Search Results

EliA RNA Pol III is intended for the in vitro semi-quantitative measurement of IgG antibodies directed to RNA polymerase III (RNA Pol III) in human serum as an aid in the diagnosis of systemic sclerosis (diffuse form) in conjunction with other laboratory and clinical findings. EliA RNA Pol III uses the EliA IgG method.

EliA RNA Pol III is a semi-quantitative solid-phase fluoroenzymeimmunoassay, for the determination of autoantibodies against RNA polymerase III. The EliA RNA Pol III test System is fully integrated and automated system which comprises of assay-specific reagents, EliA method-specific reagents, and general reagents.

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

Acceptance Criteria and Device Performance

Study Details

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

• Test Set (Method Comparison with Predicate Device):
  • Sample Size: 193 patient serum samples.
  • Data Provenance: Not explicitly stated, but includes 126 samples with a diagnosis of SSc. The context suggests these are clinical samples.
• Test Set (Clinical Sensitivity and Specificity):
  • Sample Size: 596 clinically defined serum samples.
  • Data Provenance: Clinically defined samples from patients with various diagnoses, including Systemic Sclerosis (diffuse and limited forms), and various control diseases/conditions (e.g., Celiac disease, Crohn's disease, SLE, RA, bacterial/viral infections). The text does not specify country of origin or if prospective/retrospective; however, "clinically defined serum samples" typically implies retrospective collection with known diagnoses.
• Test Set (Assay Cut-Off):
  • Sample Size: 70 apparently healthy blood donor samples and 17 target disease (Systemic Sclerosis, diffuse) samples.
  • Data Provenance: "apparently healthy blood donor samples" and "target disease samples" (Systemic Sclerosis, diffuse). The text explicitly mentions "sera from Caucasian, African American, Hispanic and Asian population obtained from a blood bank" for the frequency distribution, which likely overlaps with the "apparently healthy blood donor samples" used for cut-off establishment.

3. Number of Experts and Qualifications for Ground Truth (Test Set):

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The wording "clinically defined serum samples with a diagnosis" implies that the diagnoses used as ground truth were established clinically by medical professionals, but their specific roles, number, or years of experience are not detailed.

4. Adjudication Method (Test Set):

• Adjudication Method: Not specified. The diagnostic labels for the clinical samples are presented as established fact without mention of an adjudication process.

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

• Was an MRMC study done? No. This device is an in-vitro diagnostic (IVD) assay designed for automated measurement of antibodies, not for interpretation by human readers. Therefore, an MRMC comparative effectiveness study regarding human reader improvement with AI assistance is not applicable.

6. Standalone (Algorithm Only) Performance:

• Was a standalone study done? Yes. The entire performance evaluation, including analytical performance, method comparison, and clinical sensitivity/specificity, evaluates the performance of the EliA RNA Pol III assay itself (the "algorithm only" in this context, as it's an automated system) without human interpretation as part of the primary measurement. The results are generated directly by the instrument.

7. Type of Ground Truth Used:

• For Method Comparison: The ground truth was the results from the predicate device, QUANTA LITE RNA POL III ELISA.
• For Clinical Sensitivity and Specificity: The ground truth was clinical diagnosis ("clinically defined serum samples with a diagnosis from patients with systemic sclerosis, diffuse," etc.).
• For Assay Cut-Off: The ground truth was based on apparently healthy blood donors and clinically diagnosed Systemic Sclerosis, diffuse patients.

8. Sample Size for the Training Set:

• Training Set (Assay Cut-Off establishment): This involved 70 apparently healthy blood donor samples and 17 target disease (Systemic Sclerosis, diffuse) samples. While not explicitly called a "training set," these samples were used to "define the cut-off," which is a form of model training/optimization for classification.
• Other "training" data: The document mentions that "New batches of IgG calibrators are compared to a secondary standard (standardized with the IRP) or the IRP directly and adjusted accordingly to meet the correct concentration." This implies a continuous process of calibration and standardization, where previous data/standards (IRP) could be considered a form of "training" for the calibrators. However, a distinct, large-scale training set for an AI/algorithm in the conventional sense is not detailed as this is a chemical assay.

9. How the Ground Truth for the Training Set was Established:

• For Assay Cut-Off establishment: The ground truth for the 70 apparently healthy blood donors means they were confirmed healthy (presumably through standard screening). For the 17 Systemic Sclerosis (diffuse) samples, the ground truth was their clinical diagnosis.
• For Calibrators: The ground truth for calibrators is established through their traceability to the International Reference Preparation (IRP) 67/86 of Human Serum Immunoglobulins A, G and M from WHO.

Ask a specific question about this device

The Quantum DiRex System provides diagnostic quality images to aid the physician with diagnosis. The DiRex can be used to perform radiographic exposures of the skeleton (including skull, spinal column and extremities), chest, abdomen and other body parts. The DiRex is not indicated for use in mammography.

The Quantum DiRex System is an integrated digital imaging system that combines the currently marketed Quantum Q-Rad Radiographic System with the currently marketed Agfa DX-S CR System (digitizer with NX workstation). The Quantum DiRex System is a combination of these previously cleared systems that have been combined and will be marketed as a single system.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Quantum DiRex System:

Based on the provided text, the Quantum DiRex System is an integrated digital imaging system that combines the Quantum Q-Rad Radiographic System with the Agfa DX-S CR System. The primary acceptance criteria for this submission revolve around demonstrating substantial equivalence to predicate devices and meeting pre-determined performance criteria.

Here's a breakdown of the requested information:

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

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

The document does not specify a sample size for any test set or data provenance. The descriptions are general statements about "performance data" and "performance testing."

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

The document does not mention any expert review committee, experts used to establish ground truth, or their qualifications. The testing appears to be focused on technical performance and equivalence, rather than clinical efficacy studies requiring expert reader consensus on diagnostic images.

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

The document does not mention any adjudication method.

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 comparative effectiveness study was not conducted. This submission is for a digital X-ray system, not an AI-powered diagnostic device, hence, there is no mention of human readers improving with AI assistance.

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

This question is not applicable as the device is a medical imaging system (hardware and associated software for image acquisition), not a standalone diagnostic algorithm. The performance described relates to the system's ability to produce diagnostic quality images, not an automated reading of those images.

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

The document does not explicitly state the type of ground truth used. Given the nature of the device (an imaging system, not a diagnostic algorithm), the "performance testing" likely involved evaluating image quality metrics, dose, system functionality, and compliance with pre-determined technical specifications (e.g., spatial resolution, contrast resolution, noise, consistency) rather than clinical ground truth diagnoses.

8. The sample size for the training set

The document does not mention a training set sample size. This is expected as the device is an imaging system, not a machine learning model that requires a training set.

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

This question is not applicable as the device is an imaging system and not an AI/ML model requiring a training set with established ground truth.

Ask a specific question about this device