Search Results

Spinal Guides PACS is intended to perform: operations relating to the acceptance of spinal X-ray medical images and patient demographic information, their display, digital processing, review and editing, measurements report generation with respective storage and teleradiology exchange capabilities. They are intended to be used by a physician to view the images and use as an aid in calculation of alteration of motion segment integrity (AOMSI) of human spine. This device is not intended to be used for mammography images and does not require usage of 5 Mega pixel monitors.

The device is represented by a software computer program installed on regular computers. The device has two types of users: a web and a local user. The web user submits an image set for processing to the local user via internet to the device's database. The local user is viewing the images of the image set, process the image set and sends a PDF report with calculations for the web user.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Spinal Guides PACS device:

Important Note: The provided document is a Traditional 510(k) Submission for a PACS system, specifically focusing on spinal X-ray image analysis for Alteration of Motion Segment Integrity (AOMSI). It is crucial to understand that for a PACS system like this, the primary "performance" is often related to its ability to accurately display and facilitate measurements, not to provide an automated diagnostic output like many modern AI-driven devices. Therefore, the "acceptance criteria" and "device performance" will relate more to software functionality, display accuracy, and the ability to perform the intended measurements as per established medical protocols, rather than diagnostic accuracy metrics like sensitivity or specificity.

Acceptance Criteria and Study Analysis for Spinal Guides PACS

The provided 510(k) submission document for the Spinal Guides PACS system describes its intended use and a "testing" section. However, it does not explicitly state quantitative acceptance criteria or provide a detailed study report with specific performance metrics beyond general conformance to standards.

Instead, the submission primarily focuses on establishing substantial equivalence to a predicate device (Opal-Rad™) by demonstrating similar intended use, technological characteristics, and control methods. The "testing" mentioned is nonclinical software verification and validation, not a clinical performance study with human subjects or a direct comparison of its measurement accuracy against a "gold standard" using specific statistical thresholds.

Given this, I will interpret "acceptance criteria" in the context of what the document implies are the necessary characteristics for the device to be cleared, and "reported device performance" based on the confirmation of these characteristics being met.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a sample size for a "test set" in the way one would for a clinical performance study involving diagnostic accuracy. The testing described is "nonclinical tests for Spinal Guides PACS (software verification and validation)." This typically involves testing software functionality, data integrity, user interface, and compliance with specifications, rather than using a dataset of patient images with established ground truth for performance metrics.

Therefore, information on:

• Sample size for the test set: Not provided.
• Data provenance (e.g., country of origin, retrospective or prospective): Not provided.

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

Since the document does not describe a clinical performance study with a "test set" and ground truth establishment in the traditional sense for diagnostic accuracy, this information is not provided in the submission. The acceptance relates to the software's ability to facilitate measurements, where the "ground truth" for those measurements would be the established medical protocols and the physician's expertise. The submission emphasizes that analyses are to be performed by "a licensed medical practitioner who is knowledgeable of specific spinal anatomy and specific protocols for image analysis" (page 3).

4. Adjudication Method for the Test Set

As no specific "test set" for clinical performance evaluation is described, an adjudication method is not applicable and not mentioned in the document.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned in the provided document. The submission focuses on substantial equivalence based on functional and technical characteristics, and nonclinical software validation. There is no mention of evaluating human reader improvement with or without AI assistance.

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

No, a standalone performance study in the sense of the algorithm providing an automated diagnostic output without human interaction is not described. The device is intended to be an "aid in calculation" for a physician, implying a human-in-the-loop. The "automated part of the reporting process" refers to tasks like placing the 4th point based on others, mathematical calculations, and quality control notifications, not a fully automated diagnostic output.

7. The Type of Ground Truth Used

For the nonclinical software verification and validation, the "ground truth" would be the functional specifications, established DICOM standards, and the medical protocols for AOMSI calculation (e.g., AMA 6th Edition criteria for spinal evaluations). The software's correct execution of these specifications and protocols is what was validated.

8. The Sample Size for the Training Set

There is no mention of a training set in the document. This is because the device, as described, is a PACS system with automated calculation assistance, not a machine learning or AI model trained on a dataset to learn patterns or make predictions. Its rules for automation (e.g., 4th point placement, mathematical calculations) are explicitly programmed based on anatomical and mathematical principles, not learned from a training set.

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

As there is no mention of a training set, the method for establishing its ground truth is not applicable and not provided.

Ask a specific question about this device