(146 days)
The proposed device: GE Lunar DXA Bone Densitometers with the enCORE version 18 software is intended for medical purposes to measure bone density, bone mineral content, and fat and lean tissue content by x-ray transmission measurements through the bone and adjacent tissues.
The changes proposed in this Premarket Notification will be used with the existing GEHC DXA Bone Densitometers, which utilize the enCORE software.
The GEHC DXA Bone Densitometers are composed of a scanner and a computer. The scanner comprises the x-ray source and detector, the patient scan table, the mechanical drive system, and the lowest level portions of the control system. The scanner is in communication with the computer, which is a standard PC. The computer runs the enCORE software, and thus controls the scanner, acquires scan data from the scanner, stores and analyzes the data, and interacts with the human operator.
GEHC DXA Bone Densitometers are used healthcare facilities and hospitals to measure bone mineral density (BMD) and body composition (%fat, fat mass, lean mass) using a technique called Dual-energy X-ray Absorptiometry or DXA. DXA measures the attenuation of x-rays of two different energy levels after they pass through the body of a subject. As bone, fat tissue, and lean tissue absorb the different energy x-rays at different rates, the relative attenuation of each x-ray energy is measured and used to calculate the composition of each pixel.
The provided text describes the GEHC DXA Bone Densitometers with enCORE version 18. This device is a bone densitometer that measures bone mineral density (BMD) and body composition using Dual-energy X-ray Absorptiometry (DXA). The submission is a 510(k) premarket notification, indicating that the device claims substantial equivalence to existing legally marketed predicate devices.
1. Table of Acceptance Criteria and Reported Device Performance
The document does not explicitly present a table of acceptance criteria with specific numerical thresholds for performance. Instead, it describes various software features and states that bench performance testing confirmed that design outputs met design input requirements and that results are "comparable" or "precise and accurate."
Here's an attempt to structure the information into a table based on the provided text, recognizing that precise criteria are often implied rather than explicitly stated with quantifiable targets in this type of summary:
| Feature/Study | Acceptance Criteria (Implied from text) | Reported Device Performance |
|---|---|---|
| DXAVision and Adult TBLH (Bone & Body Composition) | Differences in DXA results for neck-to-knees scans and total body measurements should be slightly larger but similar to precision studies in literature. | Differences are "slightly larger but similar to the results of precision studies of duplicate scans reported in the literature for both iDXA and Prodigy." |
| Integrated TBS iNsight (TBS and FRAX adjusted for TBS) | Results should be comparable between the integrated TBS and the previously cleared standalone TBS iNsight application. | "demonstrates that TBS and FRAX adjusted for TBS results are comparable between results calculated with the TBS integrated into enCORE 18 software and previously cleared TBS iNsight application." |
| CoreScan VAT/SAT (Visceral and Subcutaneous Adipose Tissue) | VAT/SAT results should be precise and accurate. | "demonstrate[s] that the VAT/SAT results for the updated CoreScan software option are precise and accurate." |
| Small Body Composition ROIs (Bone and Body Composition of Arms/Legs) | Accuracy of bone and body composition for upper and lower arms and legs should be confirmed. | "confirm the accuracy of bone and body composition of the upper and lower arms and legs." |
| Advanced Analytics | The feature should incorporate results of previously cleared features into user-defined equations and not affect safety or effectiveness. | "incorporates results of previously cleared features into user defined equations and does not affect the safety or effectiveness of the system." |
| Software Verification and Validation | Software should meet design specification requirements. | "confirmed that the software met the design specification requirements." |
2. Sample Size Used for the Test Set and Data Provenance
- DXAVision/Adult TBLH: The study involved a "heterogeneous sample of men and women with a diverse range of age, BMI, BMD, and body fat." No specific numerical sample size is provided.
- Integrated TBS iNsight: "internal engineering DXA data sets" were used. No specific numerical sample size.
- CoreScan: An "anthropomorphic phantom" was used. This is not human data.
- Small Body Composition ROIs: No specific sample size or provenance is mentioned for the verification results.
- Advanced Analytics: No specific sample size or provenance is mentioned.
- Software Verification and Validation: No specific sample size or provenance is mentioned besides "software verification and validation testing."
The provenance of human data, based on the description, is internal to GE Healthcare ("internal engineering DXA data sets"). It is implied to be retrospective, as it refers to existing data sets.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not explicitly mention the number or qualifications of experts used to establish ground truth for any of the described tests. The studies focus on comparing the updated software features to previous cleared versions or literature, or phantom data, rather than independent expert-adjudicated ground truth.
4. Adjudication Method for the Test Set
No adjudication method is mentioned for any of the described tests. The evaluations appear to be based on comparisons to predicate technology, internal software testing, or phantom measurements.
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
No MRMC comparative effectiveness study is mentioned. The device's improvements are focused on streamlined workflow, integration of features, and accuracy/precision of measurements, not on direct human reader improvement with AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Yes, the studies described are primarily standalone algorithmic performance evaluations. For example, the CoreScan bench testing used an anthropomorphic phantom, and the TBS iNsight validation compared algorithmic outputs. The DXAVision and Adult TBLH studies compare algorithm results to literature-reported precision, implying a standalone assessment of the algorithm's measurement precision.
7. The Type of Ground Truth Used
- DXAVision/Adult TBLH: The "ground truth" used for comparison appears to be the precision of "duplicate scans reported in the literature" for existing iDXA and Prodigy devices. This implies a comparison to established performance benchmarks rather than an independently adjudicated 'ground truth' for each case.
- Integrated TBS iNsight: The "ground truth" is the results from the "previously cleared TBS iNsight application."
- CoreScan: The "ground truth" is derived from the known characteristics of an "anthropomorphic phantom."
- Small Body Composition ROIs: "Accuracy of bone and body composition" is confirmed, suggesting comparison to a known standard or previously validated measurements, but the specific nature of this ground truth is not detailed.
- Advanced Analytics: No explicit ground truth is mentioned, as this feature is about user-defined equations using existing data.
8. The Sample Size for the Training Set
The document does not provide details about training sets for any of the software features. This submission is for updates to existing software (enCORE version 18) and integration of previously cleared applications, suggesting development was not based on a new, distinct training phase described in this summary.
9. How the Ground Truth for the Training Set Was Established
Since no training set details are provided, the method for establishing ground truth for a training set is not described. The focus of this 510(k) submission is on changes and integrations based on verified algorithms and features, rather than the initial development of a new AI model requiring a training set.
§ 892.1170 Bone densitometer.
(a)
Identification. A bone densitometer is a device intended for medical purposes to measure bone density and mineral content by x-ray or gamma ray transmission measurements through the bone and adjacent tissues. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.