases using an artificial intelligence algorithm to identify suspected findings. It makes case-level output available to a PACS/workstation for worklist prioritization or triage. EFAI PNXXR is not intended to direct attention to specific portions or anomalies of an image. Its results are not intended to be used on a stand-alone basis for clinical decision-making nor is it intended to rule out pneumothorax or otherwise preclude clinical assessment of X-Ray cases.
Device Description
EFAI ChestSuite XR Pneumothorax Assessment System, herein referred to as EFAI PNXXR, is a radiological computer-assisted triage and notification software system. The software uses deep learning techniques to automatically analyze PA chest x-rays and sends notification messages to the picture archiving and communication system (PACS)/workstation to allow suspicious findings of pneumothorax to be identified.
The device is intended to provide a passive notification through the PACS/workstation to the radiologists indicating the existence of a case that may potentially benefit from the prioritization. It does not mark, highlight, or direct users' attention to a specific location on the original chest X-ray. The device aims to aid in prioritization and triage of radiological medical images only.
The deployment environment is recommended to be in a local network with an existing hospitalgrade IT system in place. EFAI PNXXR should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:
- · Local network setting of input and output destinations;
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