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The NL5000 [OmniTom] system is intended to be used for x-ray computed tomography applications for anatomy that can be imaged in the 40 cm aperture, primarily head and neck.

The CT system is intended to be used for both pediatric and adult imaging and as such has preset dose settings based upon weight and age. The CT images can be obtained either with or without contrast.

The NL5000 CT System is a high resolution, 16 row, 40 cm bore, and 30 cm field of view x-ray computed tomography system. The lightweight translating gantry consists of a rotating disk with a solid state x-ray generator, Gd2O2S detector array, collimator, control computer, communications link, power slip-ring, data acquisition system, reconstruction computer, power system, brushless DC servo drive system (disk rotation) and an internal drive system (translation). The power system consists of batteries which provide system power while unplugged from the charging outlet. The system has the necessary safety features such as the emergency stop switch. x-ray indicators, interlocks, patient alignment laser and 110% x-ray timer. The gantry has omni-directional wheels that allow for robust diagonal, lateral, and rotational 360 degree movement and electrical drive system so the system can be moved easily to different locations.

Here's a breakdown of the acceptance criteria and study information for the OmniTom device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes the OmniTom as a Computed Tomography (CT) X-ray system. The acceptance criteria are implicitly defined by a comparison against a predicate device (CereTom K051765) and adherence to recognized industry standards. The performance is assessed through various tests, including image quality, safety, and functional parameters.

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

• Test Set Sample Size:
  • Phantom Studies: Not explicitly stated, but "phantom image quality tests" were utilized. Phantoms are typically manufactured objects, not human subjects, so provenance in terms of country of origin is not applicable.
  • Clinical Images: "Clinical images for adult brain exams" were used. The document does not specify the number of adult brain exams used to form the test set.
  • Pediatric Scans: "Phantom image evaluations were performed for pediatric scans," but it's unclear if actual clinical pediatric images were also used for evaluation or just phantoms.
• Data Provenance: The document does not specify the country of origin for any clinical images used. The studies appear to be retrospective clinical data review, as they refer to "clinical images for adult brain exams" being reviewed, which implies pre-existing scans.

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

• Number of Experts: "a board-certified radiologist" (singular) reviewed the clinical images.
• Qualifications of Experts: The expert was "board-certified radiologist." The document does not specify their years of experience.

4. Adjudication Method for the Test Set

• No explicit adjudication method is mentioned. Given that a single "board-certified radiologist" reviewed the images, a formal multi-reader adjudication method (like 2+1 or 3+1) was not employed for the clinical image review described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

• No, an MRMC comparative effectiveness study was not performed. This submission is for a CT system itself, not an AI-assisted interpretation or diagnostic software. The focus is on the device's image quality and safety compared to a predicate device, as well as its compliance with relevant standards. Therefore, the effect size of human readers improving with or without AI assistance is not applicable or discussed.

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

• This is not applicable. The OmniTom is a CT scanner, not an independent algorithm or AI diagnostic tool. Its "performance" is its ability to produce high-quality diagnostic images and operate safely, not to autonomously interpret scans.

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

• For image quality, the ground truth appears to be established through:
  • Phantom studies: These use known physical properties of the phantom to assess image metrics like spatial resolution, noise, and contrast.
  • Expert review: A "board-certified radiologist" determined if clinical images were of "diagnostic quality." This could be considered a form of expert consensus, albeit from a single expert, against an internal standard of diagnostically acceptable images.
• For safety and functional performance, the ground truth is established by:
  • Conformance to recognized consensus standards (e.g., IEC, NEMA).
  • Internal verification and validation testing against defined specifications (e.g., electrical, mechanical, radiation hazards).

8. The sample size for the training set

• The document describes a 510(k) submission for a CT hardware system, not a software algorithm that requires a "training set" in the machine learning sense. Therefore, there is no training set sample size mentioned or applicable.

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

• As there is no training set for an AI algorithm in this submission, this question is not applicable.

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