Search Results

The Boston KPro is indicated to provide a transparent optical pathway through an opacified cornea in an eye that is not a reasonable candidate for any form of corneal transplant, including penetrating keratoplasty.

The Boston KPro, Type I Lucia is an artificial corneal device that can be used in patients with severe corneal opacity. The device consists of two components: a front plate constructed of clear polymethyl methacrylate (PMMA) plastic, and a back plate constructed of titanium that locks the device in place around a corneal donor graft. The Boston KPro, Type I Lucia is supplied with an assembly tool/pin to assist in device assembly.

The provided document describes a 510(k) submission for the Boston Keratoprosthesis, Type I Lucia (Boston KPro), which is an artificial corneal device. This is primarily a submission seeking to modify the manufacturing method of the back plate and make related minor changes to the front plate and assembly tool. The document focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive study to establish new clinical performance acceptance criteria.

Therefore, the information regarding specific acceptance criteria for clinical efficacy studies, sample sizes, expert involvement, and ground truth for clinical performance is not directly provided in the context of a new clinical study with new acceptance criteria. Instead, the focus is on non-clinical testing to confirm that the modified device maintains the performance of the predicate device.

However, I can extract the relevant non-clinical acceptance criteria and the testing performed to demonstrate that the modified device meets these.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: The document does not specify a "test set" in the context of a clinical study for establishing performance metrics. The testing performed is non-clinical, verification and validation testing of the device's physical and material properties, and manufacturing process. Therefore, sample sizes would be per the requirements of the specific engineering and material tests (e.g., number of devices tested for assembly force, number of samples for chemical analysis, etc.), which are not detailed here.
• Data Provenance: Not applicable in the context of clinical patient data, as no clinical studies were performed. The non-clinical testing data would originate from the manufacturing and testing facilities of MEEI and its subcontractors (e.g., Tecomet, Inc.).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. No ground truth in the clinical diagnosis sense was established, as no clinical studies were conducted for this 510(k) submission. Non-clinical tests rely on established scientific methods and standards.

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

• Not applicable, as no human-based clinical performance assessment or diagnostic study was performed that would require adjudication.

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 MRMC study was done, as this is a physical medical device (corneal implant), not an AI-assisted diagnostic tool.

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

• Not applicable, as this is a physical medical device.

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

• The "ground truth" for the non-clinical testing is based on predefined engineering specifications, material standards (e.g., ASTM, ISO), and regulatory limits. For example, the ground truth for sterilization efficacy is conformance to ISO 11135, and for material biocompatibility, it's conformance to ISO 10993.

8. The sample size for the training set

• Not applicable. No training set in the machine learning sense was used. This is a physical device modification submission.

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

• Not applicable.

Ask a specific question about this device

The Lucia is intended to emit energy in the visible and infrared spectrum to provide topical heating for the purpose of elevating tissue temperature for the temporary relief of minor muscle and joint pain and stiffness, minor arthritis pain, or muscle spasm; the temporary increase in local blood circulation; and/or the temporary relaxation of muscle.

The Lucia has a hand-held treatment probe, is non-invasive, low level visible lamp that provides continuous heat therapy at fixed frequency, The System consists of a Drive Unit, Power Supply, controls and optional treatment probes that contain the visible radiating elements. The Lucia generator automatically calculates energy output in relation to set treatment parameters. The parameters (treatment time, output power delivered) can be adjusted by using key, treatment specifications and menus. The progress of the treatment is displayed on the LCD display in real time. The Lucia generator operates simultaneous and independent management of outputs for connecting probes with laser sources from the Red wavelength. The Lucia generator is supplied with 2 completely independent outputs for using two freatment probes. The LCD displays operating parameters from both channels. This allows two operators to carry out laser therapy independently: just like having two separate instruments.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided 510(k) summary does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only mentions "Performance data was presented which showed that skin temperature was elevated as required for this type of device." This suggests a test was conducted, but details are not included in this summary.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth. The study appears to be a technical performance evaluation (skin temperature elevation) rather than one requiring clinical interpretation needing expert consensus.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method. Given the nature of the stated performance data ("skin temperature was elevated as required"), it's likely a direct measurement rather than an expert-adjudicated outcome.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed. This type of study is more common for diagnostic devices where human interpretation is a key component. The Lucia is a therapeutic heating device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The concept of "standalone" performance typically applies to AI algorithms. The Lucia is a physical infrared lamp device, so this question is not applicable in the traditional sense. The performance data would directly reflect the device's physical output.

7. The Type of Ground Truth Used

The ground truth for the "Performance data" was likely direct measurement of skin temperature elevation. The statement "skin temperature was elevated as required for this type of device" implies a quantitative measurement against predefined physiological or regulatory targets.

8. The Sample Size for the Training Set

The document does not mention a training set sample size. This is expected as the Lucia is a physical medical device, not a machine learning model that requires a training set.

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

Since there is no mention of a training set (as the device is not an AI algorithm), this question is not applicable.

Ask a specific question about this device