Search Results

The Panther Fusion GI Expanded Bacterial Assay is a multiplex real-time PCR in vitro diagnostic test for the rapid and qualitative detection and differentiation of Yersinia enterocolitica, Vibrio (V. parahaemolyticus, V. vulnificus, V. cholerae), Escherichia coli O157, and Plesiomonas shigelloides. Nucleic acids are isolated and purified from Cary-Blair preserved stool specimens collected from individuals exhibiting signs and symptoms of gastroenteritis.

This assay is intended to aid in the differential diagnosis of Yersinia enterocolitica, Vibrio (V. parahaemolyticus, V. vulnificus, V. cholerae), Escherichia coli O157, and Plesiomonas shigelloides infections. The results of this assay should be used in conjunction with clinical presentation, laboratory findings, and epidemiological information and should not be used as the sole basis for diagnosis, treatment, or other patient management decisions. Positive results do not rule out coinfection with other organisms that are not detected by this test and may not be the sole or definitive cause of patient illness. Negative results in the setting of clinical illness compatible with gastroenteritis may be due to infection by pathogens that are not detected by this test, or non-infectious causes such as ulcerative colitis, irritable bowel syndrome, or Crohn's disease. This assay is designed for use on the Panther Fusion System.

The Panther Fusion GI Expanded Bacterial Assay is a multiplex real-time PCR in vitro diagnostic test for the rapid and qualitative detection and differentiation of Yersinia enterocolitica, Vibrio (V. parahaemolyticus, V. vulnificus, V. cholerae), Escherichia coli O157, and Plesiomonas shigelloides. Nucleic acids are isolated and purified from Cary-Blair preserved stool specimens collected from individuals exhibiting signs and symptoms of gastroenteritis.

The Panther Fusion System fully automates specimen processing, including sample lysis, nucleic acid capture, amplification, and detection for the Panther Fusion GI Expanded Bacterial Assay. Nucleic acid capture and elution takes place in a single tube on the Panther Fusion System. The eluate is transferred to the Panther Fusion System reaction tube containing the assay reagents. Multiplex real-time PCR is then performed for the eluted nucleic acid on the Panther Fusion System.

Sample processing: Prior to processing and testing on the Panther Fusion System, specimens are transferred to an Aptima Multitest tube containing specimen transport media (STM) that lyses the cells, releases target nucleic acid, and protects them from degradation during storage.

Nucleic acid capture and elution: An internal control (IC-B) is added automatically to each specimen via the working Panther Fusion Capture Reagent-B (wFCR-B) to monitor for interference during specimen processing, amplification, and detection caused by reagent failure or inhibitory substances. Specimens are first incubated in an alkaline reagent (FER-B) to enable cell lysis. Nucleic acid released during the lysis step hybridizes to magnetic particles in the wFCR-B. The capture particles are then separated from residual specimen matrix in a magnetic field by a series of wash steps with a mild detergent. The captured nucleic acid is then eluted from the magnetic particles with a reagent of low ionic strength (Panther Fusion Elution Buffer).

Multiplex PCR amplification and fluorescence detection: Lyophilized single unit dose reaction master mix is reconstituted with the Panther Fusion Reconstitution Buffer I and then combined with the eluted nucleic acid into a reaction tube. Panther Fusion Oil reagent is added to prevent evaporation during the PCR reaction. Target-specific primers and probes then amplify targets via polymerase chain reaction while simultaneously measuring fluorescence of the multiplexed targets. The Panther Fusion System compares the fluorescence signal to a predetermined cut-off to produce a qualitative result for the presence or absence of each analyte.

The analytes and the channel used for their detection on the Panther Fusion System are summarized in the table below:

Assay Components: The assay components configuration for the Panther Fusion GI Expanded Bacterial Assay is analogous to the Panther Fusion Respiratory Assays. The reagents required to perform the Panther Fusion GI Expanded Bacterial Assay are packaged and sold separately. There are 7 boxes containing 9 reagents which are required for sample processing. The Panther Fusion GI Expanded Bacterial Assay requires one ancillary kit and one specimen collection kit, neither of which are provided with the assay and can be acquired separately:

1. Aptima Assay Fluids Kit (303014)
2. Aptima Multitest Swab Specimen Collection Kit (PRD-03546)

Table 1: Reagents Required to Perform the Panther Fusion GI Expanded Bacterial Assay

Table 2: Ancillary and Collection Kits Required to Perform the Panther Fusion GI Expanded Bacterial Assay

Instrumentation: The Panther Fusion GI Expanded Bacterial Assay has been designed for and validated on the Panther Fusion system. The Panther Fusion System fully automates specimen processing, including sample lysis, nucleic acid capture, amplification, and detection for the Panther Fusion GI Expanded Bacterial Assay.

N/A

Ask a specific question about this device

The Genius TM Digital Diagnostics System with the Genius™ Cervical AI algorithm includes the Genius TM Digital Imager, Genius™ Image Management Server (IMS), the Genius™ Review Station, and the Genius™ Cervical AI algorithm. The Genius™ Digital Diagnostics System with the Genius™ Cervical AI algorithm is intended for the creation and viewing of digital images of scanned ThinPrep® Pap Test glass slides. Objects of interest selected by the Genius™ Cervical AI algorithm from the scanned digital image are presented in a gallery format next to the image of the whole cell spot on the Genius™ Review Station for review and interpretation. The Genius Digital Diagnostics System with the Genius™ Cervical AI algorithm is intended to aid in cervical cancer screening for the presence of atypical cells, cervical neoplasia, including its precursor lesions (Low Grade Squamous Intraepithelial Lesions, High Grade Squamous Intraepithelial Lesions) and carcinoma, as well as all other cytological categories as defined by The Bethesda System for Reporting Cervical Cytology1.

After digital review with the Genius Cervical AI algorithm, if there is uncertainty in the diagnosis, then direct examination of the glass slide by light microscopy should be performed. Digital images from the Genius Digital Diagnostics System with the Genius™ Cervical AI algorithm should be interpreted by qualified cytologists in conjunction with the patient's screening history, other risk factors, and professional guidelines which guide patient management.

The Genius Digital Diagnostics System includes, Genius Digital Image. Management Server, Genius Review Station(s), and Genius Cervical AI algorithm, as shown in Figure 1 below.

Here's a breakdown of the acceptance criteria and the study that proves the Genius™ Digital Diagnostics System with the Genius™ Cervical AI algorithm meets them, based on the provided text:

Acceptance Criteria and Device Performance for Genius™ Digital Diagnostics System with Genius™ Cervical AI algorithm

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly lay out bulleted acceptance criteria for the entire system in a single section. However, performance benchmarks derived from various studies are detailed. The primary clinical acceptance criteria revolve around the AI-assisted review being comparable or superior to the standard of care (manual review) and other automated systems (TIS review) in terms of sensitivity and specificity at various diagnostic thresholds for cervical cytology. Key technical performance criteria are also highlighted.

Here is a summary, inferred from the "Performance Characteristics" section:

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

• Sample Size for Clinical Test Set: 1994 slides.
• Data Provenance:
  • Country of Origin: United States ("A multi-center Genius Cervical AI Algorithm Clinical Study was performed within the United States.").
  • Retrospective/Prospective: The study used "residual material after the clinical sites signed out the case," implying these were historical samples (retrospective) collected from women screened for cervical cancer using the ThinPrep Pap test.

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

• Number of Experts: An adjudication panel composed of 3 adjudication CT/PT teams, each consisting of 1 Cytotechnologist (CT) and 1 Pathologist (PT). So, a minimum of 3 CTs and 3 PTs were involved in establishing the ground truth.
• Qualifications of Experts: They were "qualified cytologists" and "qualified CTs and PTs" (implied as experts in cervical cytology based on their role in the study and laboratory context). The participating laboratories had "extensive experience in the processing and evaluation of gynecologic ThinPrep Pap test slides" and the experts were "trained in the use of the Genius Digital Diagnostics System with Genius Cervical AI algorithm."

4. Adjudication Method for the Test Set

• Method: Consensus-based adjudication followed by multi-headed microscope review for discordant cases.
  1. Each of the three adjudication CT/PT teams independently reviewed the slides.
  2. A consensus result was obtained if there was majority agreement (at least two of the three adjudication CT/PT teams).
  3. If a consensus was not initially obtained, these cases underwent review by the three adjudication PTs simultaneously using a multi-headed microscope (multi-head review).
  4. The final "reference" or "ground truth" diagnosis was based on either the initial consensus or the multi-head review result.

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

• Was it done? Yes, a multi-center, multi-reader, multi-case study was performed, comparing Genius AI review, Manual review, and TIS review. This is evident from the "Clinical Studies" section, where 4 clinical sites participated, and at each site, 3 independent CT/PT teams reviewed cases.

• Effect Size of Human Readers Improvement with AI vs. without AI Assistance:
  The study directly compares human performance with AI assistance (Genius AI review) to human performance without AI assistance (Manual review). The improvement is shown in sensitivity at various diagnostic thresholds:

  • LSIL+: Sensitivity increased by 4.4% with Genius AI assistance compared to Manual review (89.1% vs 84.7%). This was statistically significant.
  • ASC-H+: Sensitivity increased by 8.2% with Genius AI assistance compared to Manual review (87.8% vs 79.6%). This was statistically significant.
  • HSIL+: Sensitivity increased by 7.5% with Genius AI assistance compared to Manual review (81.5% vs 74.0%). This was statistically significant.
  • ASCUS+: Sensitivity increased by 1.6% (91.7% vs 90.1%), but this was not statistically significant.
  • UNSAT (for correct identification): Sensitivity increased by 18.6% with Genius AI assistance (80.4% vs 61.8%).

  While specificity generally saw small, statistically significant decreases at some thresholds with AI assistance, the overall benefit-risk assessment concluded that the probable benefits outweigh the risks due to the increased sensitivity, particularly for higher-grade lesions. The document also highlights a reduction in false negative rates (false NILM) with the Genius AI: "The 7.5% increase in HSIL + sensitivity means a decrease in Manual false negative rate of 26% to 18.5% false negative rate by the Genius Digital Diagnostics System with the Genius Cervical AI algorithm resulted in 28.8% reduction in the number false negative reviews (28.8%=(26%-18.5%) / 26%)."

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

• Was it done? Not explicitly for diagnostic interpretation. The "Objects of Interest (OOI) Reproducibility" study evaluated the AI algorithm's ability to select OOIs against the adjudicated reference diagnosis, but the primary clinical performance assessment was human-in-the-loop (AI-assisted review). The AI's function is to "selects OOIs to be displayed for review by a CT or PT," not to render a final diagnosis on its own.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus. The "adjudicated diagnosis" served as the "gold standard" or "reference" (ground truth). This was established by a panel of 3 CT/PT teams reaching consensus, with multi-head microscope review for discordant cases.

8. Sample Size for the Training Set

• The document mentions the use of "Convolutional Neural Network (CNN) technology" for the AI algorithm but does not explicitly state the sample size for the training set. It only mentions that the "Genius Cervical AI algorithm v1.0.16.0 will remain locked for use with the authorized device and will not be continually trained and improved with each cohort analyzed in clinical practice, after marketing authorization," implying it was trained on a fixed dataset prior to these performance studies.

9. How Ground Truth for the Training Set Was Established

• The document does not explicitly describe how the ground truth for the training set was established. It focuses only on the ground truth for the test set used for performance validation.

Ask a specific question about this device

The Affirm Prone Biopsy System combines the function of a standard x-ray mammography unit with that of a lesion localization system to produce a device that has specific application in first accurately localizing lesions in the breast in two and/or three dimensions, and then providing guidance for interventional purposes (such as biopsy, pre-surgical localization or treatment devices) for lesions determined to be suspicious through prior mammographic examination.

It is intended for lesion location for biopsy while the patient is in the prone position to provide guidance for interventional purposes (such as biopsy, presurgical localization or treatment devices).

The Affirm Prone Biopsy Table is a mammographic x-ray system intended for lesion location while the patient is in the prone position. The subject device will be capable of both 2D and 3D imaging methods to calculate the target location. The system localizes suspicious lesions, as determined through prior mammographic examinations, using either stereotactic or tomosynthesis techniques. The system then affords a physician the capacity of performing vacuum assisted or needle core biopsy, or wire localization of the lesion.

Localization can be accomplished either via conventional 2D stereotactic imaging, or by use of a 3D data set. With the 3D data set, the image plane or "slice" most fully containing the suspected lesion is chosen by the physician from the data set to compute the lesion depth within the breast.

Here's an analysis of the provided text to extract information about acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance:

Based on the provided document, specific quantitative acceptance criteria or a direct performance table are not explicitly stated or presented. The document focuses on regulatory compliance and substantial equivalence to predicate devices.

However, the "Performance Testing" section (Page 4, Section 7) indirectly points to an acceptance criterion:

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

The document does not specify a sample size for a test set in the context of clinical performance or diagnostic accuracy. The testing mentioned appears to be primarily engineering verification and validation against safety and quality standards.

The data provenance (e.g., country of origin, retrospective/prospective) related to any clinical test set is not mentioned.

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

This information is not provided in the document. The type of testing described (compliance with IEC standards, design control) does not typically involve expert-established ground truth in the same way a clinical performance study would.

4. Adjudication Method for the Test Set:

An adjudication method (e.g., 2+1, 3+1) for a clinical test set is not mentioned because a clinical test set for diagnostic performance is not described.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance is not mentioned in this document. The device is a "Biopsy System" for guidance, not a diagnostic AI interpretation tool.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:

The document describes the "Affirm Prone Biopsy System" as a physical mammographic x-ray system that provides guidance for interventional procedures. While it utilizes "2D and 3D imaging methods to calculate the target location" (Page 4, Section 4), it is fundamentally a system that aids a physician. Therefore, a standalone (algorithm only) performance, separate from human operation or decision-making, is not described or implied. The device's function is to guide a human operator.

7. The Type of Ground Truth Used:

The document focuses on engineering and regulatory ground truth:

• Compliance with IEC 60601-2-45 standards: This implies meeting established technical specifications and safety requirements.
• Compliance with 21 CFR Part 820 (Design Controls): This refers to documented evidence that the device design meets user needs and intended uses.
• Equivalence to Predicate Devices: The "ground truth" for its performance is its demonstrated similarity in technology, operation, and functional performance to previously cleared devices.

There is no mention of clinical ground truth such as pathology, expert consensus on lesions, or outcomes data for a diagnostic or interventional accuracy claim. The device's purpose is localization and guidance, not diagnostic interpretation.

8. The Sample Size for the Training Set:

The document does not mention a training set size. This is consistent with the nature of the device being an imaging and guidance system, rather than a machine learning or AI algorithm requiring a training set for feature learning. The image processing for 2D/3D localization would be based on established algorithms, not necessarily a trained machine learning model.

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

Since a training set is not mentioned, the method for establishing its ground truth is also not applicable/provided.

Ask a specific question about this device