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The Aevumed PROTEKT™ Suture Anchors are intended to be used for suture or tissue fixation in the foot, ankle, knee, hand, wrist, elbow, shoulder, and hip. Specific indications are listed below:

Shoulder: Rotator Cuff Repair, Bankart Repair, SLAP Lesion Repair, Biceps Tenodesis, Acromio-Clabicular Separation Repair, Deltoid Repair, Capsular Shift or Capsulolabral Reconstruction

Foot/Ankle: Lateral Stabilization, Medial Stabilization, Achilles Tendon Repair, Metatarsal Ligament Repair, Hallux Valgus Reconstruction, Digital Tendon Transfers, Mid-foot Reconstruction

Knee: Medial Collateral Ligament Repair, Lateral Collateral Ligament Repair, Patellar Tendon Repair, Posterior Oblique Ligament Repair, Iliotibial Band Tenodesis

Hand/Wrist: Scapholunate Ligament Reconstruction, Carpal Ligament Reconstruction, Repair/Reconstruction of Collateral Ligaments, Repair of Flexor and Extensor Tendons at the PIP, DIP, and MCP joints for all Digits, Digital Tendon Transfers

Elbow: Biceps Tendon Reattachment, Ulnar or Radial Collateral Ligament Reconstruction

Hip: Capsular repair, Acetabular Labral Repair

The Aevumed PROTEKT™ Sutures Anchor with HS Fiber™ suture is a suture anchor manufactured from polyetheretherketone (PEEK) material and are preloaded on a disposable inserter assembly intended for fixation of soft tissue to bone. The Aevumed PROTEKT™ Suture Anchors is available in diameter size: 6.5 mm. It is offered sterile and is for single use only.

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The Access anti-HAV IgM assay is a paramagnetic particle, chemiluminescent immunoassay for the in vitro qualitative detection of IgM antibodies to hepatitis A virus (anti-HAV IgM) in human pediatric (2 through 21 years) and adult serum and serum separator tubes or plasma [lithium heparin, lithium heparin separator tubes, dipotassium (K2) EDTA, and tripotassium (K3) EDTA] using the DxI 9000 Access Immunoassay Analyzer. The Access anti-HAV IgM assay results may be used as an aid in the laboratory diagnosis of acute or recent hepatitis A virus (HAV) infection in individuals with signs and symptoms of hepatitis A virus, when used in conjunction with other serological and clinical information.

This assay is not intended for use for screening donors of blood or blood products or human cells, tissues, or cellular or tissue-based products (HCT/Ps).

The Access anti-HAV IgM assay requires Access anti-HAV IgM (reagent packs), Access anti-HAV IgM Calibrator (C1), and Access anti-HAV IgM QC (QC1-QC2). The Access anti-HAV IgM assay is a two-step sandwich immunoassay. Paramagnetic particles coated with anti-human IgM monoclonal antibody and prediluted sample are added to a reaction vessel. After incubation, materials bound to the solid phase are held in a magnetic field while unbound materials are washed away. HAV antigen and anti-HAV monoclonal antibody alkaline phosphatase conjugate are added. HAV antigen complexed to the conjugate binds to the IgM antibodies captured on the particles. A second separation and wash step removes unbound conjugate.

A chemiluminescent substrate is then added to the vessel and light generated by the reaction is measured with a luminometer. The light production is compared to the cut-off value defined during calibration of the instrument. The qualitative assessment is automatically determined from a stored calibration.

Quality control (QC) materials simulate the characteristics of patient samples and are essential for monitoring the system performance of the Access anti-HAV IgM immunoassay. In addition, they are an integral part of good laboratory practices. When performing assays with Access reagents for anti-HAV IgM, include quality control materials to validate the integrity of the assay. The assayed values should fall within the acceptable range if the test system is working properly.

The Access anti-HAV IgM reagents are provided in liquid ready-to-use format designed for optimal performance on the Beckman Coulter DxI 9000 Access Immunoassay Analyzer only. Each reagent kit contains two reagent packs. The Access anti-HAV IgM Calibrator kit contains one vial, and the Access anti-HAV IgM QC kit contains three vials each of anti-HAV IgM positive control and anti-HAV IgM negative control. Other items needed to run the assay include Lumi-Phos PRO (chemiluminescent substrate) and UniCel DxI Wash Buffer II.

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The Ascent3T Neonatal Magnetic Resonance Imaging System (Ascent3T) is a whole-body magnetic resonance scanner designed for neonates and infants. The system can produce cross-sectional images of the internal structure of the head, body or extremities in any orientation.

Images produced by the Ascent3T show the spatial distribution of protons exhibiting magnetic resonance. Images produced by the Ascent3T, when interpreted by a trained physician, may provide information useful in diagnosis.

The Ascent3T Neonatal Magnetic Resonance Imaging System is suitable for neonates and infants weighing up to 9kg (19.8 lbs).

The Ascent3T Neonatal Magnetic Resonance Imaging System (Ascent3T) is a high-field magnetic resonance imaging system, appropriately sized and optimized for the neonate and infant population, with a format that allows siting near point of care. The Ascent3T presents a solution for the technical limitations associated with using an adult-size MRI system and provides clinicians with an improved ability to visualize and diagnose disease in the neonatal patient population.

The Ascent3T is equipped with a small format superconducting magnet designed for neonate applications. The system is designed to operate at 3.0 Tesla and achieves a high level of homogeneity over a 24cm diameter spherical volume using passive shims. The magnet requires a minimal amount of helium and no quench pipe. These features, in combination with the size and weight of the magnet, support near-patient siting.

The Ascent3T patient table is detachable and can serve as a patient transport device. The patient table includes a tabletop cradle with features for securing the patient during scanning. The patient table is mobile, providing flexibility in workflow based on institutional needs and preferences.

The Ascent3T contains a menu of pulse sequences intended to provide the user with a variety of sequences useful for producing images for diagnostic purposes.

Key Features of the Ascent3T:

• 3T superconducting magnet with 25cm patient bore.
• Minimal helium capacity with no quench pipe required.
• Gradient system: 80 mT/m maximum amplitude per axis, 300 mT/m/ms slew rate per axis.
• Real-time SAR monitoring and alerts with Normal and First-Level Controlled Operating Modes.
• Capable of producing images in axial, sagittal, coronal, and oblique orientations.
• Accommodates neonates and infants weighing up to 9 kg (19.8 lbs).
• Detachable, mobile patient table with built-in safety features.

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It is used by radiation oncology department to segment CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaption.

The proposed device, AccuContour 4.0 Family, is a standalone software with the following variants: AccuContour and AccuContour-Lite. The functions of AccuContour-Lite is a subset of AccuContour.

AccuContour:
It is used by oncology department to register multi-modality images and segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.

The product has two image processing functions:

1. Deep learning contouring: it can automatically contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas,
2. Automatic registration: rigid and deformable registration, and
3. Manual contouring.

It also has the following general functions:

• Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
• Patient management;
• Review tool of processed images;
• Extension tool;
• Plan evaluation and plan comparison;
• Dose analysis.

AccuContour-Lite:
It is used by oncology department to segment (non-contrast) CT images, to generate needed information for treatment planning, treatment evaluation and treatment adaptation.

The product has one image processing function:
Deep learning contouring: it can automatically contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas,

It also has the following general functions:

• Receive, add/edit/delete, transmit, input/export, medical images and DICOM data;
• Patient management;
• Review tool of processed images.

Here's an analysis of the acceptance criteria and study details for the AccuContour 4.0, extracted and organized from the provided FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are derived from the "Pass Criteria" columns in Tables 1, 2, 3, and 4, which specify minimum DSC and maximum HD95 values. The reported device performance is represented by the "Lower Bound 95% CI" for both DSC and HD95, and the "Average Rating" for clinical applicability.

Table A: Performance for Synthetic CT (sCT) Contouring Function (Derived from MR Images)

*Note: For Stomach, the reported DSC (0.782) is below the pass criteria (0.8). However, the document states, "The results indicate that the auto-segmentation performance of the AccuContour system for sCT images derived from both CBCT and MR modalities meets the requirements for geometric accuracy." This suggests there might be an overall or combined assessment, or other factors led to acceptance despite this single instance. The average clinical rating is 4.1, which is above the threshold of 3.

Table B: Performance for Synthetic CT (sCT) Contouring Function (Derived from CBCT Images)

Table C: Performance for 4DCT Registration Function (Rigid Registration)

*Note: For Stomach (0.791) and Spleen (0.786), the reported DSC is below the pass criteria (0.8). However, the document states, "According to the results, the accuracy of 4DCT image registration images meets the requirements and all structure models demonstrating that only minor edits would be required in order to make the structure models acceptable for clinical use." The average clinical rating for both is 4.5, above the threshold of 3.

Table D: Performance for 4DCT Registration Function (Deformable Registration)

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

• Synthetic CT (sCT) Contouring Function:

  • Sample Size: 247 synthetic CT images (116 generated from MR, 131 generated from CBCT).
  • Data Provenance:
    • Demographic Distribution: 57% male, 43% female. Age distribution: 13% (21-40), 44.1% (41-60), 36.8% (61-80), 6.1% (81-100). Race: 78% White, 12% Black or African American, 10% Others.
    • Imaging Equipment: MR images from GE (21.6%), Philips (56.9%), Siemens (21.6%). CBCT images from Varian (58.8%), Elekta (41.2%).
    • Retrospective/Prospective: Not explicitly stated, but the description of demographic and equipment distribution from a "sample" indicates retrospective data collection from existing patient records.
    • Country of Origin: The racial distribution explicitly mentions "U.S. clinical radiotherapy practice," suggesting the data is primarily from the United States.

• 4DCT Registration Function:

  • Sample Size: 30 4DCT image sets.
  • Data Provenance:
    • Imaging Equipment: Siemens (90.0%), Philips (10.0%) scanners.
    • Demographic Distribution: 17 males (56.7%), 13 females (43.3%). Age: 33-82 years, with majority in 51-65 (40.0%) and 66-80 (43.3%) year brackets.
    • Image Characteristics: Uniform 3mm slice thickness (100%).
    • Sourcing Location: Most images (90.0%) from Drexel Town Square Health Center/Community Memorial Hospital, remainder from Froedtert Hospital.
    • Retrospective/Prospective: Not explicitly stated, but implies retrospective data from patient archives of the mentioned hospitals.
    • Country of Origin: Based on the hospital names (Drexel Town Square Health Center, Community Memorial Hospital, Froedtert Hospital), the data is from the United States.

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

• Number of Experts: Not explicitly stated. The text mentions "clinical experts evaluate the clinical applicability" and "RTStruct contoured by the professional physician as the gold standard." This implies at least one, and likely multiple, qualified medical professionals.
• Qualifications of Experts: The experts are described as "clinical experts" and "professional physician(s)." Their specific qualifications (e.g., "radiologist with 10 years of experience") are not provided. They are implied to be clinically qualified radiotherapy personnel.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The ground truth for segmentation is stated to be "RTStruct contoured by the professional physician". For clinical applicability, "clinical experts evaluate the clinical applicability" and assign a 1-5 scale score. This suggests a single expert (or group consensus without specific adjudication rules like 2+1) established the ground truth segmentation, and separate clinical experts evaluated the results. There is no mention of a formal adjudication process for disagreements in ground truth labeling if multiple experts were involved in its creation.

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

• Was an MRMC study done? No.
• Effect Size of Human Improvement (if applicable): Not applicable, as no MRMC study comparing human readers with and without AI assistance was reported. The testing focused solely on the algorithm's performance against expert-generated ground truth and expert evaluation of the algorithm's output.

6. Standalone Performance

• Was a standalone performance study done? Yes. The entire report details the "Performance Test Report on Synthetic CT (sCT) Contouring Function" and "Performance Test Report on 4DCT Registration Function," measuring the algorithm's performance (DSC, HD95) against gold standard contours and qualitative evaluation by clinical experts. This reflects the algorithm's performance independent of human interaction during the contouring process.

7. Type of Ground Truth Used

• Ground Truth: For the synthetic CT contouring and 4DCT registration functions, the ground truth was "RTStruct contoured by the professional physician" (i.e., expert consensus or expert-generated contours).

8. Sample Size for the Training Set

• Training Set Sample Size: Not provided in the document.

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

• Training Set Ground Truth Establishment: Not provided in the document. The document only details the ground truth used for the validation/test set.

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AV Vascular is indicated to assist users in the visualization, assessment and quantification of vascular anatomy on CTA and/or MRA datasets, in order to assess patients with suspected or diagnosed vascular pathology and to assist with pre-procedural planning of endovascular interventions.

AV Vascular is a post-processing software application intended for visualization, assessment, and quantification of vessels in computed tomography angiography (CTA) and magnetic resonance angiography (MRA) data with a unified workflow for both modalities.

AV Vascular includes the following functions:

• Advanced visualization: the application provides all relevant views and interactions for CTA and MRA image review: 2D slides, MIP, MPR, curved MPR (cMPR), stretched MPR (sMPR), path-aligned views (cross-sectional and longitudinal MPRs), 3D volume rendering (VR).

• Vessel segmentation: automatic bone removal and vessel segmentation for head/neck and body CTA data, automatic vessel centerline, lumen and outer wall extraction and labeling for the main branches of the vascular anatomy in head/neck and body CTA data, semi-automatic and manual creation of vessel centerline and lumen for CTA and MRA data, interactive two-point vessel centerline extraction and single-point centerline extension.

• Vessel inspection: enable inspection of an entire vessel using the cMPR or sMPR views as well as inspection of a vessel locally using vessel-aligned views (cross-sectional and longitudinal MPRs) by selecting a position along a vessel of interest.

• Measurements: ability to create and save measurements of vessel and lumen inner and outer diameters and area, as well as vessel length and angle measurements.

• Measurements and tools that specifically support pre-procedural planning: manual and automatic ring marker placement for specific anatomical locations, length measurements of the longest and shortest curve along the aortic lumen contour, angle measurements of aortic branches in clock position style, saving viewing angles in C-arm notation, and configurable templated

• Saving and export: saving and export of batch series and customizable reports.

This summarization is based on the provided 510(k) clearance letter for Philips Medical Systems' AV Vascular device.

Acceptance Criteria and Device Performance for Aorto-iliac Outer Wall Segmentation

The reported device performance for all primary and secondary metrics meets the predefined acceptance criteria.

Study Details for Aorto-iliac Outer Wall Segmentation Validation

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

   • Sample Size: 80 patients
   • Data Provenance: Retrospectively collected from 7 clinical sites in the US, 3 European hospitals, and one hospital in Asia.
   • Independence from Training Data: All performance testing datasets were acquired from clinical sites distinct from those which provided the algorithm training data. The algorithm developers had no access to the testing data, ensuring complete independence.
   • Patient Characteristics: At least 80% of patients had thoracic and/or abdominal aortic diseases and/or iliac artery diseases (e.g., thoracic/abdominal aortic aneurysm, ectasia, dissection, and stenosis). At least 20% had been treated with stents.
   • Demographics:
     • Geographics: North America: 58 (72.5%), Europe: 3 (3.75%), Asia: 19 (23.75%)
     • Sex: Male: 59 (73.75%), Female: 21 (26.25%)
     • Age (years): 21-50: 2 (2.50%), 51-70: 31 (38.75%), >71: 45 (56.25%), Not available: 2 (2.5%)

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

   • Number of Experts: Three
   • Qualifications: US-board certified radiologists.

3. Adjudication Method for the Test Set:

   • The three US-board certified radiologists independently performed manual contouring of the outer wall along the aorta and iliac arteries on cross-sectional planes for each CT angiographic image.
   • After quality control, these three aortic and iliac arterial outer wall contours were averaged to serve as the reference standard contour. This can be considered a form of consensus/averaging after independent readings.

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

   • The provided document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to measure human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm compared to an expert-derived ground truth.

5. Standalone (Algorithm Only Without Human-in-the-Loop Performance):

   • Yes, the performance data provided specifically describes the standalone performance of the AI-based algorithm for aorto-iliac outer wall segmentation. The algorithm's output was compared directly against the reference standard without human intervention in the segmentation process.

6. Type of Ground Truth Used:

   • Expert Consensus/Averaging: The ground truth was established by averaging the independent manual contouring performed by three US-board certified radiologists.

7. Sample Size for the Training Set:

   • The document states that the testing data were independent of the training data and that developers had no access to the testing data. However, the exact sample size for the training set is not specified in the provided text.

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

   • The document implies that training data were used, but it does not describe how the ground truth for the training set was established. It only ensures that the testing data did not come from the same clinical sites as the training data and that algorithm developers had no access to the testing data.

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AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

AI-Rad Companion Brain MR provides the following functionalities:
• Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
• Quantitative comparison of each brain structure with normative data from a healthy population
• Presentation of results for reporting that includes all numerical values as well as visualization of these results

AI-Rad Companion Brain MR runs two distinct and independent algorithms for Brain Morphometry analysis and White Matter Hyperintensities (WMH) segmentation, respectively. In overall, comprises four main algorithmic features:

• Brain Morphometry
• Brain Morphometry follow-up
• White Matter Hyperintensities (WMH)
• White Matter Hyperintensities (WMH) follow-up

The feature for Brain Morphometry is available since the first version of the device (VA2x), while segmentation of White Matter Hyperintensities was added since VA4x and the follow-up analysis for both is available since VA5x. The brain morphometry and brain morphometry follow-up feature have not been modified and remain identical to previous VA5x mainline version.

AI-Rad Companion Brain MR VA60 is an enhancement to the predicate, AI-Rad Companion Brain MR VA50 (K232305). Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. The detected WMH can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

Here's a structured overview of the acceptance criteria and study details for the AI-Rad Companion Brain MR, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

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

   • White Matter Hyperintensities (WMH) Feature: 100 subjects (Multiple Sclerosis patients (MS), Alzheimer's patients (AD), cognitive impaired (CI), and healthy controls (HC)).
   • White Matter Hyperintensities (WMH) Follow-up Feature: 165 subjects (Multiple Sclerosis patients (MS) and Alzheimer's patients (AD)).
   • Data Provenance: Data acquired from Siemens, GE, and Philips scanners. Testing data had balanced distribution with respect to gender and age of the patient according to target patient population, and field strength (1.5T and 3T). This indicates a retrospective, multi-vendor, multi-national (implied by vendor diversity) dataset.

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

   • Number of Experts: Three radiologists.
   • Qualifications: Not explicitly stated beyond "radiologists." It is not specified if they are board-certified, or their years of experience.

3. Adjudication Method for the Test Set:

   • For each dataset, three sets of ground truth annotations were created manually.
   • Each set was annotated by a disjoint group consisting of an annotator, a reviewer, and a clinical expert.
   • The clinical expert was randomly assigned per case to minimize annotation bias.
   • The clinical expert reviewed and corrected the initial annotation of the changed WMH areas according to a specified annotation protocol. Significant corrections led to re-communication with the annotator and re-review.
   • This suggests a 3+1 Adjudication process, where three initial annotations are reviewed by a clinical expert.

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

   • No, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not done. The study focuses on the standalone performance of the AI algorithm against expert ground truth.

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

   • Yes, a standalone performance study was done. The "Accuracy was validated by comparing the results of the device to manual annotated ground truth from three radiologists." This evaluates the algorithm's performance directly.

6. The Type of Ground Truth Used:

   • Expert Consensus / Manual Annotation: The ground truth for both WMH and WMH follow-up features was established through "manual annotated ground truth from three radiologists" and involved a "standard annotation process" with annotators, reviewers, and clinical experts.

7. The Sample Size for the Training Set:

   • The document states that the "training data used for the fine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm." However, the specific sample size for the training set is not provided in the given text.

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

   • The document implies that the WMH follow-up algorithm "does not include any machine learning/ deep learning component," suggesting a rule-based or conventional image processing algorithm. Therefore, "training" might refer to parameter tuning rather than machine learning model training.
   • For the "fine-tuning the hyper parameters of WMH follow-up algorithm," the ground truth establishment method for this training data is not explicitly detailed in the provided text. It only states that this data was "independent of the data used to test" the algorithm.

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