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NeoRad PACS is a software device used for viewing and manipulating medical images and other healthcare related electronic records. Digital imaging units (including DR, CR, CT, MR, US, RF and 2D/3D mammography), other computed and direct radiographic devices, secondary capture devices, document and film scanners, imaging gateways, other imaging devices and healthcare related electronic records can be displayed, processed, managed, stored and communicated across computer networks using this software.

NeoRad PACS, can be integrated with a facility's existing Electronic Medical Records System (EMR), Hospital Information System (HIS), Practice Management System, Radiology Information System (RIS) and Dictation System. NeoRad PACS, may also be integrated with the facility's Teleradiology Provider systems. These integrations provide seamless operation and access for Providers and other caregivers to the entire spectrum of patient's radiology records from the Provider's orders, to images of the exam, to completion data all the way to the interpretations (results/reports).

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretation on the NeoRad PACS. Mammographic images may only be interpreted using an FDA cleared display that offers at least 5 MP resolution and meets other technical specifications reviewed and accepted by the FDA.

NeoRad PACS uses web-based technology and a secure operating system. This means that virtually any computer at any location can use an Internet browser to access NeoRad PACS systems. Whether on-site or from another location, physicians can manage, edit, view and move diagnostic exams quickly and efficiently. As part of NeoRad PACS support, Everrtech Software performs the system maintenance within its scope. NeoRad PACS, has been designed to be expandable, with storage beginning at 2 TB and growing to 25 TB onboard or 256 TB expansion.

NeoRad PACS is an implementation of an IHE (Integrating the Healthcare Enterprise) compliant Image Archive and Report Repository. This includes the capabilities:

· To store and retrieve various kinds of DICOM Objects such as:

1. Images from multiple modalities,

2. Grayscale Presentation States, which specifies the presentations of images as gray scaling, zoom, text and graphical annotations,

3. Key Objects, which specifies a particular selection of images for a specified reason and with an attached note,

· To process HL7 notification messages from the Order Filler, about Procedure Updates and Patient Information Updates,

NeoRad PACS is a server application. Its Web-based User Intended for system configuration and monitoring by system administrators.

This document is a 510(k) clearance letter from the FDA for a Medical Image Management and Processing System called "Neorad Pacs." It primarily focuses on demonstrating substantial equivalence to a predicate device and outlines the device's intended use and regulatory compliance. It does not contain information about specific acceptance criteria, detailed study designs, or performance metrics in a way that allows for a direct answer to all parts of your request regarding the device's performance against detailed acceptance criteria.

However, I can extract the available information and highlight what is missing based on your request.

Here's a breakdown of the information available in the provided text:

Acceptance Criteria and Device Performance:

The document states: "The NeoRad PACS has been assessed and tested at the factory and has passed all predetermined testing criteria." and "Validation testing indicated that as required by the risk analysis, designated individuals performed all verification activities and that the results demonstrated that the predetermined acceptance criteria were met."

However, the specific "predetermined testing criteria" (acceptance criteria) and the "results" (reported device performance) are not provided in detail within this document. The table below will reflect this.

Additional Information (Based on the document):

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

   • Sample Size: Not specified in the provided text.
   • Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

   • Not specified. The document states "designated individuals performed all verification activities," but provides no details on their number or qualifications.

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

   • Not specified.

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 MRMC comparative effectiveness study is mentioned. The device is a PACS system for image management and processing, not an AI diagnostic tool for reader improvement.

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

   • The document implies standalone testing of the software functions and system capabilities, as it discusses "validation testing" and "predetermined acceptance criteria." However, it does not specify if "algorithm only" performance was assessed in a medical diagnostic context (e.g., for an AI algorithm). Given it's a PACS system, the "standalone" performance would likely refer to its functional robustness and compliance with DICOM/HL7 standards for image and data handling.

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

   • Not specified. The "ground truth" for a PACS system would primarily be its adherence to technical specifications, interoperability standards (DICOM, HL7), data integrity, and functional correctness rather than clinical diagnostic ground truth.

7. The sample size for the training set:

   • Not applicable as this is a PACS system, not a machine learning model that requires a training set in the conventional sense.

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

   • Not applicable.

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