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The Accudxa 2 is a dual-energy x-ray (DXA) device indicated for use in estimating the bone density of the middle finger of the non-dominant hand (BMD). This BMD value is a relative indicator of bone density elsewhere in the body. Accudxa 2 BMD estimates, t-score and z-score can be used as an aid to the physician in determining fracture risk and for monitoring changes in bone mass over time.

The Accudxa 2 Model 7200 is a Dual Energy X-ray Absorptiometer (DXA) screening device. The device is intended to calculate an index of bone mineral density in the middle finger of the non-dominant hand. By changing the high voltage on the X-ray tube, two energies are produced. Each of the two settings produces an image of the finger and each image is analyzed using various algorithms to produce a value of bone mineral density (BMD) and bone mineral content (BMC). These values are compared with a normative database, vielding a t-score and a z-score. The t-score is the number of standard deviations that the patient is above or below the mean of a reference sample of young healthy individuals, which can be used by the physician as an aid to diagnose osteoporosis or osteopenia and to estimate fracture risk. The z-score is the number of standard deviations that the patient is above or below the mean of a reference sample of individuals of the same age as the patient, which can be used by the physician as an aid to diagnose other disorders affecting bone mass.

The BMD Test interaction with the operator is through a touchscreen LCD panel and a membrane switch/indicator panel on the front of the device. BMD Test options are presented to the operator through messages on the touchscreen.

The x-ray system state and x-ray controls are presented to the operator via the membrane switch/indicator panel on the front of the device.

The Accudxa 2 is a Class II medical device and a Class II laser product.

Here's an analysis of the provided text regarding the Accudxa 2 Bone Densitometer, focusing on acceptance criteria and study details.

Important Note: The provided 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a standalone study for the Accudxa 2. The core of their argument is that the Accudxa 2 uses the same algorithms, normative database, and sensor as its predicate (accuDEXA Model 7100). Therefore, the "study" demonstrating its performance is largely a comparison to the predicate's known performance.

Acceptance Criteria and Reported Device Performance

Given the nature of the 510(k) submission, explicit "acceptance criteria" for a new, independent study on the Accudxa 2 are not detailed. Instead, the performance is evaluated by demonstrating equivalence to the predicate device.

Study Information (Based on provided text)

1. Sample size used for the test set and the data provenance:

   • The document does not explicitly state a sample size for an independent clinical test set for the Accudxa 2.
   • The primary method for demonstrating performance is through comparison to a well-established predicate device (accuDEXA Model 7100). The implication is that the performance of the Accudxa 2 is directly tied to, and validated by, the long-standing performance data and regulatory clearances of the predicate.
   • The normative database, which would involve data from individuals, is stated to be the same as that used by the predicate. Its provenance is not specified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable/Not mentioned. The approach is based on technological equivalence to a predicate device, not on a new, independent clinical study requiring expert ground truth for a test set.

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

   • Not applicable/Not mentioned.

4. 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. This device is a bone densitometer, not an AI-assisted diagnostic tool that would involve "human readers" or AI assistance in that context. It provides quantitative measurements (BMD, t-score, z-score) for physician interpretation.

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

   • Yes, in essence. The comparison to the predicate relies on the device's ability to consistently produce equivalent BMD readings using the "same algorithms" and "same sensor" as the predicate device. The performance of the algorithms themselves (which are the same as the predicate) is the standalone performance they are leveraging from the predicate device history.

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

   • The "ground truth" for the device's core functionality (BMD measurement) relies on the known BMC and BMD values of standardized calibration phantoms. The text states: "The Accudxa 2 uses the same calibration phantom set to ensure that BMD readings obtained from the device are equivalent to those of the accuDEXA Model 7100." The accuracy of these phantoms would have been established through a primary standard (e.g., gravimetric analysis or other highly precise methods). For clinical interpretation (t-scores, z-scores), the ground truth is derived from the normative database, which represents a statistical distribution of bone density in healthy populations.

7. The sample size for the training set:

   • Not applicable/Not mentioned for the Accudxa 2 itself. The device uses the same algorithms and normative database as its predicate. The training of these original algorithms and the compilation of the normative database would have occurred with the predicate device, not described here.

8. How the ground truth for the training set was established:

   • Not applicable/Not mentioned directly for the Accudxa 2. For the predicate device's algorithms and normative database, this would have involved:
     • Normative Database: Measurement of bone density in a large, diverse population subgroup defined as "healthy young individuals" and "age-matched individuals," with data potentially involving specific inclusion/exclusion criteria.
     • Algorithm Development: Likely involved imaging of phantoms with known properties and potentially human subjects, with ground truth established through other validated methods (e.g., in vitro assays for BMC, or comparison to established DXA systems).

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