Search Results

The OEC 3D mobile fluoroscopy system is designed to provide fluoroscopic and digital spot images of adult and pediativ populations during diagnostic, interventional, and surgical procedures. Examples of a clinical application may include: orthopedic, gastrointestinal, endoscopic, neurologic, vascular, cardiac, citical care and emergency procedures.

The OEC 3D is a mobile fluoroscopic C-arm imaging system used to assist trained surgeons and other qualified physicians. The system is used to provide fluoroscopic X-ray images and volumetric reconstructions during diagnostic, interventional, and surgical procedures. These images help the physician visualize the patient's anatomy and interventional tools. This visualization helps to localize clinical regions of interest and pathology. The images provide real-time visualization and records of pre-procedure anatomy, in vivo-clinical activity and post-procedure outcomes.

The system is composed of two primary physical components. The first is referred to as the "C -Arm" because of its "C" shaped image gantry; the second is referred to as the "Workstation", and this is the primary user interface for the user to interact with the system. The C-arm has an interface tablet allowing a technician to interact with the system.

The C-arm is a stable mobile platform capable of performing linear motions (vertical, horizontal) and rotational motions (orbital, lateral) that allow the user to position the X-ray image chain at various angles and distances with respect to the patient anatomy to be imaged. The C-Arm is comprised of the high voltage generator, software, X-ray control, and a "C" shaped image gantry, which supports an X-ray tube and a Flat Panel Detector,

The workstation is a stable mobile platform with an articulating arm supporting a color image high resolution LCD display monitor. It also includes image processing equipment/software, recording devices, data input/output devices and power control systems.

On the C-Arm, the generator remains unchanged from the OEC Elite. This is also true for the 31 cm x 31 cm image receptor, consisting of a Thallium-doped Cesium Iodide [Cs] (TI)] solid state flat panel X-ray detector with Complementary Metal Oxide Semiconductor (CMOS) light imager. The X-ray tube housing and insert remains the same as on the predicate OEC Elite (K192819).

C-Arm functionality is managed by a digital flat tablet control panel mounted on the C-arm base. Motion is controlled by a joystick.

On the workstation, the main hardware includes a computer with integrated wireless capability and a dedicated computer for 3D reconstruction located within the storage bay. The OEC 3D employs the same software architecture and platform design that fully supports the flat panel detector as the OEC Elite and complies with IEC 60601-1. The OEC 3D includes the existing 2D imaging functionalities available on the OEC Elite including imaging and post processing applications.

The provided text does not contain specific acceptance criteria or a detailed study proving the device meets those criteria. Instead, it is a 510(k) premarket notification summary from the FDA, asserting substantial equivalence to predicate devices rather than demonstrating performance against explicit acceptance criteria with clinical data.

Here's an analysis of the information available in the document, and where details are explicitly not provided:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not provided in the document. The submission focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and non-clinical performance testing against general standards, rather than specific acceptance criteria for performance metrics.

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided. The document states that "clinical data is not required to demonstrate substantial equivalence" and that the device was evaluated using "engineering bench testing" and "non-clinical performance testing." Therefore, there is no discrete "test set" of patient data in the clinical sense mentioned.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

This information is not provided. Since clinical data was not used for the performance evaluation for substantial equivalence, no expert ground truth establishment for a test set is described.

4. Adjudication Method for the Test Set

This information is not provided. As no clinical test set with human assessments is described, no adjudication method is relevant or provided.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "The new performance claims did not require clinical data in order to establish safety or efficacy." Therefore, no effect size of human readers improving with AI vs. without AI assistance is reported.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The document describes non-clinical performance testing and engineering bench testing, which would broadly cover standalone algorithm performance in a technical sense (e.g., image quality metrics, reconstruction accuracy). However, it does not explicitly detail a "standalone performance study" in the context of clinical metrics like sensitivity, specificity, or reader performance. The focus is on demonstrating that the new 3D functionality is "substantially equivalent" to that of reference devices.

7. The Type of Ground Truth Used

The document does not describe the use of specific ground truth (expert consensus, pathology, outcomes data) in the context of clinical performance evaluation for substantial equivalence to the same extent as a traditional clinical study. The "ground truth" for the non-clinical performance testing would be derived from engineering specifications, phantom measurements, and compliance with standards (e.g., IEC 60601-1, NEMA XR-27). The 3D algorithm is stated to be "identical" to one of the reference devices (INNOVA IGS 5), implying its performance characteristics are assumed to be similar to that previously cleared device.

8. The Sample Size for the Training Set

This information is not provided. The document does not describe any machine learning or AI algorithm development that would involve a training set of data. The 3D algorithm is stated to be "identical" to one of the reference devices, suggesting it's an existing, proven algorithm rather than a newly trained one requiring a specific training set.

9. How the Ground Truth for the Training Set Was Established

This information is not provided, as no training set is mentioned.

Ask a specific question about this device