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The BodyTom 64 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.

BodyTom 64 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.

BodyTom 64 computed tomography (CT) system provides the same functionality as the previous version of the device BodyTom 64 (K213649). Both CT systems are identical in terms of the high resolution, multi row, 85 cm bore, and 60 cm field of view. The lightweight translating gantry consists of a rotating disk with a solid-state x-ray generator, Gd202S 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 interventional radiology package should not be used in an operating room during surgery.

The provided document, a 510(k) Premarket Notification from the FDA, states that the "BodyTom 64" device is "substantially equivalent" to a predicate device (BodyTom 64, K213649) and does not provide an extensive acceptance criteria table or detailed study results for a new clinical performance study.

Typically, when a device is found to be "substantially equivalent" based on technological characteristics and performance testing to an already cleared predicate, the FDA does not require new, large-scale clinical studies with human subjects, especially if the changes are limited to software updates and new features that do not raise new questions of safety or effectiveness. The document instead focuses on demonstrating adherence to recognized standards, quality system regulations, and bench testing to show that the modified device performs comparably and safely.

Therefore, many of the requested details about acceptance criteria, detailed performance metrics, sample sizes, expert ground truth establishment, MRMC studies, or multi-reader studies are not explicitly stated or applicable in this type of 510(k) submission where substantial equivalence is being demonstrated based on non-clinical performance and technological characteristics.

However, based on the information provided, here's what can be extracted and inferred:

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

The document primarily states that the device meets existing standards and performs comparably to its predicate. Specific quantitative acceptance criteria for clinical performance are not listed with corresponding results because the submission focuses on substantial equivalence through technical verification.

• Noise
• Slice thickness
• Low contrast resolution
• High contrast resolution
• Radiation metrics
• Modulation transfer function (MTF) | "Imaging metrics successfully demonstrated that the proposed device has comparable image quality with its previous version, predicate device (K213649) and meets all the image quality criteria that are used for testing." |
  | Electrical Safety / Electromagnetic Compatibility (EMC/EMI) | "proved to be in compliance with IEC 60601-1-2. and IEC 60601-1-2. and IEC 60601-2-44." |
  | Software Functionality and Safety | "Software is critical to the operation of the BodyTom 64 CT system and a malfunction or design flaw in the software could result in delay in delivery of appropriate medical care. As such, the risk management analysis identified potential hazards which were controlled and mitigated during development of BodyTom 64. The verification/validation testing ensured substantial equivalence of BodyTom 64."
  "The proposed BodyTom 64 device demonstrated that the new features did not exhibit any negative effects on the requirements in place, as well as they did not exhibit any concerns."
  "The proposed BodyTom 64 device was shown to meet all requirements and to not have any impact on imaging." |
  | Mechanical Safety | "To minimize electrical, mechanical and radiation hazards, NeuroLogica adheres to recognized and established industry practices." |
  | Compliance with Federal Diagnostic Equipment Performance Standard and applicable regulations (21 CFR §1020.30 and §1020.33) | "All components...are certified to meet those requirements." |
  | Compliance with Quality System Regulations and ISO 13485:2016 | "BodyTom 64 CT system is designed and manufactured to comply with the FDA Quality System Regulations and ISO 13485:2016 requirements." |

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

• Sample Size: Not applicable/not specified for a clinical test set since this submission relies on bench testing (phantom image quality tests), software verification/validation, and regulatory compliance, rather than a clinical study with human patients.
• Data Provenance: Not applicable, as no patient data was used for this substantial equivalence demonstration. The data pertains to engineering and phantom 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 applicable. Ground truth, in the context of phantom testing for image quality, is established by known physical properties of the phantoms and measurements of the system's output against defined engineering specifications, not by expert human interpretation.

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

• Not applicable, as no human reader studies requiring adjudication were conducted.

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

• No. This submission does not describe an MRMC study. The device is a CT system with software functionality updates, not an AI-assisted diagnostic tool that aids human readers in interpretation.

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

• Not applicable in the context of an algorithm's diagnostic performance. The "performance" being evaluated is the technical and physical output of the CT system and its software, not a diagnostic algorithm.

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

• The "ground truth" for the performance described generally refers to:
  • Engineering Specifications: For image quality metrics (noise, resolution, etc.), the performance is measured against established quantitative specifications derived from physical principles and industry standards using phantoms.
  • Regulatory Requirements & Harmonized Standards: For safety (electrical, mechanical, radiation) and quality, the ground truth is compliance with the detailed requirements outlined in standards like IEC 60601 series, ISO 14971, IEC 62304, and FDA regulations (21 CFR §1020.30, §1020.33).
  • Predicate Device Performance: Implicitly, the performance of the predicate device (K213649) serves as a benchmark for "comparable image quality."

8. The sample size for the training set

• Not applicable. This device is a CT scanner, not a machine learning algorithm that requires a "training set" for its core function of image acquisition and reconstruction. The software updates mentioned likely relate to system control, user interface, or image processing, which undergo traditional software verification and validation, not machine learning model training.

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

• Not applicable, as there was no training set in the context of machine learning model development.

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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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