Search Results

The PicomEnterprise software is intended for acceptance, transfer, display, storage and digital processing of medical images.

Its hardware components may include digitizers, workstations, communications devices, computers, video monitors, magnetic, optical disk, or other digital data storage devices and hardcopy devices.

The software components provide functions for performing operations related to image manipulation, enhancement, compression or quantification.

To support the diagnostic interpretation of Mammography studies, PicomEnterprise will display the full fidelity DICOM image in a non-compressed format. Images will be rendered with patient and clinical information clearly displayed as part of the DICOM Overlay as required by MQSA, on monitors cleared by FDA for use in Digital Mammography. Lossy compressed mammography images and digitized film screen images should not be used for the purpose of primary diagnosis. Mammographic images may only be interpreted using an FDA approved monitor that offers at least five megapixel resolution and meets other technical specifications reviewed and accepted by FDA.

The PicomEnterprise software is a multi-modality comprehensive two-, three- and four-dimensional image presentation software system intended for acceptance, transfer, display, storage and digital processing of medical images. The PicomEnterprise software combines reconstruction and display algorithms for medical image analysis in the familiar Microsoft Windows environment. PicomEnterprise offers full compliance with DICOM 3.0 standard that permit transfer of data from medical devices to storage server and then to other DICOM compliant devices.

The provided information does not contain details about acceptance criteria or specific studies proving the device meets them. The document is a 510(k) Premarket Notification for the Scimage PicomEnterprise software, primarily focusing on its intended use, description, and comparison to predicate devices for FDA clearance.

Here's an analysis of what is provided in relation to your request:

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

   • Not provided. The document describes the device's functions (acceptance, transfer, display, storage, digital processing of images, image manipulation, enhancement, compression, quantification) and its compliance with standards like DICOM 3.0 and JPEG. It also specifies its use for mammography studies, including displaying full-fidelity DICOM images on FDA-cleared 5-megapixel monitors. However, it does not state any specific performance metrics (e.g., accuracy, speed, uptime) or associated acceptance criteria that were met through testing.

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

   • Not provided. There is no mention of any specific test set, its size, or the provenance of any data used for testing.

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)

   • Not provided. Since no specific test set or ground truth establishment is described, details about experts are absent.

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

   • Not provided. No adjudication method is mentioned.

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

   • Not provided. This document predates widespread AI/ML in medical devices and focuses on PACS software. No MRMC study or AI assistance is mentioned.

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

   • Not provided. The device is a "Medical image workstation system, PACS," which implies a human operator. There's no mention of a standalone algorithm performance study.

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

   • Not provided. As no performance study with a test set is discussed, the type of ground truth is not specified.

8. The sample size for the training set

   • Not provided. The document describes a software system, not a machine learning model that would require a distinct "training set."

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

   • Not provided. Similar to point 8, this is not applicable given the nature of the device described.

In summary, this 510(k) filing is for a Picture Archiving and Communications System (PACS) software. Such devices, especially around the 2007 timeframe of this filing, were typically cleared based on substantial equivalence to predicate devices, compliance with established standards (like DICOM), and functional verification, rather than clinical performance studies using acceptance criteria as might be expected for novel diagnostic algorithms. The document confirms that the device is "substantially equivalent" to predicate devices and complies with voluntary standards like ACR/NEMA DICOM and JPEG.

Ask a specific question about this device

Ask a specific question about this device

The Netra™ Workstation System and NetraMD™ Software is intended for viewing and manipulation of high quality MRI, CT, Ultrasound and X-ray electronic images as an aid in diagnosis for the trained medical practitioner.

The Netra™ Workstation System and NetraMD™ Software is a Medical Image Processing System and digital image communications system for use by the trained medical practitioner. The Netral Image Processing System receives electronic information from medical imaging devices and manipulates that data for purposes of visualization, communication, archiving, characterization, comparison to other images and image enhancement. It is similar in design to other such digital image communications system devices. It has microprocessor PC computer controlled solid state digital data and video receiving and transmission electronics and accessories.

The provided text describes a 510(k) submission for the Netra™ Workstation System and NetraMD™ Software, claiming substantial equivalence to a predicate device. However, it does not contain a detailed study with acceptance criteria and reported device performance in the manner typically required for a modern AI/ML device.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document focuses on demonstrating substantial equivalence to an existing predicate device rather than undergoing a new, comprehensive clinical performance study with specific quantitative acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity, AUC). The "acceptance criteria" here are primarily about regulatory compliance, functional specifications, and maintaining the same safety and effectiveness as the predicate.

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

• Sample Size: Not specified. The document states "Performance tests were conducted by testing the system to the above standards and to the Netra™ design specifications." This suggests functional and technical testing, but there's no mention of a "test set" of medical images for diagnostic performance evaluation.
• Data Provenance: Not specified.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not applicable/not specified. The testing described is against technical standards and design specifications, not against established ground truth for diagnostic accuracy based on expert consensus.
• Qualifications of Experts: Not applicable/not specified.

4. Adjudication Method for the Test Set

• Adjudication method: Not applicable/not specified. No diagnostic "test set" requiring adjudication is mentioned.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The submission relies on demonstrating substantial equivalence to a predicate device based on technological characteristics and functional performance, not on direct comparison of human reader performance with or without the device.
• Effect size improvement: Not applicable, as no MRMC study was performed.

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

• Standalone Performance: No, a standalone performance study in the modern sense of an AI algorithm making diagnoses was not conducted or reported. The device is a "Medical Image Processing System" intended "for use by the trained medical practitioner" as an "aid in diagnosis." Its function is to display, manipulate, and communicate images, not to make diagnostic interpretations independently. The "performance tests" focused on meeting technical standards and design specifications for these functions.

7. The type of ground truth used

• Type of Ground Truth: Not applicable/not specified for diagnostic performance. The "ground truth" for the tests performed was adherence to technical standards (DICOM, DISC) and the device's own design specifications. There is no mention of pathology, outcomes data, or expert consensus used as ground truth for diagnostic accuracy of image interpretations.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not specified. This device is an image processing system, not a machine learning algorithm that undergoes a "training" phase with a dataset to learn patterns for diagnosis.

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

• Ground Truth for Training Set: Not applicable. As this is not an AI/ML device that requires a training set, the concept of establishing ground truth for a training set does not apply.

In summary: This 510(k) submission from 1996 for the Netra™ Workstation System and NetraMD™ Software focuses on demonstrating functional equivalence to a predicate device and compliance with established technical standards (like DICOM). It does not present the type of detailed clinical performance study, acceptance criteria, or ground truth establishment that would be expected for a diagnostic AI/ML device today. The "study" here refers to technical performance tests against standards and specifications, not a diagnostic accuracy trial.

Ask a specific question about this device