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The OsteoGram 2000 is indicated for use in estimating bone mineral density (BMD). The estimate of BMD and t-score may be used as an aid to the physician in determining fracture risk, and for monitoring changes in bone mass over time.

The use of the currently marketed device involves exposures and processing of ordinary x-rays of the hand and scanning and analysis of those x-rays at a central processing lab. The present 510(k) is for a modification to the device, one that would allow the analysis of the x-rays at the same hospital or clinic where the x-rays were taken using off-the-shelf scanner and computer equipment. The proposed device is basically a change in software to provide user help and enhanced records keeping capability.

Here's an analysis of the provided text regarding the OsteoGram® 2000, addressing your requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific numerical acceptance criteria for the OsteoGram® 2000. It generally claims "performs as well as the currently marketed device" and that "The precision and accuracy of the naïve operators and central lab operators were almost identical."

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

• Sample Size for Test Set: The document states "scanning and analysis of several patient x-rays." This indicates a very small, unspecified number, which is insufficient for rigorous statistical testing. It does not provide an exact count.
• Data Provenance: The document does not specify the country of origin. It does not explicitly state whether the data was retrospective or prospective, but the phrasing "several patient x-rays" suggests these were existing (retrospectively gathered) x-rays used for evaluation, rather than a new prospective study.

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

The document refers to "naïve operators and central lab operators" performing the scanning and analysis. It does not specify:

• The number of either "naïve" or "central lab" operators.
• Their qualifications (e.g., radiologist, years of experience). The term "naïve operators" suggests less experienced users, while "central lab operators" implies experienced individuals, but no specific professional credentials are given.

4. Adjudication Method for the Test Set

The document does not mention an adjudication method (such as 2+1, 3+1, or none) for establishing ground truth or resolving discrepancies in readings. It seems to compare the device's output to that of the predicate device or a "central lab" without a formal adjudication process described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or described. The study compared performance between "naïve operators" and "central lab operators" using the new system and the predicate, but it does not detail an MRMC design to quantify human reader improvement with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The document describes the device as a "change in software" and mentions "the analysis software has been mounted is a lows the OsteoGram x-improved patient revorus managers managers they are made." The testing involved "scanning and analysis... by both naïve operators and central lab operators," indicating a human-in-the-loop scenario. Therefore, a standalone (algorithm only) performance study was not explicitly described. The comparison is between the new device (with human operators) and the predicate device (also with human operators).

7. The Type of Ground Truth Used

The ground truth is implicitly established by the "currently marketed device" or the "central lab operators." The study compares the performance of the new device against the performance of the predicate device (or the standard practice of central lab analysis with the predicate). It does not explicitly state an independent, gold-standard ground truth like pathology or specific clinical outcomes. The goal was to demonstrate equivalence to the established method.

8. The Sample Size for the Training Set

The document does not provide any information about a training set since it focuses on a software modification to an existing device rather than the development of a new AI model from scratch that requires separate training. The "software" changes described are related to "user help and enhanced records keeping capability" and deployment, rather than a deep learning or machine learning algorithm requiring extensive training data in the modern sense.

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

As no training set is mentioned (see point 8), there is no information on how ground truth for a training set was established.

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This premarket notification covers the LAN and WAN teleradiology concept (transmission), and study information distribution functions. The CompuMed DICOM client is an on-site (LAN) or offsite (WAN or dial up) station for viewing patient information and images and reporting.

The CompuMed DICOM client is a viewing station manufactured by CompuMed, Inc. The CompuMed DICOM client is an on-site (LAN) or offsite (WAN or dial up) station for viewing patient information and images and reporting. It supports industry standard information transmission and communication protocols.

This document is a 510(k) summary for the CompuMed DICOM Client, submitted in 1995. It primarily focuses on demonstrating substantial equivalence to existing teleradiology products and does not contain the detailed performance study information requested.

Therefore, I cannot populate the table or answer most of your specific questions because the provided text does not include the type of information you are asking for regarding acceptance criteria, study details, sample sizes, ground truth establishment, or expert involvement.

Here's a breakdown of what can be extracted and why other questions cannot be answered:

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

• Cannot be provided. The document does not define specific acceptance criteria for performance metrics (e.g., accuracy, sensitivity, specificity, image quality parameters) nor does it report the device's performance against such criteria. The submission focuses on substantial equivalence based on intended use and technical characteristics.

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

• Cannot be provided. The document does not describe a performance study with a test set of data.

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):

• Cannot be provided. No reference to a test set or ground truth establishment by experts is made.

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

• Cannot be provided. No test set or expert adjudication is described.

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:

• Cannot be provided. This document is for a DICOM client (a viewing station and communication software), not an AI-powered diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is irrelevant and not mentioned.

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

• Cannot be provided. The device is a viewing station and communication software, not a standalone diagnostic algorithm.

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

• Cannot be provided. No performance studies or ground truth definitions are mentioned.

8. The sample size for the training set:

• Cannot be provided. The device is not an AI/machine learning model, so there is no concept of a "training set."

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

• Cannot be provided. Irrelevant as it's not an AI/ML model.

Summary of available information from the document:

• Device Name: CompuMed DICOM Client
• Intended Use: LAN and WAN teleradiology concept (transmission), and study information distribution functions. Viewing patient information and images and reporting.
• Technological Characteristics: DICOM conformant, low-cost viewing workstation for on-site and off-site viewing and reporting.
• Substantial Equivalence Claimed Against:
  • CompuMed PC Teleradiology product
  • Dejarnette Teleshare
  • Line Imaging WinRad
  • SIENET DRC (Magic View workstation)
• Rationale for Substantial Equivalence: "The CompuMed DICOM client products have the same intended use and similar technical characteristics as the devices listed above."
• Safety and Effectiveness Concerns: "The device labeling contains instructions for use. It includes indications for use and cautions. This information provides for safe and effective use of the device."

This 510(k) submission is a "pre-AI era" filing and therefore does not contain the types of performance study details now common for AI/ML-based medical devices.

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