(37 days)
The 3M Trimatic™ Modular Advanced Processing System is an automatic, modular system for unloading exposed x-ray film from x-ray film cassettes, processing the film, and reloading the empty x-ray film cassette with fresh x-ray film of the same size and type. This system is used for handling and processing of x-ray films from all general radiographic, diagnostic procedures which employ conventional x-ray film / screen technology. The integrated automatic x-ray film processor can also be used as a stand-alone processor.
The 3M Trimatic™ Modular Advanced Processing System is an automatic, modular system for unloading exposed x-ray film from x-ray film cassettes, processing the film, and reloading the empty x-ray film cassette with fresh x-ray film of the same size and type. The 3M Trimatic™ Modular APS will accommodate from four to seven different film types / sizes in film modules. The integrated automatic x-ray film processor in the subject device uses mechanical rollers and guides, chemical replenishment and chemical agitation methods. The software used to control the operation of the subject device has added capabilities over the software used in the predicate device. Encoded in the bar codes on the x-ray film cassettes are the x-ray film size, type and the correct processing conditions for that particular film. This information drives the operation of the device. Remote diagnostics via phone line is also an added feature in the subject device. The control panel controls both the load / unload process and the automatic x-ray film processor.
The provided text describes a medical device, the 3M Trimatic™ Advanced Processing System, which is an automatic x-ray film loading and processing system. However, the document is a 510(k) Summary from 1996, which pre-dates the current expectations for detailed performance data for AI/ML-enabled devices.
Based on the information provided, it is not possible to fully answer all components of your request as a modern AI/ML device study would. The document focuses on technological characteristics and safety/performance standards typical for non-AI hardware at the time.
Here's an attempt to answer based only on the provided text, highlighting what is missing for a complete response relating to AI/ML acceptance criteria and studies:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Not explicit for AI/ML performance. The document focuses on electromechanical function and safety standards. | This document does not provide specific quantitative performance metrics like sensitivity, specificity, or accuracy that would be expected for an AI/ML device. The "Performance Data" section references general standards and internal validation. |
| Voluntary Standards Adherence: | UL 122, IEC 950, IEC 801-2, 3, 4, 5, EN 22011 |
| Internal Qualification/Validation (Software): | Successfully concluded field test and internal qualification/validation tests. |
| Software Final Release Approval: | Approved by product team for production after successful tests. |
| (Implied) Equivalence to Predicate Device: | "The subject device has moved and altered some of the internal mechanisms to reduce the size of the film magazine modules." "The integrated automatic x-ray film processor in the subject device uses mechanical rollers and guides, chemical replenishment and chemical agitation methods which are similar to the predicate processor." "The software used to control the operation of the subject device has added capabilities over the software used in the predicate device." (This suggests performance is at least equivalent, if not improved, for the described functions). |
2. Sample size used for the test set and the data provenance
- Sample Size: Not specified. The document mentions "field test" and "internal qualification / validation tests" but does not quantify the number of films, operational cycles, or any other relevant units for the test set.
- Data Provenance: Not specified, but implied to be from internal testing and field tests of the device itself. No mention of country of origin for data. Retrospective or prospective is not specified; "field test" implies prospective, but "internal qualification/validation" could be either.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This information is not provided. The device processes x-ray film and unloads/reloads cassettes; it does not interpret medical images or perform a diagnostic function that would require expert ground truth for its core operation as described.
4. Adjudication method for the test set
- Not applicable as no expert-derived ground truth is described for the device's function. The "adjudication" seems to be internal engineering and product team approval based on functional testing.
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, an MRMC study was not done. This device is an automatic film processor, not an AI-powered diagnostic tool that assists human readers.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- This question is phrased for an algorithm's performance. The device itself operates "standalone" in its film processing function without human intervention during the processing cycle. However, this is not an "algorithm-only" performance in the context of AI/ML. The "software" mentioned controls mechanical processes rather than performing diagnostic or interpretive tasks.
7. The type of ground truth used
- For the described functions (unloading, processing, reloading film, remote diagnostics), the "ground truth" would be functional correctness and adherence to specifications. For example, did the film successfully load? Was it processed correctly? Was the correct film type/size detected from the bar code? Was the remote diagnostic function operational? This is not pathology, outcomes data, or expert consensus in the typical AI/ML sense.
8. The sample size for the training set
- Not applicable. This device predates the common use of "training sets" for AI/ML algorithms. The software described is control software for electromechanical functions, not a learned algorithm.
9. How the ground truth for the training set was established
- Not applicable for the same reasons as #8. Ground truth for control software would be established through engineering specifications and functional testing of each component and the integrated system.
§ 892.1900 Automatic radiographic film processor.
(a)
Identification. An automatic radiographic film processor is a device intended to be used to develop, fix, wash, and dry automatically and continuously film exposed for medical purposes.(b)
Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.