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510(k) Data Aggregation
(124 days)
nCommand Lite System
The nCommand Lite system allows remote access for viewing/review of images as well as the ability to remotely provide real-time quidance to the licensed technologist operating a compatible MRI, CT, and PET-CT scanner. This access must be granted by the local licensed technologist operating the system. The remote access is only available for systems supporting remote connectivity capability. Images reviewed remotely are not for diagnostic use.
The nCommand Lite system is a SaMD (Software as Medical Device) that is customer-facing and provides remote access through a web application for viewing images as well as the ability to remotely provide real-time guidance to the near-patient licensed technologist (local users) operating the medical imaging devices in the context of training, procedure assessment, for MN, CT, and PET-CT scanner.
Remote users are licensed technologists and are able to access an imaging medical device from a computer that meets the minimum required specifications anywhere, using a secure software connection that streams video and mouse access. This allows remote users to assist local users with both remote and resoonsibilities being subject to local requlations where the imaging occurs. This access must by the local user using the system, and it can also be revoled at any time by the local user, thus allowing him/her to maintain complete control of the session at all times. Also, each remote session is controlled via a secure credential that is managed by the local user. Connections to a medical device are limited to one remote session at a time to maintain strict control over who is remotely interacting with the system. Images reviewed remotely are not for diagnostic use. n Command Lite is an IT hardware and software-based installation solution that supports multisesson, allowing remote technologists to view multiple medical imaging devices as needed. However, access is restricted to one medical imaging device at any given time, as only one keyboard and one mouse are provided.
Scanners that are compatible with nCommand Lite system comply with a common protocol, allowing for scanning parameter management and remote scanning.
The provided text describes the nCommand Lite System, a Software as a Medical Device (SaMD) that facilitates remote access for viewing medical images and providing real-time guidance to technologists operating MRI, CT, and PET-CT scanners.
However, the submission primarily focuses on establishing substantial equivalence to predicate devices based on technological characteristics and non-clinical testing, rather than presenting performance metrics against specific acceptance criteria for diagnostic accuracy or clinical outcomes. The device explicitly states that "Images reviewed remotely are not for diagnostic use." This significantly changes the type of acceptance criteria and supporting studies expected.
Therefore, the response will focus on the acceptance criteria implied by the non-clinical tests and the general safety and effectiveness claims.
Acceptance Criteria and Study Details for nCommand Lite System
Given that the nCommand Lite system is explicitly "not for diagnostic use" and assists in providing "real-time guidance" during scanner operation, the acceptance criteria are focused on safety, functionality, usability, and cybersecurity rather than diagnostic performance metrics (e.g., sensitivity, specificity, AUC). The provided submission details the non-clinical tests performed to demonstrate substantial equivalence to predicate devices in these areas.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria (Inferred from submission) | Reported Device Performance (Inferred from submission) |
|---|---|---|
| Software Functionality & Reliability | The software operates as intended, reliably facilitating remote access, image viewing, and real-time guidance. | Non-clinical software testing was performed following FDA guidance (e.g., "Content of Premarket Submissions for Device Software Functions," "General Principles of Software Validation Guidance") and recognized standards (e.g., IEC 62304, ANSI/AAMI SW91). The conclusion is that the device is "as safe, as effective, and performs equivalently" to predicates. |
| Cybersecurity | The system is secure against unauthorized access, data breaches, and other cyber threats. | Cybersecurity testing was performed following FDA guidance ("Cybersecurity in Medical Devices") and recognized standards (e.g., NIST Cybersecurity Framework, IEC 81001-5-1, ANS/ISA 62443-4-1, AAM TIR57). The conclusion states the device is "as safe, as effective, and performs equivalently" to predicates. |
| Usability & Human Factors | The system is safe and effective for its intended users (remote and local technologists) in its intended use environment, without causing undue use errors. | Usability engineering/human factors testing was performed following FDA guidance ("Applying Human Factors and Usability Engineering to Medical Devices") and recognized standards (e.g., IEC 62366-1). The conclusion states the device is "as safe, as effective, and performs equivalently" to predicates. |
| Electrical Safety & Electromagnetic Compatibility (EMC) | The device meets electrical safety and EMC requirements to ensure safe operation without interfering with other equipment. | Adherence to recognized standards: IEC 60601-1 (medical electrical equipment basic safety and essential performance) and IEC 60601-1-2 (collateral standard for electromagnetic disturbances). The conclusion states the device is "as safe, as effective, and performs equivalently" to predicates. |
| Risk Management | All identified risks associated with the device's use are appropriately managed and mitigated. | Application of risk management processes following ISO 14971:2019. The conclusion states the device is "as safe, as effective, and performs equivalently" to predicates. |
2. Sample Size for Test Set and Data Provenance
The submission primarily discusses non-clinical testing (software, cybersecurity, usability, electrical safety) rather than clinical studies with patient data. Therefore, there isn't a "test set" in the traditional sense of medical images or patient cases to determine diagnostic accuracy.
- Sample Size: Not applicable in the context of patient data. The "test set" would refer to the scenarios, use cases, and testing environments used for the non-clinical evaluations. Specific numbers for these scenarios are not provided in the summary.
- Data Provenance: Not applicable as no patient data or images were used to demonstrate performance against diagnostic acceptance criteria. The provenance relates to the simulated environments, test data, and user interactions during the non-clinical tests.
3. Number of Experts and Qualifications for Ground Truth
Given the non-diagnostic, assistance-based nature of the device and the focus on non-clinical testing, the concept of "ground truth" established by medical experts (e.g., radiologists) for a test set is not directly relevant in the way it would be for a diagnostic AI device.
- Number of Experts: Not specified or applicable for a ground truth derived from clinical diagnostic labels.
- Qualifications of Experts: For usability testing and software validation, "experts" would typically refer to software engineers, human factors engineers, cybersecurity specialists, and potentially experienced medical technologists who participated in user acceptance testing or usability evaluations. Their specific qualifications are not detailed in the provided summary.
4. Adjudication Method for Test Set
Not applicable, as there is no mention of a test set requiring adjudication by medical experts to establish ground truth for diagnostic purposes. Adjudication methods like 2+1 or 3+1 are used to resolve disagreements in expert opinions for diagnostic labels.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- Was an MRMC study done? No, an MRMC comparative effectiveness study was not performed.
- Effect size of human readers with AI vs. without AI assistance: Not applicable, as this device is not designed to provide AI assistance for diagnostic interpretation of medical images. Its purpose is to provide remote guidance and access to operating medical imaging equipment.
6. Standalone Performance (Algorithm only without Human-in-the-loop)
- Was a standalone performance study done? Yes, in a sense, the non-clinical testing (software, cybersecurity, compliance with standards) evaluates the device's functionality and safety characteristics in a standalone manner, separate from human operation during actual clinical scenarios. For example, software unit tests, integration tests, and system tests would evaluate the algorithm's performance on its own, ensuring it can connect, stream video, and process input/output as designed. However, this is not a clinical standalone performance in terms of diagnostic accuracy.
7. Type of Ground Truth Used
The concept of "ground truth" in this submission relates to:
- Functional Specifications: The ground truth for software testing is that the software performs its specified functions correctly and reliably.
- Cybersecurity Standards: The ground truth for cybersecurity is adherence to established security protocols and standards.
- Usability Requirements: The ground truth for usability is that the device can be used effectively and safely by its intended users.
- Regulatory Standards: The ground truth is compliance with the recognized electrical safety and EMC standards.
There is no mention of "expert consensus, pathology, or outcomes data" being used as ground truth for a clinical assessment of the device, as its intended use is not diagnostic.
8. Sample Size for the Training Set
Not applicable. The nCommand Lite system is a software device that provides remote access and guidance. It is not described as an AI/ML model that requires a "training set" of medical images or other data to learn and make predictions or classifications. Its functionality is based on established software engineering principles and protocols for remote connectivity.
9. How the Ground Truth for the Training Set was Established
Not applicable, as there is no training set for an AI/ML model.
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