Search Results

Indications for use vary depending on the specific product and its intended application. These products are designed for use in medical or laboratory settings by trained professionals. Depending on the device, intended uses may include:

• Diagnostic purposes: Analyzing biological samples (e.g., blood, urine, tissue) to identify diseases, conditions, or other health markers. This can include detecting infections, monitoring chronic illnesses, or screening for genetic predispositions.
• Therapeutic procedures: Assisting in or performing medical interventions, such as administering medications, delivering fluids, or providing respiratory support.
• Research and development: Used in laboratory experiments and studies to investigate biological processes, test new drugs, or develop new medical technologies.
• Monitoring physiological parameters: Measuring heart rate, blood pressure, oxygen saturation, or other vital signs.
• Sample collection and preparation: Gathering, processing, and storing biological samples for further analysis.

Specific indications for use are provided in the product's labeling, instructions for use (IFU), or accompanying documentation. Users should always refer to the manufacturer's provided information for the most accurate and complete indications.

{{overview}}

The FDA Clearance Letter for the Swoop® Portable MR Imaging® System (V2) provides details on the acceptance criteria and the studies conducted to demonstrate the device meets these criteria, particularly focusing on the "Advanced Reconstruction" feature which likely incorporates deep learning for image quality optimization.

Here's a breakdown of the requested information:

1. Acceptance Criteria and Reported Device Performance

The core performance of the device's "Advanced Reconstruction" was evaluated through three studies: Performance Analysis, Contrast-to-Noise Ratio (CNR) Validation, and Advanced Reconstruction Image Validation.

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

The document describes three distinct test sets for different validation studies.

• Performance Analysis (Robustness, Stability, Generalizability):
  • Sample Size:
    • T1, T2, FLAIR group: 40 patients, 111 images.
    • DWI group: 29 patients, 94 images.
  • Data Provenance: Not explicitly stated regarding country of origin. The test set was "entirely independent from the dataset used for model training." The "Equipment Type" is listed as "Swoop v2" (with

Ask a specific question about this device

The Informed Vital Core Application (IVC App) is intended for non-invasive spot measurement of pulse and respiration rate of adult patients in home use, hospitals, clinics, and long-term care settings.

The Informed Vital Core Application is indicated for use on adults 22 years of age or older who do not require critical care or continuous vital signs monitoring.

Informed Vital Core is not intended to independently direct therapy.

The IVC App is a Software as a Medical Device (SaMD) progressive web application that utilizes existing optical camera technology embedded in a smart-phone, tablet, laptop, or desktop computer to estimate an individual's vital signs including pulse rate (PR) and respiration rate (RR) developed by Mindset Medical. The IVC App software algorithms will provide spot checks for PR and RR of the individual. The IVC App software uses proprietary software algorithms to extract a raw video signal through remote plethysmography (rPPG) by detecting subtle color variations in the microvasculature around a patient's face that occur with each cardiac cycle due to changes in blood volume to measure PR. Additionally, this system utilizes software for optical camera-based measurement of respiration rate. The non-contact, periodic, spot measurement of respiration rate is taken when the subject is at rest based on shoulder movement.

Here's a breakdown of the acceptance criteria and the study proving the Informed Vital Core Application (IVC App) meets them, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance for Informed Vital Core Application (IVC App)

The IVC App (v2.0.0.2.0.0) is intended for non-invasive spot measurement of pulse and respiration rate in adult patients. The performance claims for the device are presented in the following table.

1. Table of Acceptance Criteria and Reported Device Performance

Note: While acceptance criteria for Pulse Rate are stated in the comparative table, the clinical study summary specifically focuses on the respiration rate's performance. It is implied that the pulse rate performance was either established through other means (e.g., non-clinical testing, equivalence to predicate) or not the primary focus of this specific clinical study summary.

2. Sample Size and Data Provenance

• Test Set Sample Size: The clinical study enrolled 65 subjects (35 healthy patients and 29 patients with comorbidities).
• Data Provenance: The study was conducted at six study sites located in the United States. The data origin is prospective, as described by the "clinical study was conducted" and "study recruited subjects" phrasing.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: The text states, "The clinicians counting the raw EtCO2 waveform were blinded to the results from the test device." It does not specify the exact number of clinicians used, but explicitly refers to them as "clinicians."
• Qualifications of Experts: The specific qualifications (e.g., "radiologist with 10 years of experience") are not explicitly stated in the provided text. However, they are described as "clinicians," implying medical professionals capable of accurately counting end-tidal CO2 waveforms.

4. Adjudication Method for the Test Set

• Adjudication Method: The ground truth for respiration rate was established by "blinded, manually-counted end-tidal CO2 (EtCO2) with an FDA-cleared capnography device." The term "blinded" implies efforts to prevent bias, and "manually-counted" suggests human assessment. However, there is no explicit mention of an adjudication process like 2+1 or 3+1 if there were multiple clinicians involved in counting the waveforms for a single case. It only states they were "clinicians counting," not how disagreements (if any) were resolved or if multiple clinicians independently counted each waveform.

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

• MRMC Study Done?: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted to assess how human readers improve with AI vs. without AI assistance. The study described is a single-arm cohort study evaluating the device's accuracy against a gold standard for respiration rate measurement, not a human-in-the-loop performance study.

6. Standalone (Algorithm Only) Performance

• Standalone Performance Done?: Yes, the clinical study primarily assesses the standalone performance of the IVC App's algorithms for measuring respiration rate. It compares the device's output directly to the gold standard (blinded, manually-counted EtCO2). The device is a "Software as a Medical Device (SaMD)" that outputs vital signs measurements.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for respiration rate was established using expert consensus/measurement from an objective, FDA-cleared device. Specifically, it was "blinded, manually-counted end-tidal CO2 (EtCO2) with an FDA-cleared capnography device." This combination leverages both objective physiological signals (EtCO2 waveform) and expert interpretation/counting of those signals.

8. Sample Size for the Training Set

• Training Set Sample Size: The provided document does not specify the sample size or details about the training set used for the IVC App's algorithm development. The "Summary of Clinical Testing" section pertains to the validation/test study.

9. How Ground Truth for Training Set Was Established

• Ground Truth for Training Set Established: The document does not specify how the ground truth for the training set was established. This information is typically part of the device's development and validation process but is not included in the provided 510(k) summary for this clearance.

Ask a specific question about this device

The Visualase V2 ™ MRI-Guided Laser Ablation System is a neurosurgical tool and is indicated for use to ablate, necrotize, or coagulate intracranial soft tissue including brain structures (for example, brain tumor, radiation necrosis, and epileptic foci as identified by non-invasive and invasive neurodiagnostic testing, including imaging) through interstitial irradiation or thermal therapy in pediatrics and adults with 980 nm lasers. The intended patients are adults and pediatric patients from the age of 2 years and older.

The Visualase MRI-Guided Laser Ablation System comprises hardware and software components used in combination with three MR-compatible (conditional), sterile, single-use, saline-cooled laser applicators with proprietary diffusing tips that deliver controlled energy to the tissue of interest. The system consists of:

• a diode laser (energy source)
• a coolant pump to circulate saline through the laser application
• Visualase workstation which interfaces with MRI scanner's host computer
• Visualase software which provides the system's ability to visualize and monitor relative changes in tissue temperature during ablation procedures, set temperature limits and control the laser output; one monitors to display all system imaging and laser ablation via a graphical user interface and peripherals for interconnections

The provided FDA 510(k) clearance letter for the Visualase V2 MRI-Guided Laser Ablation System does not contain the detailed information necessary to fully address all aspects of the request. Specifically, the document focuses on regulatory compliance, substantial equivalence to predicate devices, and general testing summaries (software V&V, system V&V, electrical safety). It does not include specific acceptance criteria with performance metrics, details of a clinical study (like sample sizes, ground truth establishment, expert qualifications, or MRMC studies), or direct data proving the device met specific performance criteria.

The letter explicitly states: "A clinical trial was not required to establish substantial equivalence. Clinical evidence provided in a literature summary format supports the safe use of the Visualase V2 System in the intended patient population." This indicates that a prospective clinical performance study, often associated with detailed acceptance criteria and reported performance, was not the primary method for demonstrating substantial equivalence for this particular submission.

Therefore, many sections of your request cannot be fulfilled based on the provided document. I will fill in the information that is present and explicitly state where information is not available.

Acceptance Criteria and Device Performance for Visualase V2 MRI-Guided Laser Ablation System

Based on the provided FDA 510(k) clearance letter (K250307), the device's acceptance criteria and proven performance are primarily demonstrated through verification and validation activities for its software and system, and compliance with electrical safety standards. A formal clinical trial with quantitative performance metrics against specific acceptance criteria (e.g., sensitivity, specificity, accuracy) was not required for this submission to establish substantial equivalence, but rather clinical evidence was provided via a literature summary.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) for the Visualase V2 System as described in the document, performance acceptance criteria are focused on safety, functionality, and equivalence to predicate devices, rather than clinical efficacy metrics typically found in AI/diagnostic device submissions.

2. Sample Size for the Test Set and Data Provenance

The document explicitly states: "A clinical trial was not required to establish substantial equivalence. Clinical evidence provided in a literature summary format supports the safe use of the Visualase V2 System in the intended patient population."

Therefore, no specific "test set" sample size for a clinical performance study is reported in this document. The "testing summary" refers to internal verification and validation against design controls and standards, not a clinical data set for performance evaluation of an AI algorithm.

Data Provenance: Not applicable for a clinical test set in this context, as a clinical performance study was not the basis for substantial equivalence for this upgrade. The clinical evidence was a literature summary.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable, as a specific clinical test set for performance evaluation (e.g., for an AI algorithm's diagnostic accuracy which would require ground truth labeling) was not conducted as part of this 510(k) as described. The "ground truth" for the device's functionality and safety was established via engineering verification, validation, and regulatory compliance.

4. Adjudication Method for the Test Set

Not applicable, as a clinical test set requiring adjudication was not reported as part of this 510(k) as described.

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

No, a MRMC comparative effectiveness study was not reported in this 510(k) clearance letter. The submission focused on establishing substantial equivalence through other means (software/system V&V, safety testing, literature review) rather than demonstrating AI assistance performance improvement.

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

The Visualase V2 System is a medical device system that includes hardware and software for MRI-guided laser ablation, with software providing monitoring and control capabilities related to temperature and thermal damage estimation. It is not an AI diagnostic algorithm for which a standalone performance evaluation (e.g., AUC, sensitivity/specificity) would typically be required or reported in this format. The software's performance is intrinsically linked to the system's function and user interaction.

Therefore, a "standalone algorithm only" performance study in the sense of a diagnostic AI product is not applicable and not reported.

7. The Type of Ground Truth Used

For the system's functional and safety validation, the "ground truth" would be engineering specifications, design requirements, and established medical and electrical safety standards (e.g., IEC standards, 21 CFR 820.30 Design Controls).

For any inferred clinical claims from the "literature summary," the ground truth would originate from the clinical data reported in the summarized peer-reviewed literature, which could involve histological confirmation, long-term patient outcomes, or expert clinical diagnosis, but these details are not provided in the 510(k) letter itself.

8. The Sample Size for the Training Set

Not applicable. The document describes a medical device system update, not an AI algorithm developed using a specific training dataset in the machine learning sense. The "training" for the system's software would derive from its design and programming, verified through the V&V processes.

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

Not applicable, as this is not an AI algorithm developed through data-driven training in the machine learning sense. The "ground truth" for the device's design and engineering would be based on scientific and engineering principles, preclinical testing, and existing medical knowledge, as per design control procedures.

Ask a specific question about this device

The Single Use Cannula V PR-V223Q/V414Q/V416Q/V418Q/V420Q/V427Q/V434Q/V435Q are intended to be used to inject contrast medium in the biliary or pancreatic duct in combination with an endoscope.

The Single Use 2-Lumen Cannula V PR-V614M is intended to be used to inject contrast medium in the biliary or pancreatic duct in combination with an endoscope.

The Single Use Cannula V and the Single Use 2-Lumen Cannula V PR Series is comprised of nine (9) sterile, single-use, cannulas designed to inject contrast medium in the biliary or pancreatic duct when used in conjunction with a compatible endoscope.

Each device has two sections: the handle (proximal portion) and the insertion portion. The insertion portion is introduced into the biliary or pancreatic ducts through an endoscope. The distal end of the insertion portion is designed for smooth cannulation of the papilla of Vater or the minor papilla. All models are visible under fluoroscopy and feature a distal marking system.

The Single Use Cannula V and the Single Use 2-Lumen Cannula V PR Series models are to be used with compatible endoscopes.

The provided 510(k) Premarket Notification document describes a medical device, the "Single Use Cannula V and the Single Use 2-Lumen Cannula V PR Series," and its comparison to a predicate device for demonstrating substantial equivalence.

However, the document does not contain information about acceptance criteria or a study proving the device meets specific acceptance criteria in the context of device performance as typically expected for software-enabled devices or those with diagnostic capabilities.

This submission is for a physical medical device (cannulas) and focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance testing of its physical properties and biocompatibility. Therefore, many of the requested categories (like sample size for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance, and ground truth types) are not applicable to the information provided.

Based on the document, here's what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

The document outlines performance data that was provided to demonstrate substantial equivalence, rather than specific and quantitative acceptance criteria with reported numerical device performance against those criteria. The "Analysis" column in the comparison table broadly states "Substantially equivalent" or "Identical," but doesn't provide the detailed numbers that would typically be associated with acceptance criteria for a diagnostic or algorithmic device.

Acceptance Criteria and Reported Performance (General statement from the document):

The document states: "Non-clinical testing demonstrates that the slight differences in device design do not alter the safety, efficacy, or performance of the subject devices when compared to the predicate devices." and "The non-clinical data demonstrate that the subject device is as safe, as effective, and performs as well as or better than the identified predicate device." This is a qualitative conclusion of meeting equivalence rather than presenting specific numerical acceptance criteria.

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

• Sample Size for Test Set: Not specified in the provided document. The performance data refers to various non-clinical tests, and the sample size for these individual tests (e.g., number of cannulas tested for insertion force) is not detailed.
• Data Provenance: Not applicable in the context of clinical data. The tests are non-clinical (laboratory/bench testing).

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

• Not applicable. Ground truth as typically understood for diagnostic performance (e.g., disease presence/absence) is not relevant for these non-clinical, physical device performance tests. "Human Factors Testing" is mentioned, which would involve experts, but the number and qualifications are not provided, nor is it the type of "ground truth" establishment usually refers to in the context of diagnostic AI.

4. Adjudication method for the test set:

• Not applicable. This is typically used for clinical study endpoints or image interpretation, not for physical performance tests of a cannula.

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 physical cannula, not an AI or diagnostic imaging system.

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

• Not applicable. This is not an algorithm or AI device.

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

• Not applicable in the conventional sense. The "ground truth" for the non-clinical tests would be the physical properties and functional performance measured against predefined specifications or predicate device performance.

8. The sample size for the training set:

• Not applicable for a physical device where "training set" doesn't apply in the context of machine learning.

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

• Not applicable for a physical device.

Ask a specific question about this device

UltraExtend NX CUW-U001S Ultrasound Image Analysis Program is designed to allow the user to observe images and perform analysis based on examination data acquired using the following diagnostic ultrasound systems; TUS-AI900, TUS-AI800, and TUS-AI700.

This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The UltraExtend NX, V2.0 is designed to allow the user to observe images and perform analysis based on examination data acquired using the Aplio i900/i800/i700 diagnostic ultrasound systems. RAW only or data saved in Image + RAW should be used for UltraExtend NX.

The FDA 510(k) clearance letter for the UltraExtend NX CUW-U001S V2.0 Ultrasound Image Analysis Program indicates that the device has integrated AI/ML-based functionality (2D Wall Motion Tracking with Full-assist function for left ventricle (LV) and Auto EF with Full-assist function for LV) that was previously cleared with a reference device (K223017). The submission states that "these studies utilized a representative subset of the clinical data acquired for the original performance testing of these features; additionally these studies applied the same acceptance criteria to evaluate the performance of these features compared to the same ground truth as utilized in the original performance evaluation of these features with the reference device."

Unfortunately, the provided text does not contain the specific acceptance criteria or detailed results of the performance testing for these AI/ML features. It only states that the features "perform as intended when integrated into the subject device, and with substantial equivalence as with the reference device."

Therefore, I cannot provide a table of acceptance criteria and reported device performance or many of the specific details requested in your prompt based solely on the provided document. The document refers to the original performance testing of the reference device (K223017) for these details.

However, I can extract and infer information about the study design to the extent possible:

Here's what can be inferred from the provided text, and what cannot be determined:

Acceptance Criteria and Device Performance

• The document states that the same acceptance criteria as the original performance testing for the reference device (K223017) were applied.
• Cannot Determine: The specific numerical acceptance criteria (e.g., specific accuracy, sensitivity, specificity thresholds) or the reported device performance metrics (e.g., actual accuracy, sensitivity, specificity values) are not provided in this document.

Study Information

Summary of missing information:

To fully answer your prompt, you would need to consult the original 510(k) submission for the reference device (K223017), Aplio i900/i800/i700 Diagnostic Ultrasound System, Software Version 7.0, as that is where the detailed performance data, acceptance criteria, and ground truth establishment methodology for the AI/ML features would have been submitted and evaluated by the FDA. The current document (K250328) focuses on demonstrating that these already cleared AI/ML features maintain their performance when integrated into a new workstation.

Ask a specific question about this device

The cobas liat SARS-CoV-2 & Influenza A/B v2 nucleic acid test is an automated rapid multiplex real-time reverse transcription polymerase chain reaction (RT-PCR) test intended for the simultaneous qualitative detection and differentiation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus and influenza B virus nucleic acids in anterior nasal (nasal) and nasopharyngeal swab specimens from individuals exhibiting signs and symptoms of respiratory tract infection. Clinical signs and symptoms of respiratory tract infection due to SARS-CoV-2 and influenza can be similar. This test is intended to aid in the differential diagnosis of SARS-CoV-2, influenza A and influenza B infections in humans and is not intended to detect influenza C virus infections.

Nucleic acids from the viral organisms identified by this test are generally detectable in nasopharyngeal and nasal swab specimens during the acute phase of infection. The detection and identification of specific viral nucleic acids from individuals exhibiting signs and symptoms of respiratory tract infection are indicative of the presence of the identified virus, and aid in diagnosis if used in conjunction with other clinical and epidemiological information and laboratory findings.

The results of this test 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. The organism(s) detected by the cobas liat SARS-CoV-2 & Influenza A/B v2 nucleic acid test may not be the definite cause of disease. Negative results do not preclude SARS-CoV-2, influenza A virus or influenza B virus infections.

The cobas liat SARS-CoV-2 & Influenza A/B v2 nucleic acid test is performed on the cobas liat analyzer which automates and integrates sample purification, nucleic acid amplification, and detection of the target sequence in biological samples using real-time PCR assays. The assay targets both the ORF1 a/b non-structural region and membrane protein gene that are unique to SARS-CoV-2, a well-conserved region of the matrix gene of influenza A (Flu A target), and the nonstructural protein 1 (NS1) gene of influenza B (Flu B target). An Internal Control (IC) is included to control for adequate processing of the target virus through all steps of the assay process and to monitor the presence of inhibitors in the RT-PCR processes.

This document describes the validation study for the cobas liat SARS-CoV-2 & Influenza A/B v2 nucleic acid test.

Here's an analysis of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a diagnostic test, the primary acceptance criteria revolve around analytical and clinical performance metrics like Limit of Detection, Inclusivity, Cross-Reactivity, Reproducibility, and Clinical Agreement (Positive Percent Agreement and Negative Percent Agreement). The document doesn't explicitly state "acceptance criteria" values in a separate table, but these are implied by the performance metrics reported and the general standards for diagnostic device clearance. I will extract the reported device performance from the provided text.

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

• Prospective Clinical Study:

  • Sample Size: 1729 symptomatic subjects enrolled.
    • 1705 evaluable NPS specimens for analysis (19 non-evaluable due to missing/invalid results, 5 due to handling).
    • 1706 evaluable ANS specimens for SARS-CoV-2 and Influenza B analysis (22 non-evaluable due to missing/invalid results, 1 due to handling).
    • 1704 evaluable ANS specimens for Influenza A analysis (2 additional found inconclusive for comparator).
  • Data Provenance: Prospective, collected between September 2023 and March 2024 at 14 point-of-care testing sites in the United States (US).

• Retrospective Clinical Study (Influenza B Supplement):

  • Sample Size: 223 archived NPS specimens and 206 archived ANS specimens (total 429).
    • One NPS sample pre-characterized as positive for influenza B was non-evaluable.
  • Data Provenance: Retrospective, frozen archived (Category III) specimens collected between 2019 and 2023. Distributed to 6 sites for testing.

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

The document does not mention the use of experts to establish ground truth for the clinical test sets. For molecular diagnostic tests like this, the ground truth is typically established by comparing the investigational device's results against a highly accurate, accepted comparator method (another FDA-cleared Nucleic Acid Amplification Test specific for the target analytes). The expertise lies in the development and validation of these comparator methods, not in individual expert review of each sample for ground truth in this context.

4. Adjudication Method for the Test Set

The document describes discrepant result analysis for both prospective and retrospective clinical studies.

• For the prospective study, "discrepant NAAT results" are detailed for SARS-CoV-2 (NPS and ANS), Influenza A (NPS and ANS), and Influenza B (NPS and ANS).
• For the retrospective study, discrepant NAAT results are detailed for Influenza B (NPS and ANS).

The method appears to be:

• The cobas liat test result is compared to the FDA-cleared comparator NAAT result.
• When there's a discrepancy (e.g., cobas liat positive, comparator negative), it explicitly states how many were "positive" and "negative" upon further investigation or re-evaluation (e.g., with "discrepant NAAT results").
  • For example: "Of 12 specimens negative on cobas® liat and positive on the comparator, 8 were positive and 4 were negative." This implies some form of re-testing or deeper analysis (not specified as "adjudication by experts" but rather "discrepant NAAT results"). It's more of a re-confirmation of the comparator or a third method, rather than a human expert consensus process. Such re-evaluation often involves re-testing using the comparator or a reference method.

Therefore, while there's no "2+1" or "3+1" expert adjudication method described as would be seen in imaging studies, there is a discrepant resolution process based on further NAAT results. It's not "none" in the sense that discrepancies are just reported without follow-up; rather, they are further investigated using additional NAAT results to re-confirm the original comparator status if possible.

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. This is a standalone diagnostic test (RT-PCR), not an AI-assisted imaging device or a test that involves human "readers" interpreting results. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

Yes, implicitly. This is a fully automated RT-PCR test run on the cobas liat analyzer. The performance metrics (LoD, inclusivity, cross-reactivity, reproducibility, and clinical agreement) are measures of the device's performance on its own against established ground truth (comparator NAAT). While humans load samples and interpret the final digital result (positive/negative), the core detection and differentiation is algorithm-driven within the instrument, making its performance essentially "standalone" in the context of diagnostic accuracy.

7. The Type of Ground Truth Used

• Clinical Performance (Prospective and Retrospective): The ground truth for clinical sample testing was established by comparing the cobas liat results against an FDA-cleared Nucleic Acid Amplification Test (NAAT), which serves as the reference or "ground truth" method for molecular diagnostic assays. The document explicitly states: "PPA and NPA were determined by comparing the results of cobas® liat SARS-CoV-2 & Influenza A/B v2 to the results of an FDA-cleared Nucleic Acid Amplification Test (NAAT)." and "The comparator method was an acceptable FDA-cleared molecular assay."
• Analytical Studies (LoD, Inclusivity, Cross-Reactivity, Interference, Reproducibility): Ground truth was established by preparing precisely known concentrations of viral material (cultured or inactivated viruses) or specific microorganisms in controlled laboratory settings. For these studies, the "ground truth" is meticulously prepared and verified laboratory standards.

8. The Sample Size for the Training Set

The document does not specify a separate "training set" sample size. For an RT-PCR diagnostic platform, the "training" involves the fundamental biochemical and optical engineering, and the optimization of assay (reagent) design to achieve sensitivity and specificity. This is distinct from machine learning models that often require large, labeled datasets for "training." The analytical and clinical validation studies described here are verification and validation (V&V) studies, akin to a "test set" to prove the device's performance against its design specifications and clinical utility.

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

Since no explicit "training set" for a machine learning algorithm is mentioned (as this is a molecular diagnostic test), this question is not directly applicable. However, the ground truth for assay development and optimization (which can be considered analogous to "training" in a broader sense of device development) would have been established through extensive laboratory work using:

• Highly characterized viral cultures or purified nucleic acids: Used to define target sequences, optimize primer/probe design, and determine initial analytical sensitivity.
• Spiked samples: Adding known quantities of targets or interferents to negative clinical matrices to mimic real-world conditions during early development.
• Early clinical samples: Used to refine assay performance and resolve initial issues prior to formal validation studies.

These processes ensure the assay correctly identifies the target nucleic acids.

Ask a specific question about this device

MySegmenter (v2.0.0) is a medical images segmentation tool that converts CT and MRI scans into 3D anatomical models. The models are intended for surgical planning or educational purposes but are not to be used in the operating room.

The software must be used under the supervision of qualified medical professionals. The 3D models and replicas are to be utilized solely for pre-operational planning in orthopedic and craniomaxillofacial cases, and for educational purposes; they must not be used in the operating room. The MR images have not been tested for craniomaxillofacial applications.

MySegmenter (v2.0.0) is a software platform for visualizing medical images in 3D mesh format. processes images by analyzing the luminance of individual pixels to segment and highlight the regions of interest (specific anatomical areas). MySegmenter supports editing, modification and manipulation of segmented areas using various segmentation tools. The software features include:

• Importing medical images in DICOM and other formats.
• Viewing and navigating through DICOM images.
• Segmenting selected regions using generic tools (segments, island effects, Boolean operations, etc.)
• Editing segments with multiple slice edits, fast marching, watershed effects, etc.
• Generating editable 3D STL files for different applications.
• Converting the segment to mesh models.
• Exporting the segmented models in STL format suitable for further manipulation and manufacturing.

Here's a breakdown of the acceptance criteria and study information for MySegmenter (v2.0.0), based on the provided FDA 510(k) clearance letter and summary:

Acceptance Criteria and Reported Device Performance

Study Details

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

Test Set - CT and MRI Accuracy Studies:

• Sample Size: 30 datasets (15 CT, 15 MRI)
• Data Provenance: Indian origin, evenly distributed between adult male and female subjects.
• Retrospective/Prospective: Not explicitly stated, but typically such studies utilize retrospective data sets.

Test Set - 3D Printing Accuracy:

• Sample Size: 20 datasets (15 CT, 5 MRI)
• Data Provenance: Indian origin, evenly distributed between adult male and female subjects.
• Retrospective/Prospective: Not explicitly stated, but typically such studies utilize retrospective data sets.

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

• Number of Experts: Not specified. The document states "experts in the field of medical imaging."
• Qualifications of Experts: "Experts in the field of medical imaging." Specific experience levels or specializations (e.g., radiologist with X years of experience) are not provided.

4. Adjudication method for the test set

• Adjudication Method: Inter-expert agreement was evaluated using Intraclass Correlation Coefficient (ICC) scores to ensure absolute agreement for the ground truth models created by experts. This implies that multiple experts reviewed and agreed upon the reference models.

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

• MRMC Study: No, an MRMC comparative effectiveness study involving human readers and AI assistance was not explicitly mentioned or described. The studies focused on the accuracy of the device's output (3D models and prints) compared to a predicate device and expert-created ground truth.

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

• Standalone Performance: Yes, the accuracy studies (CT and MRI Accuracy Studies, and 3D Printing Accuracy) describe the performance of the MySegmenter (v2.0.0) algorithm in generating 3D models and prints, which are then compared to ground truth or predicate device outputs. While manual editing is mentioned as influencing performance for DICE/Jaccard, the core evaluation is on the software's ability to produce these models. The device description also highlights "Manual and Semiautomatic tools for segmentation," suggesting that the algorithm performs segmentation which users can then refine.

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

• Type of Ground Truth: For the CT and MRI Accuracy Studies, the ground truth was expert-created reference models using the predicate device. This is a form of expert consensus or expert-derived reference. For the 3D Printing Accuracy, the digital models produced by the predicate device served as the comparison point for the 3D printed physical models.

8. The sample size for the training set

• Training Set Sample Size: Not provided in the document. The document only details the test set sizes for validation.

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

• Training Set Ground Truth Establishment: Not provided in the document, as the training set details are not mentioned.

Ask a specific question about this device

The cobas® liat SARS-CoV-2 v2 nucleic acid test is an automated real-time reverse transcription polymerase chain reaction (RT-PCR) test intended for the qualitative detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acids in anterior nasal (nasal) and nasopharyngeal swab specimens collected from individuals exhibiting signs and symptoms of respiratory tract infection (i.e., symptomatic). Additionally, this test is intended to be used with nasal and nasopharyngeal swab specimens collected from individuals without signs and symptoms of COVID-19 (i.e., asymptomatic).

The cobas® liat SARS-CoV-2 v2 nucleic acid test is intended for use as an aid in the diagnosis of COVID-19 if used in conjunction with other clinical and epidemiological information and laboratory findings. SARS-CoV-2 RNA is generally detectable in nasal swab and nasopharyngeal swab specimens during the acute phase of infection.

Positive results are indicative of the presence of SARS-CoV-2 RNA. Positive results do not rule out co-infection with other microorganisms. Negative results do not preclude SARS-CoV-2 infection. Negative results must be combined with clinical observations, patient history, and epidemiological information. The results of this test should not be used as the sole basis for diagnosis, treatment, or other patient management decisions.

A negative result from an asymptomatic individual is presumptive. Additionally, a negative result obtained with a nasal or nasopharyngeal swab collected from an asymptomatic individual should be followed up by testing at least twice over three days with at least 48 hours between tests.

The cobas® liat SARS-CoV-2 v2 nucleic acid test is performed on the cobas® liat analyzer which automates and integrates sample purification, nucleic acid amplification, and detection of the target sequence in biological samples using real-time PCR assays. The assay targets both the ORF1 a/b non-structural region and membrane protein gene that are unique to SARS-CoV-2. An Internal Control (IC) is included to control for adequate processing of the target virus through all steps of the assay process and to monitor the presence of inhibitors in the RT-PCR processes.

The provided text is a 510(k) Clearance Letter for a medical device which does not include information about AI/ML models. Therefore, it's not possible to extract the information you requested about Acceptance Criteria and a study proving an AI/ML device meets those criteria.

The device described, the "cobas liat SARS-CoV-2 v2 nucleic acid test," is an in vitro diagnostic (IVD) device based on real-time RT-PCR technology. It directly detects viral targets and its performance is evaluated through analytical and clinical studies common for IVDs, not against AI/ML performance metrics like sensitivity, specificity, MRMC studies, or multi-reader reviews.

Here's a breakdown of why your requested information isn't present in this document:

• No AI/ML Component: The document describes a traditional RT-PCR assay. There is no mention of algorithms, machine learning, deep learning, or any AI component.
• Performance Metrics Differ: The performance metrics provided (Limit of Detection, Inclusivity, Cross-reactivity, Reproducibility, Positive Percent Agreement, Negative Percent Agreement) are standard for IVD assays. They are not analogous to metrics used for evaluating AI/ML models (e.g., AUC, F1-score, accuracy in image classification, or diagnostic improvement from AI-assistance).
• No Human Reader Interaction: Since it's an automated lab test, there's no "human reader" (like a radiologist) involved in interpreting the device's output in a way that an AI would assist. The output is qualitative (Detected/Not Detected).
• No Ground Truth Experts in the AI Sense: Ground truth for this IVD is established by a "comparator" (another FDA-cleared NAAT) and clinical/epidemiological information, not by multiple human experts reviewing AI outputs or images.
• No Training/Test Set Split for AI: The "test set" and "training set" concepts described in your request are fundamental to AI/ML model development and validation. For this IVD, there's a "clinical performance evaluation" using prospective and retrospective samples, which serves as the validation dataset, but it's not structured as a training/test split for an AI.

Therefore, I cannot provide the requested table and detailed points because the provided document does not pertain to an AI/ML device.

If you have a document describing an AI/ML medical device, please provide that, and I can attempt to extract the relevant information.

Ask a specific question about this device

Cleerly LABS is a web-based software application that is intended to be used by trained medical professionals as an interactive tool for viewing and analyzing cardiac computed tomography (CT) data for determining the presence and extent of coronary plaques (i.e. atherosclerosis) and stenosis in patients who underwent Coronary Computed Tomography (CCTA) for evaluation of CAD or suspected CAD. This software post processes CT images obtained using any Computed Tomography (CT) scanner. The software provides tools for the measurement and visualization of coronary arteries.

The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people who have been appropriately trained in the software's functions, capabilities and limitations. Users should be aware that certain views make use of interpolated data. This is data that is created by the software based on the original data set. Interpolated data may give the appearance of healthy tissue in situations where pathology that is near or smaller than the scanning resolution may be present.

Cleerly LABS is a post-processing web-based software application that enables trained medical professionals to analyze 2D/3D coronary images acquired from Coronary Computed Tomography Angiography (CCTA) scans. The software is a post-processing tool that aids in determining treatment paths for patients suspected of having coronary artery disease (CAD).

Cleerly LABS utilizes machine learning and simple rule-based mathematical calculation components which are performed on the backend of the software. The software applies deep learning methodology to identify high quality images, segment and label coronary arteries, and segment lumen and vessel walls. 2D and 3D images are presented to the user for review and manual editing. This segmentation is designed to improve efficiency for the user, and help shorten tedious, timeconsuming manual tasks.

The user is then able to edit the suggested segmentation as well as adjust plaque thresholds, demarcate stenosis, stents, and chronic total occlusions (CTOs) as well as select dominance and indicate coronary anomalies. Plaque, stenosis, and vessel measurements are output based on the combination of user-editable segmentation and user-placed stenosis, stent, and CTO markers. These outputs are mathematical calculations and are not machine-learning based.

Cleerly LABS provides a visualization of the Cleerly LABS analysis in the CORONARY Report. The CORONARY Report uses data previously acquired from the Cleerly LABS image analysis to generate a visually interactive and comprehensive report that details the atherosclerosis and stenosis findings of the patient. This report is not intended to be the final report (i.e., physician report) used in patient diagnosis and treatment. Cleerly Labs provides the ability to send the text report page of the CORONARY Report to the user's PACS system.

Cleerly LABS software does not perform any functions that could not be accomplished by a trained user with manual tracing methods or other commercially available software. Rather, it represents a more robust semiautomatic software intended to enhance the performance of time-intensive, potentially error-prone, manual tasks, thereby improving efficiency for medical professionals in the assessment of coronary artery disease (CAD).

The provided FDA 510(k) summary for Cleerly LABS (v2.0) indicates that no new clinical testing was conducted to demonstrate safety or effectiveness for this submission (K242338), as the non-clinical testing was deemed sufficient. The submission primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device, Cleerly LABS v2.0 (K202280), primarily due to modifications in product labeling, workflow, and minor technological enhancements, without changes to the underlying algorithms or mathematical calculations.

Therefore, the document does not contain details about specific acceptance criteria, device performance, sample sizes for test sets, data provenance, expert adjudication methods, MRMC studies, standalone performance, or ground truth establishment for a new clinical study. Instead, it refers to the sufficiency of previous non-clinical testing and substantial equivalence to the predicate device.

Given this, I will extract information related to the overall performance claims and testing mentioned, emphasizing that these refer to the previous evaluation or software testing for this specific submission, rather than a new clinical study.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a specific table of quantitative acceptance criteria and corresponding reported device performance metrics for a new clinical study. It states that "Results of testing re-confirmed that the software requirements fulfilled the pre-defined acceptance criteria." However, these specific criteria and results are not detailed in this summary.

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

Not applicable for a new clinical study in this submission. The document states, "No clinical testing was conducted to demonstrate safety or effectiveness as the device's non-clinical testing was sufficient to support the intended use of the device." For software evaluation, it states "multiple pre-production environments using simulated data and in production for release verification." No specific sample sizes for these internal software tests or data provenance are provided.

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

Not applicable for a new clinical study in this submission. Ground truth establishment for previous studies or internal validation is not detailed.

4. Adjudication Method for the Test Set

Not applicable for a new clinical study in this submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

No MRMC comparative effectiveness study was mentioned or performed for this submission. The device is described as "more robust semiautomatic software intended to enhance the performance of time-intensive, potentially error-prone, manual tasks, thereby improving efficiency for medical professionals." However, no specific effect size or improvement metrics are provided for human readers with or without AI assistance in this document.

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

The device is described as a "web-based software application that is intended to be used by trained medical professionals as an interactive tool" and "not intended to replace the skill and judgment of a qualified medical practitioner." It also mentions that "The software applies deep learning methodology to identify high quality images, segment and label coronary arteries, and segment lumen and vessel walls." While the core functions are supported by AI, the tool is semi-automatic with user review and editing capabilities. The document doesn't explicitly state if a standalone performance study (without human interaction) was performed for regulatory submission, but rather focuses on its role as an interactive tool for professionals.

7. The Type of Ground Truth Used

Not explicitly stated for the "software evaluation activities" mentioned. For the underlying algorithms (which were unchanged from the predicate), the document implies that expert review and manual editing are part of the process, suggesting expert consensus or reference standards may have been used in the original development.

8. The Sample Size for the Training Set

No information on the sample size for the training set is provided in this document.

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

No information on how the ground truth for the training set was established is provided in this document.

Ask a specific question about this device

The XBeam Software can be used for validating the monitor units or radiation dose to a point that has been calculated by hand or another treatment planning system for external beam radiation therapy. In addition, the XBeam Software can also be used as a primary means of calculating the monitor units or radiation dose to a point for external beam radiation treatments.

XBeam is only intended to be used with Xstrahl's superficial and orthovoltage radiotherapy and surface electronic brachytherapy systems. XBeam is intended to be used by authorized personnel trained in medical physics.

XBeam is a standalone dose calculation software for Xstrahl's medical devices include:

• Xstrahl 100, Xstrahl 150, Xstrahl 200, Xstrahl 300 (K962613)
• X80 RADiant Photoelectric Therapy System (K172080)
• . RADiant Aura (X80 RADiant Photoelectric Therapy System) (K230611)

XBeam's dose calculation algorithm can be used to determine the beam-on time or monitor units based on the applicator and filter selected for the specific device. The beam-on time / monitor units are calculated based on the percent dose depth (PDD) curve and the absolute dose output for the specified applicatorfilter combination. The software allows for calculating treatment parameters for single or two (parallel opposed) beams.

XBeam is intended to be used within a clinical environment where the patient is treated with Xstrahl's medical systems. XBeam is intended to be used by authorized personnel trained in medical physics. It is not intended to be used by patients or general public.

Here's an analysis of the provided text regarding the acceptance criteria and the study that proves the device meets those criteria:

The provided FDA 510(k) summary for the XBeam (v2) device focuses on demonstrating substantial equivalence to its predicate device, RADCalc (K193381), primarily through a comparison of intended use, technical characteristics, and a summary of non-clinical testing. While it mentions "acceptance criteria" through verification and validation activities, it does not explicitly define specific numerical acceptance criteria (e.g., "accuracy must be > 95%") for its performance when compared against ground truth.

Instead, the summary reports the results of the performance testing and concludes that they are acceptable, implying that these results meet implicit acceptance criteria for clinical equivalence and safety/effectiveness.

Given this, I will infer the implicit acceptance criterion based on the reported results.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document states: "Three hundred twenty-three (323) independent verification tests were executed." This refers to verification activities (system tests, module tests, etc.) rather than a specific test set of patient cases or dosimetric scenarios for performance evaluation against ground truth.

For the dosimetric accuracy validation:

• Sample size: Not explicitly stated as a number of distinct cases or patient datasets. It refers to comparing XBeam's output against two standard methods (hand calculations and RadCalc) and then comparing planned dose (presumably from XBeam) to delivered dose using physical measurements. The number of such comparisons or the range of parameters tested is not quantified.
• Data provenance: Not specified in terms of country of origin. The study appears to be a prospective validation of the software's dose calculation against established methods and physical measurements, rather than clinical retrospective or prospective patient data analysis.

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

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: The ground truth for dose calculation was established by "hand calculations" and the output of the predicate device "RadCalc (version: 7.3)." This implies that the 'experts' or processes involved in performing these hand calculations or configuring/using RadCalc would be "authorized personnel trained in medical physics" as stipulated in the device's indications for use.

4. Adjudication Method

Not applicable/specified. The validation involves direct comparison of numerical outputs (dose, monitor units) against established calculational methods and physical measurements, rather than assessment by multiple human reviewers requiring adjudication for a "ground truth" establishment in a subjective medical imaging context.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or reported in this summary. The device is a dose calculation software, not an AI-powered diagnostic image analysis tool that would typically involve human readers interpreting results with and without AI assistance.

6. Standalone Performance Study

Yes, a standalone performance study was done. The summary describes the validation of the XBeam algorithm's output (dose calculations) by comparing it against two independent methods:

1. Hand calculations.
2. The output of the predicate device, RADCalc.
   It also compared XBeam's planned dose to the physically delivered dose using measurement. This demonstrates the algorithm-only performance.

7. Type of Ground Truth Used

The ground truth used for the dosimetric accuracy validation was a combination of:

• Expert Consensus/Established Methods: "Hand calculations" (representing established physics principles and manual computation).
• A Legally Marketed Predicate Device's Output: "RadCalc (version: 7.3)".
• Physical Measurements/Outcomes Data (Indirectly): Comparison of "planned dose" (from XBeam) to "delivered dose" (presumably measured with dosimetry equipment in a controlled setting).

8. Sample Size for the Training Set

The document does not explicitly mention a "training set" or "training data." The XBeam software appears to be a dose calculation algorithm based on physics models, rather than a machine learning model that requires a distinct training phase with labeled data. Therefore, the concept of a training set as typically understood in AI/ML is not directly applicable to this description.

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

As noted above, the concept of a training set is not explicitly referred to for XBeam. The data that would inform the development and calibration of such a physics-based dose calculation system would typically come from extensive commissioning data (e.g., PDD curves, absolute dose output, beam profiles) measured for each specific Xstrahl radiotherapy system it supports, established via standard medical physics protocols. These measurements would be considered the "ground truth" for calibrating the physics model within the software. However, the document does not detail this specific process for XBeam's development.

Ask a specific question about this device