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510(k) Data Aggregation
(162 days)
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(30 days)
The Paragonix BAROguard is intended to be used for the static hypothermic preservation of lungs during transportation and eventual transplantation into a recipient using cold storage solutions indicated for use with the lungs.
The intended organ storage time for BAROguard is up to 8 hours.
Donor lungs exceeding clinically accepted static hypothermic preservation times should be evaluated by the transplant surgeon to determine transplantability in accordance with accepted clinical guidelines and in the best medical interest of the intended recipient.
Note: Partial lungs can be transported via BAROguard by packaging lungs per institutional protocol and UNOS guidelines.
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(234 days)
RevealAI-Lung Software is a computer aided diagnostic (CADx) software application intended for the characterization of incidentally-detected lung nodules on computed tomography (CT) scans. When a nodule is identified, the Software automatically compares the nodule characteristics with a clinically established database of lung nodules and provides a similarity score to assist clinicians' assessment of patients' cancer risk.
The mSI score is indicated for the evaluation of incidentally-detected pulmonary nodules of diameter 6-15mm in patients aged 18 years or above. In cases where multiple abnormalities are present, the mSI score can be used to assess each abnormality independently. Risk should be interpreted on an individual patient level and mSI is a relative risk score, not a percentage cancer risk.
Note that mSI is not indicated for lung cancer screening. The validation data excluded CT images with missing slices.
The RevealAI-Lung device is a post-processing software program that analyzes patient lung computed tomography (CT) images and is designed to provide computer-aided diagnostic (CADx) information about lung nodules to radiologists.
The user opens the patient's lung CT image from a third-party acquisition device in an existing medical device viewing system and scrolls through the image slices as in their normal workflow. The user identifies a lung nodule on the CT image, and evaluates that nodule for cancer risk and the potential need for follow-up using existing known risk factors, clinical management guidelines and the Reveal-AI-Lung provided mSI score. In cases where multiple nodules are present, RevealAI-Lung can be used to assess each nodule independently.
Here's a breakdown of the acceptance criteria and the study proving RevealAI-Lung meets them, based on the provided FDA 510(k) Clearance Letter:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Primary Endpoint (Multi-Reader Multi-Case (MRMC) Study): Improvement in radiologists' ability to discriminate between malignant and benign pulmonary nodules from CT images with and without the aid of the mSI. Measured as the difference in Area Under the Receiver Operating Characteristic Curve (AUC). | Average AUC improvement: 0.181 (from 0.538 unassisted to 0.719 with RevealAI-Lung assistance). This difference was statistically significant (p < 0.0001). |
| Consistency of Performance Across Readers: Every radiologist must improve their performance when using RevealAI-Lung. | Achieved: Every radiologist (10/10) improved their performance when using RevealAI-Lung. Individual AUC improvements ranged from 0.106 to 0.258. |
| Sensitivity Improvement (at 5% malignancy likelihood threshold): Increase in sensitivity when using RevealAI-Lung. | Increased sensitivity by 14 points (from 0.68 ± 0.039 to 0.82 ± 0.036). |
| Specificity Improvement (at 5% malignancy likelihood threshold): Increase in specificity when using RevealAI-Lung. | Increased specificity by 12 points (from 0.344 ± 0.041 to 0.467 ± 0.043). |
| Standalone Performance: Ability of RevealAI-Lung to discriminate between benign and malignant nodules. | Achieved: Standalone testing of RevealAI-Lung demonstrated it performed as expected in discriminating between benign and malignant nodules. (Specific quantitative metrics for standalone AUC are not explicitly provided, but "performed as expected" is stated.) |
| Validation on External Populations: Consistent device performance across additional incidental nodule populations. | Achieved: Tested on three additional populations (US, Canada, UK). Each study produced performance with an AUC > 0.8, and demonstrated follow-up decisions would be improved compared to clinical guidelines. |
| Consistency Across Subgroups: Performance improvements consistent across patient, nodule, and technical parameters. | Achieved: Results were independent of radiologist experience, patient demographics (age, sex, race/ethnicity), scan characteristics (contrast, scan date, manufacturer), and nodule parameters (size, lobe, opacity). Range of improvement in subgroups: 0.12 - 0.30. |
| Software Quality System Compliance: Adherence to FDA guidance for software in medical devices, 21 CFR §892.2060 special controls, human factors, usability, and cybersecurity. | Achieved: Design, validation, and verification were planned, executed, and documented according to FDA guidance. Assessed as Moderate Level of Concern. Usability evaluations confirmed safety and effectiveness. Cybersecurity activities and risk management were performed. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Clinical Performance Testing (MRMC Study): 108 cases (patients) with incidental lung nodules. The cases included size-matched benign and malignant nodules.
- Sample Size for Validation Testing on External Populations: 675 patients with incidental lung nodules (276 with cancer).
- Data Provenance:
- MRMC Study: Sourced from 3 US sites and 1 in Canada.
- External Validation Studies: One each from the US, Canada, and the UK.
- Retrospective or Prospective: Both the MRMC study and the external validation studies appear to be based on retrospective data, as they used "CT series... from patients in routine practice where lung nodules had been noted incidentally on the original radiology report" and involved "following the patients for at least 5 years" for ground truth (where pathology was not available).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
The document specifies the ground truth for the test sets (both MRMC and external validation) was established with "strict requirement for diagnostic certainty (either pathologic confirmation or two-years radiologic monitoring to confirm benign nodules)."
While it doesn't explicitly state the number of experts who established the ground truth, the involvement of "pathologic confirmation" or "two-years radiologic monitoring" implies the standard clinical practice involving pathologists and/or radiologists in the diagnostic process. The MRMC study itself involved 10 radiologists reading the cases, and while they were assessing malignancy likelihood, the ground truth for those cases was pre-established based on the methods described.
4. Adjudication Method for the Test Set
The adjudication method for establishing the ground truth (pathologic confirmation or two-year radiological monitoring) is not explicitly detailed in terms of expert consensus (e.g., 2+1, 3+1). However, the "strict requirement for diagnostic certainty" implies a high standard of clinical diagnosis.
For the MRMC study's reader evaluations, there was no direct adjudication of reader disagreement against each other. Instead, each reader's interpretation (with and without AI) was compared against the pre-established ground truth for each case. Each case was read twice by each reader, separated by a 28-day washout period, with AI use randomized for the second read.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance
Yes, an MRMC comparative effectiveness study was done.
- Reader Improvement: Radiologists improved their accuracy for the diagnosis of pulmonary nodules by an average of 18 points (0.181 AUC).
- Average AUC without the device: 0.538
- Average AUC with the device: 0.719
- Statistical Significance: This difference was statistically significant (p < 0.0001; Dorfman-Berbaum-Metz ANOVA random-reader random-case (RRRC) with jackknife (Wilcoxon)).
- Consistent Improvement: Every radiologist (10 out of 10) improved their performance when using RevealAI-Lung, with individual improvements ranging from 0.11 to 0.26 AUC points.
6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, standalone testing was done.
- Performance: "Standalone testing of RevealAI-Lung demonstrated that it performed as expected in discriminating between benign and malignant nodules."
- Additional Validation: "Validation of RevealAI-Lung was performed to determine device performance against the ground truth using pre-established acceptance criteria. The device was subsequently tested on incidental nodules from three additional populations (one each US, Canada, and the UK). Each of these studies produced performance with an AUC > 0.8, and demonstrated follow-up decisions would be improved compared to clinical guidelines." This indicates strong standalone performance on external datasets.
7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)
The ground truth for both training and validation sets was established with "strict requirement for diagnostic certainty":
- Pathologic Confirmation: For malignant nodules, this would typically involve biopsy results.
- Two-Years Radiologic Monitoring: For benign nodules, this means stable appearance over two years of follow-up CT scans, indicating a non-cancerous nature.
- Outcome Data: The phrase "following the patients for at least 5 years" for confidently matched diagnoses used in training, and implied in validation, points to long-term outcomes data to confirm the definitive diagnosis.
8. The Sample Size for the Training Set
- Training Dataset: RevealAI-Lung was trained on "radiologist-identified lung nodules from 4-30mm in diameter."
- Specific Sample Size: The exact number of cases or nodules in the training set is not explicitly stated in the provided document, beyond the characteristics of the subjects (median age 63, 43% female).
9. How the Ground Truth for the Training Set Was Established
- Method: "Only nodules that were confidently matched to a definitive diagnosis were used for training, including following the patients for at least 5 years."
- This implies a combination of pathology (for malignant cases) and long-term radiologic stability/outcomes (for benign cases) to ensure diagnostic certainty, similar to the method described for the test sets. The mention of "radiologist-identified lung nodules" for the training set likely refers to how the nodules were initially marked or selected, while the "confidently matched to a definitive diagnosis" over 5 years is how their ground truth was ultimately confirmed.
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(30 days)
The device is indicated for use by dental technicians in the construction of custom made all ceramic restorations for anterior and posterior location.
BruxZir Shaded 16 PLUS blanks are used for the production of full-contour zirconia and zirconia-based substructures for crowns and bridges (restorations). Multiple thicknesses and shades (A1, A2, A3, A3.5, A4, B1, B2, B3, B4, C1, C2, C3, C4, D2, D3, D4, BL1, BL3, and White) are available for milling into BruxZir restorations. The manufactured restorations are made utilizing the CAD/CAM system for design and manufacturing. The designed and manufactured restorations are then sintered and glazed. BruxZir Shaded 16 PLUS restorations are designed to match the body shade in the glazed state; however, precolor and stain should be applied if polychromatic (gingival to incisal) blending or other esthetic effects are desired. The sintered material exhibits maximum strength, color, and translucency similar to natural dentition.
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(147 days)
"POINT" Kinguide Agile Robotic Arm Surgical Stereotactic System (Kinguide RobotArm) is an accessory to the compatible "POINT" Kinguide Agile Hybrid Navigation System (Kinguide Agile) and is intended to be an intraoperative image guided localization system to support the surgeon to achieve pre-planned trajectories with surgical instruments.
"POINT" Kinguide Agile Hybrid Navigation System is intended as an aid for precisely locating anatomical structures in either open or percutaneous neurosurgical and orthopedic procedures.
The device is indicated for pedicle screw entry point alignment and angular orientation when using a posterior approach into T12 to S1 vertebrae (or T1-S1 vertebrae when used with the "POINT" Kinguide RobotArm), and where reference to the rigid anatomical structure can be identified by intraoperative 3D reconstruction images.
"POINT" Kinguide Agile Robotic Arm Surgical Stereotactic System (Kinguide RobotArm) is an accessory to the compatible "POINT" Kinguide Agile Hybrid Navigation System (Kinguide Agile) and is intended to be an intraoperative image guided localization system to support the surgeon to achieve pre-planned trajectories with surgical instruments.
Kinguide Agile system is intended as an aid for precisely locating anatomical structures in either open or percutaneous neurosurgical and orthopedic procedures. Kinguide Agile system is indicated for any medical condition in which the use of stereotactic spinal surgery may be appropriate, and where reference to a rigid anatomical structure can be identified relative to the digital markers of medical images (e.g. 3D C-arm) of the anatomy.
Kinguide RobotArm is compatible with Kinguide Agile Software version 15.0.0 or above.
Kinguide RobotArm consists of a RobotArm Station, a Guiding Tool Set and single use accessories.
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(59 days)
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:
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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).
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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.
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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.
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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.
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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
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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
| Metrics | Acceptance Criteria | Reported Device Performance (Mean with 98.75% confidence intervals) |
|---|---|---|
| 3D Dice Similarity Coefficient (DSC) | > 0.9 | 0.96 (0.96, 0.97) |
| 2D Dice Similarity Coefficient (DSC) | > 0.9 | 0.96 (0.95, 0.96) |
| Mean Surface Distance (MSD) | < 1.0 mm | 0.57 mm (0.485, 0.68) |
| Hausdorff Distance (HD) | < 3.0 mm | 1.68 mm (1.23, 2.08) |
| ∆Dmin (difference in minimum diameter) | > 95% |∆Dmin| < 5 mm | 98.8% (98.3-99.2%) |
| ∆Dmax (difference in maximum diameter) | > 95% |∆Dmax| < 5 mm | 98.5% (97.9-98.9%) |
The reported device performance for all primary and secondary metrics meets the predefined acceptance criteria.
Study Details for Aorto-iliac Outer Wall Segmentation Validation
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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%)
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Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
- Number of Experts: Three
- Qualifications: US-board certified radiologists.
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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.
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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.
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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.
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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.
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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.
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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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(126 days)
The Perin Health Platform is a wireless remote monitoring system intended for use by healthcare professionals for spot check collection of physiological data in healthcare and home settings for long-term monitoring. The Perin Health Patch can monitor auscultation data of heart and lung sounds, photoplethysmography waveforms (PPG), oxygen saturation (%SpO2), heart rate, electrocardiography (ECG), heart rate variability, R-R interval, respiratory rate, skin temperature, activity detection (including step count), and posture (body position relative to gravity including fall).
The Perin Health System is intended for spot-checking and tracking changes of adult patients in hospitals, clinics, long-term care, and at home. In home-use environments, the Perin Health Platform is able to integrate with optional third-party devices for blood pressure, and weight data collection via the mobile application. The mobile application transmits data from the Health Patch and third party devices to the cloud and web-based portal for storage, analysis, and review by healthcare professionals. The Perin Health Platform can include the ability to notify healthcare professionals when physiological data falls outside set limits or manual trigger by the patient.
The device is intended to provide physiological information for non-critical, adult population.
The Perin Health System is a wireless remote patient monitoring platform that enables healthcare professionals to perform spot-checking and retrospective monitoring of physiological data from adult patients. The Perin Health System is designed for use in hospitals, clinics, long-term care facilities, physician offices, and home environments.
The Perin Health System comprises the following components:
- Perin Health Patch wearable device
- Perin Health Patient Mobile Application
- Perin Health Cloud
- Perin Health Provider Portal
- Perin Health Inpatient Application
1. The Perin Health Patch
The Perin Health Patch is a chest-worn wearable device that performs scheduled spot-check measurements of multiple physiological parameters. Unlike continuous monitoring systems, the Perin Health Patch captures measurements at predetermined intervals configured by healthcare providers based on clinical need.
The device integrates six primary sensing modalities:
- Auscultation (heart and lung sounds)
- Electrocardiography (1-channel ECG)
- Pulse oximetry via photoplethysmography (PPG)
- Bioimpedance (BioZ) for respiratory monitoring
- Temperature sensing (skin)
- Motion and orientation detection via accelerometer
The combination of these modalities in a small, low-power wearable form allows for the spot-checking of primary vital signs:
- Heart rate and R-R intervals
- Heart rate variability (HRV) parameters
- ECG waveform data
- Auscultation sound data (heart and lung sounds)
- Respiratory rate
- Pulse (PPG) waveform
- Oxygen saturation (SpO2%)
- Skin temperature
- Fall detection events
- Body posture
- Activity level and step count
The device adheres to the patient's upper left chest at the second intercostal space with a medical-grade long-term wear adhesive. The adhesive is placed on the patient-facing side of the wearable, with cutouts for the sensors to make direct contact with the skin. The wearable device is lightweight and semi-flexible, allowing for the device to conform to the natural curvature of the chest. It is water resistant, allowing for bathing and normal activities while the patient is wearing the system.
The wearable communicates to the receiving unit (mobile phone) via an encrypted Bluetooth Low Energy connection. Measurements, all notifications and control commands, and software updates are transmitted over the BLE connection. The wearable uses Near Field Communication (NFC) to facilitate the Bluetooth pairing process with the mobile phone by simply having to tap their phone to the device to initiate a Bluetooth connection. The wearable device also contains on-board memory that can store over two weeks of spot-check data. When measurements are taken and no receiving unit is present, the wearable can store recordings in the onboard memory. Recordings are stored in a stack, such that at the next connection possibility between the wearable and the receiving unit, the most recent data will be transmitted first followed by other measurements in reverse chronological order.
Other key features of the wearable include:
- Customizable recording schedule set by the healthcare provider in their care program
- Replaceable battery
- Patient-triggered recordings via double-tap
- Signal quality indicators for measurement validation and identification of noisy measurements
2. The Patient Mobile Application
The Patient Mobile Application, available on iOS or Android platforms, is intended exclusively for use in home environments by patients under healthcare provider supervision. The application serves as a data relay and display interface, allowing the patient to complete key tasks, including onboarding, device setup, device communication, and patient-reported data.
The application serves as the primary interface between the Perin Health Patch and the cloud infrastructure, receiving spot-check measurements from the device and uploading them for provider review. The application establishes and manages secured BLE communication with the Health Patch. Given that the Health Patch operates on provider-configured recording schedules, the application manages data transfer in the background with minimal patient interaction required. When internet connectivity is unavailable, the application stores measurements locally until transmission becomes possible. The system also manages firmware updates for the Perin Health Patch.
The application integrates with FDA-cleared third-party blood pressure cuff and scale using BLE and transfers the data to the Cloud System. Healthcare providers determine which patients require the additional third-party device monitoring as part of their individualized care programs. The system also allows users to optionally enter manual data for blood pressure and weight if no third-party device is connected.
Patients are able to review their historical measurement data taken throughout their monitoring program and their goals and thresholds set by their providers. The patient can view metrics assigned within their care program:
- Heart Rate and Heart Rate Variability
- Respiratory Rate
- Oxygen Saturation
- Step Count
- Temperature
- Blood Pressure
- Weight
Patients can also select audio segments captured by the device for playback (no visualization).
The application provides comprehensive patient engagement features. Patients can complete customized questionnaires with up to 20 questions in various formats, review educational content delivered through their care programs, and submit non-critical medical reports to their care team. The reporting feature includes anatomical body mapping for location-specific symptoms, severity scaling, and photo attachment capabilities. The application supports secure messaging with care providers, virtual appointment attendance with waiting room functionality, and comprehensive offline operation with automatic synchronization upon connectivity restoration.
3. The Perin Health Cloud
The Perin Health Cloud infrastructure serves as the central hub for data management and processing. The cloud system receives encrypted spot-check data from relay systems and manages raw data processing (for Health Patch data only), storage, and retrieval of physiological measurements for retrospective clinical review. Algorithms are run in the cloud to process measurements from the Health Patch and generate Signal Quality Index, Heart Rate, Heart Rate Variability, Respiratory Rate, Oxygen Saturation, and Posture.
The alert and notification system enables healthcare professionals to configure multi-level alerts based on clinical parameters, technical issues, or manual patient triggers. Clinical alerts are based on provider-configured thresholds that are set in during the enrollment of a patient in a care program. The system supports complex notification rules including threshold exceedances, percentage changes, trending patterns, and consecutive violations. Alerts are displayed to providers for the purpose of highlighting data during their retrospective review and are not intended to support real-time patient monitoring or urgent care provider action.
The cloud infrastructure includes comprehensive audit logging of all user actions, data access, and system events. The system provides API access for integration with electronic health records with HL7 v2.x, HL7 FHIR R4, and other standard protocols, enabling bidirectional data exchange with major EHR systems.
4. The web-based Provider Portal
The web-based Provider Portal enables healthcare professionals to access and manage patient data and alert statuses remotely through any compatible web browser. Through the portal, providers can review spot-check measurements and historical trends, playback audio recordings of auscultation sounds captured by the Patch, configure individualized care programs, set measurement schedules and alert thresholds, and communicate with patients through various modalities.
Through the portal, providers can review spot-check measurements with customizable vital sign charts displaying trends over days, weeks, or months. Advanced visualization includes waveform analysis for ECG and PPG signals, audio playback for auscultation recordings, and comprehensive annotation tools. The portal displays signal quality indicators and out-of-range values with appropriate visual highlighting based on configured thresholds. The portal also displays patient severity levels (Low/Medium/High) based on the NEWS2 scoring methodology. Additional clinical measures, such blood pressure and weight, that are manually input into the EHR can be read into the Perin Health System and viewed in the Provider Portal using the EHR interface.
The system employs a structured care program architecture that ensures appropriate clinical oversight throughout the monitoring process. Healthcare organizations create standardized care program templates for common conditions. Individual providers can then select from these approved templates and customize them for specific patient needs, prescribing the specific devices needed, measurement frequencies appropriate to the condition, and recording schedules tailored to clinical requirements.
The portal includes comprehensive communication capabilities supporting both patient and care team interactions. Providers can conduct virtual appointments with integrated video calling, AI-powered real-time transcription using AWS HealthScribe, and automated clinical note generation structured into standard sections. The messaging system supports secure text communication with file attachments, while the task management system enables care coordination across team members. Providers can create and deploy customized questionnaires with various response types and scoring algorithms, manage educational content delivery, and review patient-submitted reports with collaborative response capabilities.
Additional portal features include appointment scheduling with EHR integration, comprehensive alert management with acknowledgment workflows, administrative functions for user and device management, and organization hierarchy configuration. The portal provides detailed audit trails, performance analytics, and compliance reporting to support quality improvement initiatives.
5. The Perin Health Inpatient Module
The Perin Health Inpatient Module provides a monitoring dashboard for monitoring capabilities in healthcare facility environments. The modules leverage the existing architectures for the Mobile Application and Provider Portal but offer unique interfaces for inpatient spot-check measurements.
The web-based monitoring dashboard, a page accessible through the Provider Portal, displays vital signs for up to 50 concurrent patients in a grid layout. Each patient card shows the latest values for heart rate, respiratory rate, oxygen saturation, temperature, and device status, with automatic sorting by alert priority and visual indicators for threshold violations. The dashboard refreshes every second, updating as new spot-check recordings are captured from patients across the unit.
The bedside Inpatient Application is built on top of the Android architecture of the Patient Mobile app and operates in kiosk mode. The Beside app only interfaces with the Perin Health Patch and relays information to the Cloud to provide clinicians with access to recent measurements in the Provider Portal Inpatient view. The application also maintains local data storage for backup operation and automatically synchronizes with the cloud upon connectivity restoration. Providers are unable to manually input clinical data (e.g., blood pressure measurements) directly into the bedside Inpatient Application but manual data input into the EHR can be read into and visualized in the Provider Portal over the EHR interface.
The Perin Health System supports monitoring in hospitals and out-of-hospital patient care settings where care is administered by healthcare professionals. Visual alarm indicators highlight parameter exceedances according to configured thresholds. High-priority alerts display prominently with appropriate color coding, though all clinical responses and acknowledgments must be performed through the Provider Portal to maintain proper documentation and workflow management.
The Perin Health System facilitates comprehensive spot-checking and retrospective monitoring across the continuum of care. Data flows from the wearable patch and third-party devices through the patient mobile application to the central cloud infrastructure, where processing algorithms derive clinical insights. Healthcare providers access this information through the web portal or inpatient displays for clinical review and analysis, enabling healthcare providers to track patient progress, adjust treatment plans based on measurements, and identify patients requiring intervention based on retrospective data trends.
Here's a breakdown of the acceptance criteria and the study details for the Perin Health System (PHD80060-2), based on the provided FDA 510(k) clearance documentation:
Acceptance Criteria and Device Performance Study (Perin Health System PHD80060-2)
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria and reported device performance for key physiological parameters are summarized below:
| Parameter | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Heart Rate | 20-200 bpm ± 3 BPM or 5%, whichever is greater (based on primary predicate UbiqVue) | High levels of agreement between the Perin Health Patch and the reference Holter monitor across all evaluated parameters for ECG, HR, and HRV for 243 participants. |
| Respiratory Rate | Bench Testing: 5-30 Breaths per Minute ± 1 Breaths per Minute (Accuracy Root Mean Square (Arms)).Clinical Study: ± 3 Breaths per Minute (Accuracy Root Mean Square (Arms)) derived from Trans-thoracic Impedance (TTI) and ECG Derived Respiration (EDR) based on RS Amplitude. (Predicate UbiqVue had ≤ 1 Breath per minute MAE for simulation, ≤ 3 Breaths per minute MAE for clinical study) | Clinical Validation: Arms of 1.7 breaths per minute for 259 points. Subgroups exhibited Arms between 0.5 and 2.8. Clinical Validation: Mean Absolute Error (MAE) of 0.8 breaths per minute for 259 points. Subgroups exhibited MAE between 0.4 and 1.3. |
| Skin Temperature | 15 C - 50°C ± 0.3°C Resolution: 0.008°C Time response: 30 minutes Measurement mode: Direct ISO 80601-2-56 (Matching primary predicate UbiqVue) | Verified by using bench testing as per ISO 80601-2-56:2017(E). (Specific accuracy values beyond "verified" are not explicitly stated for the Perin Health System in this summary, but implied to meet the criteria) |
| SpO2% | 70% - 100% ± 3 % (Predicate UbiqVue 0 to 100% ± 3 % (100 to 70%), Less than 70% unspecified) | Clinical Validation: Overall measured Arms in the range of 70 to 100% SpO2 was 3.3%. Arms of 3.5% for 67% to <80%, 3.1% for 80% to <90%, and 3.3% for 90% to 100%. (This implicitly meets the ± 3% criterion for the 70-100% range, with Arms values slightly above 3% for the lower range. The predicate allows unspecified below 70%). |
| Posture | Prone, supine, left lateral recumbent, right lateral recumbent, Fowler's, Trendelenburg, upright, leaning forward (> 80% average sensitivity and specificity, compared to visual) | Verified by using bench testing as per the acceptance criteria. (Specific sensitivity and specificity values are not explicitly stated beyond "verified" but implied to meet the criteria.) |
| Body Motion | Active or sedentary (> 90% sensitivity and specificity) | Verified by using bench testing as per the acceptance criteria. (Specific sensitivity and specificity values are not explicitly stated beyond "verified" but implied to meet the criteria.) |
| Fall Detection | Fall or no fall (> 80% sensitivity and specificity) | Verified by using bench testing as per the acceptance criteria. (Specific sensitivity and specificity values are not explicitly stated beyond "verified" but implied to meet the criteria.) |
| Step Count | < 5% Absolute Error Compared to Manual Count for speeds of at least 2 miles per hour Compliance: ANSI/CTA-2056-A | Verified via bench testing as per ANSI/CTA-2056-A. (Specific absolute error is not explicitly stated beyond "verified" but implied to meet the criteria.) |
| Auscultation Data | Verified by using bench testing in accordance with acceptance criteria. (No specific numerical accuracy given) | Verified by using bench testing in accordance with acceptance criteria. (Specific performance metrics are not detailed beyond meeting acceptance criteria). |
| ECG, R-R Interval, HRV | Performance testing in compliance with ANSI/AAMI/IEC 60601-2-27:2011, ANSI/AAMI/IEC 60601-2-47:2012 (No specific numerical accuracy given for these parameters directly here) | Clinical Validation: Demonstrated high levels of agreement between the Perin Health Patch and the reference Holter monitor across all evaluated parameters (timing intervals, SNR, morphological features) and for all demographic and clinical subgroups for 243 participants. |
| Wear-life | Sustained adhesion to the body for 360 hours. | Demonstrated stable performance across all evaluated parameters (timing intervals, SNR, morphological features) and for all demographic and clinical subgroups over 360 hours. |
Note: For several parameters (Skin Temperature, Posture, Body Motion, Fall Detection, Step Count, Auscultation data), the document states they were "verified by using bench testing as per the acceptance criteria" or "in accordance with acceptance criteria," implying they met the specified thresholds without explicitly re-stating the achieved performance metrics.
2. Sample Size Used for the Test Set and Data Provenance
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SpO2% (Induced Hypoxia Study):
- Sample Size: 12 healthy adults (5 female, 7 male)
- Data Provenance: Not explicitly stated (e.g., country of origin), but implied to be prospective clinical validation conducted for this submission.
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Respiratory Rate (Clinical Validation):
- Sample Size: 35 participants (17 males, 18 females)
- Data Provenance: Not explicitly stated (e.g., country of origin), but implied to be prospective clinical validation conducted for this submission.
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ECG, Heart Rate, R-R Interval, and Heart Rate Variability (Clinical Validation):
- Sample Size: 243 participants
- Data Provenance: Not explicitly stated (e.g., country of origin), but implied to be prospective clinical validation conducted for this submission.
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Wear-life Performance (Internal Clinical Wear Life Evaluation):
- Sample Size: 26 participants
- Data Provenance: Across 3 clinical sites. Implied to be prospective clinical evaluation.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not explicitly state the number or specific qualifications of experts used to establish ground truth for the clinical test sets. However, it references:
- SpO2%: "arterial blood samples analyzed by a laboratory co-oximeter" as the gold standard. This implies specialized laboratory personnel for analysis, but their number and specific qualifications are not detailed.
- Respiratory Rate: "manually counted end-tidal CO2" as the gold standard. This would typically be performed by trained clinical staff, but their number and qualifications are not specified.
- ECG, HR, HRV: "standard Holter monitor" as the reference for comparison. Interpretation of Holter data would involve cardiologists or trained technicians, but the document doesn't specify if this was used as "ground truth" to establish the Holter reference itself or if it refers to the Holter output as the reference measurement.
4. Adjudication Method for the Test Set
The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test sets. The studies compare the device's measurements directly to a "gold standard" or "reference monitor" without mentioning a multi-reader adjudication process for discrepancies.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
There is no indication of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being done to evaluate how much human readers improve with AI vs. without AI assistance. The document focuses on the standalone performance of the device's measurements against established standards.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
Yes, standalone performance was done for several key parameters. The clinical validation studies directly assess the Perin Health System's ability to measure physiological data (SpO2%, Respiratory Rate, ECG/HR/HRV) against a specified gold standard or reference device. These studies inherently evaluate the algorithm's performance without direct human interpretation influencing the measurement output. For example:
- SpO2% accuracy is measured against arterial blood samples.
- Respiratory rate accuracy is measured against manually counted end-tidal CO2.
- ECG, HR, HRV performance is validated against a standard Holter monitor.
7. Type of Ground Truth Used
The types of ground truth used for the clinical validation studies include:
- Laboratory Standard / Direct Measurement: For SpO2%, the ground truth was "arterial blood samples analyzed by a laboratory co-oximeter."
- Clinical Gold Standard: For Respiratory Rate, the ground truth was "manually counted end-tidal CO2."
- Reference Clinical Device: For ECG, Heart Rate, R-R Interval, and Heart Rate Variability, the ground truth/reference was a "standard Holter monitor."
8. Sample Size for the Training Set
The document does not provide any information regarding the sample size for the training set. This information is typically proprietary to the manufacturer and not usually disclosed in 510(k) summaries unless specifically relevant to a novel AI/ML algorithm requiring such details for FDA review.
9. How the Ground Truth for the Training Set Was Established
Since no information about the training set or its sample size is provided, there is no information available on how the ground truth for the training set was established.
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(261 days)
The Eyer 2 is accompanied by accessories: frontal module for the ocular surface (1pc), dock station (charging station) (1pc), eye cap (1pc), lens protector (1 pc), storage case (1 pc), cleaning cloth (1 pc), allen wrench (1 pc), quick start guide (1 pc), welcome card (1 pc), shipping box (1 pc), power supply (1 pc), slit-lamp adapter (1 pc), silica gel bags (2 pcs).
Eyer 2 is designed for use in a medical environment by healthcare professionals. Captured images and videos are used for documentation and consultation. The images and videos are securely stored in an internal smartphone application database.
For the retinal function, the Eyer 2 is designed for non-mydriatic fundus imaging. In non-mydriatic imaging, no mydriasis is needed because infrared light is used for targeting the fundus and white light is flashed when an image is taken. The pupil does not respond to the infrared light so examination is convenient for the patient. With small pupils, it is recommended to use mydriatic drops. Eyer 2 has fixation targets for the patient to fixate on during imaging. The middle fixation target provides a macula-center image. It is possible to fix the optical disc in the center by selecting the appropriate point.
For the ocular surface and surrounding areas function, Eyer 2 has an ocular surface module with white, blue, and infrared light sources for imaging the eye surface and surrounding areas; in this configuration, the device does not make contact with the patient.
The transfer of images to a PC is carried out via DICOM, DICOMWEB, FTPS, or local folder connections, with the client responsible for the connection and subsequent storage.
The Eyer 2 energy comes from the smartphone that has a rechargeable Li-Ion battery and is charged when the device is docked on the charge station, which is connected to the mains by a power supply cable.
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(24 days)
The Azurion series (within the limits of the used Operating Room table) are intended for use to perform:
- Image guidance in diagnostic, interventional, and minimally invasive surgery procedures for the following clinical application areas: vascular, non-vascular, cardiovascular, and neuro procedures.
- Cardiac imaging applications including diagnostics, interventional and minimally invasive surgery procedures.
Additionally:
- The Azurion series can be used in a hybrid Operating Room.
- The Azurion series contain a number of features to support a flexible and patient centric procedural workflow.
The Azurion R3.1 is classified as an interventional fluoroscopic X-Ray system. The primary performance characteristics of the Azurion R3.1 include:
- Real-time image visualization of patient anatomy during procedures
- Imaging techniques and tools to assist interventional procedures
- Post processing functions after interventional procedures
- Storage of reference/control images for patient records
- Compatibility with hospital information systems (HIS) and image archiving systems via DICOM
- Built in radiation safety controls.
The only changes to the subject device, Azurion R3.1 includes the design change to the mattress accessory for all the existing mattresses of the predicate device (Azurion R3.1, K251827, 24 October 2025) and introduction of new gray color mattress. The change includes the addition of a hook and loop fastener (Velcro) solution for use between the mattress and the system integrated patient table (AD7X), to ensure that the mattress does not slip from the patient table.
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