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The UBiT-IR300 Infrared Spectrometry System is an in vitro diagnostic device designed to measure changes in 1302 content in breath CO2 gas by infrared spectroscopic analysis. The system consists of the UBiT-IR300 Infrared Spectrophotometer, the UBiT-AS10 Autosampler, and Otsuka Breath Collection Bags.

The UBiT-IR300 Infrared Spectrometry System is intended for use in conjunction with commercially available Meretek 13C-urea breath tests for the detection of Helicobacter pylori (H. pylori) infection. The UBiT-IR300 System is suitable for use in both clinical laboratory and point-of-care settings.

The UBiT-IR300 Infrared Spectrometry System is a compact analyzer designed for use in conjunction with commercially available Meretek 13C-urea breath tests for the detection of Helicobacter pylori. The UBiT-IR300 measures absorption of breath gas by calculating the ratios of 13CO2/12CO2 for a reference breath gas and a sample breath gas. The difference between the ratios for the reference and sample breath gases is calculated to obtain the final measurement result, which is reported as △'3CO2 and expressed as delta per mil (%) or Delta Over Baseline (DOB).

The System consists of the following components:

• UBiT-IR300 Infrared Spectrophotometer -
• UBiT-AS10 Autosampler -
• Otsuka Breath Collection Bags -

The UBiT-IR300 Infrared Spectrometry System is intended for use in conjunction with commercially available Meretek 13C-urea breath tests for the detection of Helicobacter pylori (H. pylori) infection. The device was compared against the traditional Gas Isotope Ratio Mass Spectrometry (GIRMS) method.

Here's the breakdown of the acceptance criteria and study details:

Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria are not explicitly stated as numerical thresholds in the provided text. However, for a device seeking 510(k) clearance by demonstrating substantial equivalence, the expectation is that its performance is comparable to or non-inferior to the predicate device. The presented results clearly indicate a very high level of agreement and correlation, suggesting these metrics met the implicit requirements for substantial equivalence.

Study Details

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

   • Sample Size: 320 evaluable subjects.
     • 257 subjects from combined Physician Office Laboratory (POL) sites.
     • 63 subjects from one clinical laboratory site.
   • Data Provenance: The study was a multi-center, prospective study. The country of origin is not explicitly stated, but given the sponsor (Otsuka Pharmaceutical Co., Ltd., Japan) and the contact person's US number, it's likely a US-based or international study with US participant sites. The data is prospective as subjects were recruited and underwent the urea breath test for the study.

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

   • The ground truth in this study was established using the traditional Gas Isotope Ratio Mass Spectrometry (GIRMS) method. The study design directly compares the UBiT-IR300's results to the GIRMS method.
   • Therefore, the "experts" in this context are the established and recognized methodology of GIRMS for 13CO2 enrichment measurement. There's no mention of a separate panel of human experts in the traditional sense (e.g., radiologists) establishing ground truth, as it's a direct analytical comparison.

3. Adjudication Method for the Test Set:

   • Not applicable in the conventional sense. The "adjudication" is inherent in the comparison of the UBiT-IR300 results against the GIRMS method, which serves as the reference standard. Agreement and correlation were calculated based on this direct comparison.

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, this was not an MRMC study. This study evaluated the performance of an analytical device (UBiT-IR300) directly against a reference analytical method (GIRMS) for measuring 13CO2 enrichment, not human reader performance.

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

   • Yes, this study represents a standalone performance evaluation of the UBiT-IR300 Infrared Spectrometry System. It measures the device's ability to accurately measure 13CO2 enrichment independently, with its results then compared to the established GIRMS method.

6. The type of ground truth used:

   • The ground truth was established by the traditional Gas Isotope Ratio Mass Spectrometry (GIRMS) method. This is an established and accepted analytical method for measuring 13CO2 enrichment in breath.

7. The sample size for the training set:

   • The provided summary does not explicitly mention a separate "training set" for the device's development. This is typical for an analytical instrument where performance is often based on the device's physical and algorithmic design, rather than a machine learning model that requires a distinct training phase on clinical data. The clinical study described served as a validation/test set.

8. How the ground truth for the training set was established:

   • As no explicit training set is mentioned in the summary, this question is not directly applicable. If a training phase existed during device development, the ground truth would likely have been established through controlled experiments and calibrations using known standards for 13CO2 enrichment, analogous to the GIRMS method used for the clinical validation.

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The Digene DML 2000 Microplate Luminometer is intended to measure light that is emitted as a result of a chemiluminescent reaction. Assay results obtained using chemiluminescence technology in 96 well microplates are calculated and interpreted according to assay validation parameters.

The DML 2000 is a microplate luminometer capable of reading chemiluminescence from opaque microplates. Chemiluminescence is the production of light from a chemical reaction. The luminometer is optimally designed for measurement of glow type luminescence. The luminometer is designed to be run and controlled by a personal computer connected via a serial RS-232 interface port. The instrument is simple in design with measurement taking place in 96-well microplates, where each well is presented to the detector by stepper motors moving the plate. The light is detected by a photomultiplier tube. The light is amplified and converted to an electrical signal which is detected by a sensitive electrical amplifier. The detected signals are converted to Relative Light Units (RLU) and are reported for each well of the microplate. Assay results are calculated and interpreted according to assay validation parameters.

The provided text describes a 510(k) premarket notification for the Digene DML 2000 Microplate Luminometer. It outlines the device's description, intended use, and substantial equivalence to a predicate device. However, the document does not contain information related to acceptance criteria, specific performance studies, sample sizes, expert involvement, or ground truth establishment relevant for an AI/ML medical device.

The device described is a microplate luminometer, a laboratory instrument designed to measure light produced by chemiluminescent reactions. It is not an AI/ML-based device that would typically have acceptance criteria related to algorithmic performance metrics like sensitivity, specificity, or AUC, nor "studies" in the context of evaluating an AI model.

Therefore, I cannot provide the requested table and details because the provided document does not contain this type of information. The 510(k) summary focuses on demonstrating substantial equivalence of a physical laboratory instrument, not the performance of an AI/ML algorithm.

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THE ALPHA 4 LS IS INTENDED TO AUTOMATE MICROTITER PLATE PROCESSOR AND READER THAT MEASURES THE LIGHT ABORBANCE OR LUMINESCENCE OF REACTION PRODUCTS IN EITHER MODE.

ALPHA 4 LS Automated Microtiterplate Processor and Reader

This document is a 510(k) clearance letter from the FDA for the "Alpha 4 LS Automated Microtiterplate Processor and Reader". It states that the FDA has determined the device is substantially equivalent to legally marketed predicate devices.

However, this letter does not contain any information regarding acceptance criteria, device performance studies, sample sizes, ground truth establishment, or expert qualifications. These details are typically found in the 510(k) submission itself, which is not provided here.

Therefore, I cannot fulfill your request for detailed information about acceptance criteria and the study proving the device meets them based solely on the provided text. The document is essentially a regulatory approval letter, not a technical performance report.

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This accessory program may be used by customers with the general purpose analyzer (K882938) who want to link their instrument to a PC for the purpose of transferring the data from the general purpose instrument to the PC, instead of having the data typed manually by a data entry person

Stat Tracks is a dedicated software interface and reporting tool comparable to any other commercially available software. It is also comparable to manual writing, graphing, and filing.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Stat Tracks device:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated. The text mentions "Design Assurance Testing" and that "Stat Tracks has been tested," but does not provide details on the number of tests, cases, or scenarios included in this testing.
• Data Provenance: Not specified. It's likely that the testing was internal to Awareness Technology, Inc., given the nature of a software interface. There's no indication of independent testing or specific geographical origin beyond the company's location in Palm City, FL, USA. The testing appears to be prospective in the sense that it was conducted as part of the device's development and verification.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The testing would have likely involved "laboratory professionals" as they are the intended users, but their specific qualifications for evaluating the software's performance are not detailed.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the nature of a software interface for data transfer, it's unlikely that a formal adjudication process involving multiple readers was employed in the same way it would be for diagnostic image interpretation. The testing likely focused on functional verification and error checking.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, an MRMC comparative effectiveness study was not done. The document describes a software tool for data transfer, not a diagnostic aid that would typically involve human readers interpreting complex cases. The comparison is made to "other commercially available software" and "manual methods," but this is a statement of equivalence rather than a formal MRMC study.
• Effect Size of Human Reader Improvement with AI vs. Without AI Assistance: Not applicable, as this was not an AI-assisted diagnostic tool for human readers.

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

• Yes, a standalone performance assessment was done. The "Design Assurance Testing" described is essentially a standalone assessment of the software's functionality, focusing on whether it produces intended results, activates error messages, and avoids patient mis-identification. The device's primary function is to automate data transfer, which it does without direct human intervention during the transfer process itself (though a human initiates it). The phrase "No device-induced case of mis-identification of a patient result has been found under any circumstance of testing" directly refers to the algorithm's standalone performance in preventing errors.

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for this device appears to be defined by:
  • Functional correctness: Whether data is transferred accurately and as intended.
  • Error message activation: Whether specific error conditions correctly trigger appropriate messages.
  • Absence of data mis-identification: Verifying that patient results are not mistakenly linked to the wrong patient or test.
  • This is essentially a form of functional verification against predefined specifications and expected outputs.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a software interface and reporting tool, not a machine learning or artificial intelligence algorithm that requires a "training set" in the conventional sense. Its "training" would be its development and debugging process.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no traditional "training set" for this type of software. The "ground truth" for the software's development would be its functional specifications and adherence to programming logic.

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The Anthos PhACTS 2010 Microplate Reader is intended to be used as a general purpose microplate photometer for clinical use.

The Anthos PhACTS 2010, 2001 and its predicate devices all share the same principle of operation. Briefly, each device provides a light source which is focused to provide illumination of wells in a microtiter plate. A corresponding silicon-photodiodes measures the amount of light absorbed by the sample as the light passes through the microplate well. The respective well absorbance measurements is used by the data reduction software to yield a specific well absorbance value which in turn can yield a qualitative or quantitative assay result.

This document describes an application for a microplate reader, which is a laboratory instrument, not a medical device that uses AI or requires extensive clinical studies with human subjects or expert ground truth. Therefore, many of the requested categories are not applicable.

Here's the relevant information from the provided text, structured to address your request where possible:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in the way a clinical study for a diagnostic AI might. Instead, it focuses on demonstrating substantial equivalence to predicate devices. The performance is implied to be equivalent to the predicate devices.

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

Not applicable. This is a submission for a laboratory instrument, not a diagnostic algorithm evaluated on a "test set" of patient data. The "test set" in this context would refer to internal validation of the instrument's accuracy and precision, which is usually performed by the manufacturer according to their own protocols, but is not detailed in this summary.

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

Not applicable. Ground truth as typically understood for diagnostic algorithms (e.g., expert consensus on medical images) is not relevant for a microplate reader. The "truth" for a microplate reader relates to its ability to accurately and precisely measure absorbance values.

4. Adjudication Method for the Test Set

Not applicable. There is no "test set" in the sense of patient cases requiring expert 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

Not applicable. This device is a microplate reader, not an AI-powered diagnostic tool, and therefore MRMC studies are not relevant. There is no human-in-the-loop AI component.

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

Not applicable. This device is a physical instrument, not a standalone algorithm.

7. The Type of Ground Truth Used

The "ground truth" for a microplate reader would be established through calibration with known absorbance standards and validation against established reference methods, ensuring its measurements are accurate. This is an engineering and metrology "ground truth" rather than a clinical diagnostic "ground truth." The document implies that the device would perform as expected for a microplate photometer, mirroring its predicates.

8. The Sample Size for the Training Set

Not applicable. This device does not use machine learning or AI that requires a "training set" of data.

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

Not applicable. See point 8.

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The CBI 7520 Microplate Reader is intended to be used as a general purpose microplate photometer for clinical use.

The CBI 7520 microplate reader employs an automatic transport mechanism to bring the individual microplate wells into position for reading. The device employs Tungsten halogen lamp as the light source. The device uses silicon-photodiodes to measure the light absorbance.

This document describes a 510(k) premarket notification for the CBI 7520 Microplate Reader. It focuses on demonstrating substantial equivalence to predicate devices rather than presenting a study with acceptance criteria in the manner typically seen for novel AI/ML medical devices.

Therefore, many of the requested categories are not applicable or cannot be extracted from the provided text. The device described, a microplate photometer, does not involve AI, and thus concepts like "AI assistance," "ground truth," or "expert consensus" for image analysis are not relevant here.

Here's an analysis based on the provided text, addressing the points where possible:

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

The document does not explicitly state quantitative "acceptance criteria" or a detailed "reported device performance" in the way one would for a new diagnostic assay or AI algorithm. Instead, the basis of equivalence is qualitative similarity in intended use, principles of operation, and technical characteristics.

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

Not applicable. The document does not describe a performance study with a test set of samples/data. It relies on demonstrating similarity to already cleared devices.

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. There is no mention of a test set requiring expert-established ground truth.

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

Not applicable. No test set or adjudication process is described.

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

Not applicable. This is not an AI device, and no MRMC study is mentioned.

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

Not applicable. This is a hardware device, not an algorithm.

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

Not applicable. The concept of "ground truth" as applied to diagnostic algorithms is not relevant for this device's submission.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set.

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

Not applicable.

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