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The 2.5-D and 3-D AUTIMO Systems are intended for the fabrication and positioning in radiotherapy of irregular shielding blocks, dose modifiers, and compensators.

The AUTIMO 2.5-D and 3-D models are computer-controlled cutting tool systems for producing customized shielding blocks for use in radiotherapy. The computer accepts files from existing treatmentplanning system operating on a LAN. The GE-Target, Siemens Helax, and Theratronics Theraplan systems are examples of such treatmentplanning software. The computer interprets the files from the planning system and communicates with the AUTIMO device through a serial port.

The provided document describes the 510(k) summary for the HEK AUTIMO 2.5-D and 3-D systems, which are computer-controlled cutting tool systems for producing customized shielding blocks in radiotherapy.

The document does not contain information related to software performance acceptance criteria or a study proving adherence to such criteria. Instead, it focuses on the device's substantial equivalence to a predicate device based on electrical safety and electromagnetic compatibility testing.

Therefore, I cannot provide the requested information in the format specified. The document explicitly states:

• "HEK Medizintechnik carried out testing to address the following issues: (1) electrical safety (2) eletromagnetic compatibility."
• "The results from these tests supported the safety and effectiveness of the 2.5-D and 3-D AUTIMO Systems and demonstrates that it is substantially equivalent to the predicate device."
• "The AUTIMO is simply a modification with updated hardware and cosmetic changes from the predicate device."

This indicates that the "study" mentioned in the document primarily pertains to hardware safety and EMC, rather than a clinical or performance study of an AI algorithm or software functionality that would necessitate the details requested (e.g., sample size for test/training sets, expert ground truth, MRMC study, standalone performance).

The "predicate device" mentioned is the MCP-70-SE (K844180), and the new AUTIMO system is deemed "substantially equivalent" based on its technological characteristics being the same as the predicate, with only updated hardware and cosmetic changes.

In summary, none of the specific information requested about acceptance criteria for device performance, study details (sample sizes, provenance, ground truth, expert review, MRMC, standalone), or training set data is present in this 510(k) summary because the device is a physical cutting tool system, not an AI/software-driven diagnostic or treatment planning system requiring such evaluations.

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The 2-D AUTIMO system is intended for the fabrication and positioning in radiotherapy of indivdualized (irregular) shielding blocks.

The AUTIMO 2-D model is a computer-controlled cutting tool to produce customized shielding blocks for use in radiotherapy. The computer accepts files from existing treatment-planning system operating on a LAN. The GE-Target, Siemens Helax, and Theratronics Theraplan systems are examples of such treatment-planning software. The computer interprets the files from the planning system and communicates with the AUTIMO device through a serial port. The 2-D model allows automatic hot-wire-cutting of styrofoam molds such as lung blocks and irregular sections.

The provided text describes the HEK AUTIMO 2-D system, a radiation therapy beam-shaping device. However, it does not include detailed acceptance criteria or a comprehensive study demonstrating that the device meets specific performance criteria in the way typically found in modern regulatory submissions for AI/ML devices.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device, which was the regulatory pathway at the time. This means the testing performed primarily confirmed the safety and basic functionality of the new model in comparison to the existing one, rather than establishing quantifiable performance metrics against pre-defined acceptance criteria for a novel technology.

Here's an analysis based on the provided text, addressing your points where possible, and highlighting what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, a formal table of explicit acceptance criteria and corresponding reported device performance metrics (e.g., accuracy, precision, sensitivity, specificity) is not available. The document states:

• Acceptance Criteria (Implicit): The primary acceptance criterion was demonstrating "substantial equivalence" to the predicate device (MCP-70-SE). This implicitly means the device should perform at least as safely and effectively as the predicate.
• Reported Device Performance: The document only states that "The results from these tests supported the safety and effectiveness of the 2.0-D AUTIMO System and demonstrates that it is substantially equivalent to the predicate device." It does not provide specific performance numbers for cutting accuracy, speed, or other operational parameters.

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

This information is not provided in the document. The text mentions "testing" was carried out, but it doesn't specify the sample size of any test sets (e.g., how many styrofoam molds were cut, or what range of block shapes were tested). It also does not mention data provenance (country of origin, retrospective/prospective).

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

This information is not applicable and not provided. The device is a cutting tool for radiation therapy, not an AI/ML diagnostic or prognostic tool that requires expert-established ground truth on medical images or patient data. The "ground truth" for this device would likely be the design specifications for the shielding blocks.

4. Adjudication Method for the Test Set

This information is not applicable and not provided for the same reasons as point 3.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

This information is not applicable and not provided. The HEK AUTIMO 2-D is a hardware device for fabricating shielding blocks, not an AI system designed to assist human readers (e.g., radiologists) in interpretation. Therefore, an MRMC study is outside the scope of its functionality.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

This information is not applicable and not provided. The AUTIMO 2-D is a machine that performs a physical action (cutting), driven by a computer-controlled system. While it operates "standalone" in the sense that a human isn't manually guiding the cutter, it's not an AI algorithm being evaluated for diagnostic performance. Its performance would be evaluated against the accuracy of the cuts it produces compared to the input designs.

7. Type of Ground Truth Used

The concept of "ground truth" as typically applied to AI/ML (e.g., pathology, outcomes data, expert consensus on images) is not applicable here. For a device like the AUTIMO 2-D, the "ground truth" against which it would be evaluated would be the engineering specifications and design parameters for the customized shielding blocks it is instructed to produce. The testing mentioned (electrical safety, electromagnetic compatibility) are about fundamental device safety and functionality rather than output accuracy against a clinical ground truth.

8. Sample Size for the Training Set

This information is not provided. As this is not an AI/ML device in the modern sense (it's a computer-controlled cutting tool), it's highly unlikely it had a "training set" in the context of machine learning. Its operation is likely based on pre-programmed algorithms and mechanical precision.

9. How the Ground Truth for the Training Set Was Established

This information is not applicable and not provided for the same reasons as point 8.

Summary of the Study Mentioned in the Document:

The document describes "testing" carried out by HEK Medizintechnik to address:

1. Electrical safety
2. Electromagnetic compatibility

The conclusion was that "The results from these tests supported the safety and effectiveness of the 2.0-D AUTIMO System and demonstrates that it is substantially equivalent to the predicate device."

In essence, this submission focuses on demonstrating substantial equivalence primarily through safety tests and asserting technological similarity to a predicate device, as was common for 510(k) submissions in 1997 for such a device. It does not provide the detailed performance studies, acceptance criteria, or ground truth methodologies that would be expected for a modern AI/ML medical device submission.

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