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The Innova systems are indicated for use in generating fluoroscopic images of human anatomy for vascular angiography, diagnostic and interventional procedures, and optionally, rotational imaging procedures. They are also indicated for generating fluoroscopic images of human anatomy for cardiology, diagnostic, and interventional procedures. They are intended to replace fluoroscopic images obtained through image intensifier technology. Those devices are not intended for mammography applications.

Innova CT is a software option, which reconstructs 3D volumes from Rotational Fluoroscopy acquisition to provide images that assist the physician in diagnosis, surgical planning, interventional procedures and treatment follow-up.

Innova CT is intended for imaging bone and soft tissues as well as other internal body structures.

Innova CT is not intended for mammography applications

The Innova CT imaging is offered as an option for Innova 4100 Innova 3100, Innova 2100 IQ (2100-IQ), 4100 IQ and Innova 3100 IQ. The Digital Fluoroscopic Imaging Systems are designed to perform fluoroscopic x-ray examinations. The detector is comprised of amorphous silicon with a cesium iodine scintillator. The resulting digital image can be sent through a Fiber Channel link to an acquisition system then to network (in using DICOM) for applications such as post-processing, printing, viewing and archiving. Digital Fluoroscopic Imaging System consists of a monoplane positioner, a vascular or cardiac table, an X-RAY system and a digital detector.

The provided text is a 510(k) summary for the GE Healthcare Innova CT option. It focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study on specific acceptance criteria and performance metrics for the Innova CT option itself. Therefore, many of the requested details about acceptance criteria and study design are not explicitly present in the document.

However, based on the information provided, here's what can be extracted and what remains unknown:

1. A table of acceptance criteria and the reported device performance

This information is not explicitly provided in the document. The document focuses on demonstrating substantial equivalence to a predicate device (Siemens DynaCT) based on similar indications for use, design, materials, and energy sources. It does not list quantitative acceptance criteria or performance metrics for the Innova CT option.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample size for the test set: "Sample clinical data for the Innova CT option are included in this submission." The exact sample size is not specified.
• Data provenance: The country of origin and whether the data is retrospective or prospective are not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided. The document mentions "Sample clinical data" but does not describe the process of establishing ground truth or the involvement of experts for this data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The focus is on substantial equivalence to an existing device, not on comparing human reader performance with and without AI assistance. The Innova CT is described as a software option that reconstructs 3D volumes, not an AI for interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

The Innova CT is described as a "software option, which reconstructs 3D volumes from Rotational Fluoroscopy acquisition to provide images that assist the physician in diagnosis, surgical planning, interventional procedures and treatment follow-up." This implies it's an algorithm-only function (reconstruction of 3D volumes), designed to provide images for physician use, rather than an AI for diagnostic interpretation that would typically have a "human-in-the-loop" performance study. Therefore, in the context of its function, its performance would inherently be "standalone" for its image reconstruction task. However, a formal "standalone performance study" with specific metrics for image quality or diagnostic accuracy based on the reconstructed images is not detailed in this summary.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

This information is not explicitly stated. The document mentions "Sample clinical data," but the method for establishing the ground truth for this data is not described.

8. The sample size for the training set

The document does not mention a training set. As this is a 510(k) submission for a device that reconstructs 3D volumes from fluoroscopic acquisitions, it may not involve machine learning in the conventional sense that requires a "training set" for model development. The design validation mentions "Failures Modes Effects Analysis (FMEA) process," which is a risk management tool, not a method for training a machine learning algorithm.

9. How the ground truth for the training set was established

Since a training set is not mentioned, this information is not available.

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