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The NL4100 BodyTom 64 CT system is intended to be used for x-ray computed tomography applications for anatomy that can be imaged in the 85cm aperture.

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.

Body Tom 64 CT system can be used for low dose lung cancer screening. The screening must be performed in compliance with the approved and established protocols as defined by professional medical societies.

The BodyTom 64 is an improved version of the BodyTom Elite (K170238) computed tomography system, providing enhanced functionality. It still has the same high resolution, multi row, 85 cm bore, and 60 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 slipring, 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 retractable rotating caster wheels and electrical drive system so the system can be moved easily to different locations.

The BodyTom 64 x-ray detector has been updated to allow for 64 cross-sectional CT images (slices) of your body to be generated, instead of the 32 images produced by the predicate BodyTom Elite device (K170238).

Based on the provided document (K213649 510(k) Summary for the BodyTom 64 CT system), here's an analysis of the acceptance criteria and the study that proves the device meets them:

Important Note: The provided document is a 510(k) summary for a Computed Tomography X-Ray System (BodyTom 64), not an AI/ML medical device. Therefore, many of the typical acceptance criteria and study designs applicable to AI devices (e.g., MRMC studies, expert consensus for ground truth on disease detection, effect size of human improvement with AI) are not relevant for this type of device.

This 510(k) summary focuses on demonstrating substantial equivalence to a predicate CT device by proving its technological characteristics, safety, and effectiveness. The "acceptance criteria" here are primarily related to meeting performance standards, image quality metrics, and regulatory compliance, rather than clinical performance for a specific disease detection task with AI assistance.

Acceptance Criteria and Reported Device Performance

The "acceptance criteria" for the BodyTom 64, as described in this 510(k) summary, are primarily focused on demonstrating that the updated device performs equivalently to its predicate and meets established safety and performance standards. These are not acceptance criteria for a diagnostic AI algorithm in the typical sense.

Here's a table based on the provided text, outlining the performance aspects assessed:

Summary of Device Performance: The BodyTom 64 successfully demonstrated that its enhancements had no negative impact on clinical performance, it met all image quality criteria used for testing, and it complies with all relevant safety and performance standards and regulations.

Study Details (as applicable for a CT system, not an AI device)

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set: The primary testing for image quality was conducted using an ACR Phantom. This is a standard phantom designed for objective assessment of CT image quality.
   • Data Provenance: Not applicable in the context of phantom testing. This is laboratory/bench testing. No patient data (retrospective or prospective) from specific countries is mentioned for the performance testing, as it's a hardware/software update to a CT system, not a diagnostic algorithm based on patient images.

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

   • Not applicable. For a CT system, "ground truth" for technical performance is established by known phantom properties and physical measurements, not by human expert interpretation of clinical images. The study is about the device's technical specifications and how well it generates images, not about its ability to diagnose conditions.

3. Adjudication Method for the Test Set:

   • Not applicable. Adjudication methods (like 2+1) are used for resolving disagreements among human readers or annotators in clinical AI studies. This study is based on objective phantom measurements and engineering verification/validation.

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. An MRMC study is relevant for evaluating the clinical impact of an AI-assisted diagnostic tool on human performance. The BodyTom 64 is a CT scanner, not an AI tool in that sense. The study focused on demonstrating technical equivalence and compliance with performance standards.

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

   • A "standalone" performance evaluation in the context of an AI algorithm would typically mean assessing its diagnostic accuracy (e.g., sensitivity, specificity). For the BodyTom 64, the "standalone" performance is its ability to produce images that meet specified technical image quality metrics (e.g., noise, resolution) when scanning a phantom. This was indeed done (Bench/Image Testing).

6. The Type of Ground Truth Used:

   • Phantom Properties / Physical Measurements: For image quality, the ground truth is derived from the known physical properties and design of the ACR Phantom, combined with objective measurements (e.g., CT number, noise standard deviation, spatial resolution targets).
   • Compliance with Industry Standards and Regulations: For safety and operational aspects, the ground truth is defined by the requirements of the referenced IEC and NEMA standards, as well as FDA regulations (e.g., 21 CFR §1020.30, §1020.33).

7. The Sample Size for the Training Set:

   • Not applicable. This is not an AI/ML device in the sense of requiring a large training dataset of medical images for a learning algorithm. The software updates were to a control system and data acquisition, not a trainable diagnostic model.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable for the same reason as above. The "training" for such a device is its design and engineering to meet specifications, not an iterative learning process from data.

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