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The Philips CT Big Bore is a Computed Tomography X-Ray System intended to produce images of the head and body by computer reconstruction of x-ray transmission data taken at different angles and planes. These devices may include signal analysis and display equipment, patient and equipments and accessories. These systems are indicated for head and whole body X-ray Computed Tomography applications in oncology, vascular and cardiology, for patients of all ages.

These scanners are intended to be used for diagnostic imaging and for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer*. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

• Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

The Philips CT Big Bore is available in three system configurations, the Oncology configuration the Radiology (Base) configuration, and the new Sliding Gantry configuration.

The main components (detection system, the reconstruction algorithm, and the x-ray system) that are used in the Philips CT Big Bore have the same fundamental design characteristics and are based on comparable technologies as the predicate.

The main system modules and functionalities are:

• 1. Gantry. The Gantry consists of 4 main internal units:
  • a. Stator a fixed mechanical frame that carries HW and SW
  • b. Rotor A rotating circular stiff frame that is mounted in and supported by the stator.
  • c. X-Ray Tube (XRT) and Generator fixed to the Rotor frame
  • d. Data Measurement System (DMS) a detector array, fixed to the Rotor frame
• 2. Patient Support (Couch) supports the patient in a stationary position while the gantry moves in and out on a carriage.
• 3. Console A two part subsystem containing a Host computer and display that is the primary user interface and the Common Image Reconstruction System (CIRS) - a dedicated, powerful image reconstruction computer

In addition to the above components and the software operating them, each system includes workstation hardware and software for data acquisition, display, manipulation, storage and filming as well as post-processing into views other than the original axial images. Patient supports (positioning aids) are used to position the patient.

The Sliding Gantry Configuration provides the following features.

• . The patient table is stationary during the scan.
• . Patient is located on the stationary table.
• To perform imaging, CT gantry rides on a carriage and moves over the patient.

The Philips CT Big Bore Sliding Gantry Configuration is a Computed Tomography X-Ray System. The device has undergone non-clinical testing to demonstrate its substantial equivalence to the primary predicate device, Philips CT Big Bore (K171850).

1. Acceptance Criteria and Reported Device Performance

The document states that the validation test plan was executed as planned and acceptance criteria were met for each requirement. The "Values and ranges measured on phantoms" for the Philips CT Big Bore Sliding Gantry Configuration implies these were the performance metrics observed and deemed acceptable.

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

The testing involved phantom studies for basic CT performance tests. The document does not specify an exact "sample size" in terms of number of phantoms or scans. The data provenance is through bench testing of the device against international and FDA recognized consensus standards and FDA regulations and guidance documents. This is a non-clinical study.

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

This information is not applicable as the study was non-clinical bench testing using phantoms, not a study involving human experts establishing ground truth for diagnostic imaging.

4. Adjudication method for the test set

Not applicable as no human adjudication was performed; the study involved objective measurements of physical phantom properties.

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 MRMC comparative effectiveness study was done. The document explicitly states: "The proposed Philips CT Big Bore Sliding Gantry Configuration did not require a clinical study since substantial equivalence to the legally marketed predicate device was proven with the verification/validation testing."

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

The device is a Computed Tomography X-Ray System, not an AI algorithm. The performance evaluation was of the system itself (hardware and embedded software) in producing images based on defined physical metrics. Therefore, this can be considered a "standalone" evaluation of the system's technical performance.

7. The type of ground truth used

The ground truth was based on objective physical measurements from phantoms as per established CT performance testing standards. For example, for CT number accuracy, the ground truth for water is 0 HU.

8. The sample size for the training set

Not applicable. This device is a CT imaging system and not an AI algorithm that typically requires a training set. The performance evaluation was based on non-clinical verification and validation testing against engineering requirements and established standards.

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

Not applicable. As stated above, this device is a CT imaging system and does not involve a training set that requires ground truth establishment in the context of machine learning.

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The SureSigns VS3 vital signs monitor is for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. The SureSignsVS3 is for monitoring, recording and alarming of multiple physiological parameters in healthcare environments for patient types listed below. Additionally, the monitor may be used in transport situations within a healthcare facility.

The SureSigns VS4 vital signs monitor is for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. The SureSigns VS4 is for monitoring, recording and alarming of multiple physiological parameters in healthcare environments for patient types listed below. Additionally, the monitor may be used in transport situations within a healthcare facility.

The subject devices are SureSigns VS3 and SureSigns VS4 multi-parameter patient monitors. Modifications include adding connectivity to IntelliVue GuardianSoftware (IGS), adding Temporal Temperature measurement to VS3, and other enhancements such as additional title bar indicators, expanded patient records pane, display patient name or primary ID, expanded QuickCapture entries, default CO2 setting alignment (VS4 only), temperature high and low alarms color change, increased SpO2 alarm delay defaults, option to prevent inadvertently clearing NBP programs, and RFID patient ID entry.

This document describes the Philips SureSigns VS3 and VS4 Vital Signs Monitors, which are multi-parameter patient monitors. The submission is a 510(k) premarket notification for modifications to previously cleared devices.

Here's an analysis of the acceptance criteria and study information provided, focusing on what can be extracted from the text:

1. Table of Acceptance Criteria and Reported Device Performance

The document broadly states that "Pass/Fail criteria were based on the specifications cleared for the subject device." However, it does not explicitly list specific numerical acceptance criteria for performance metrics (e.g., accuracy ranges for NBP, SpO2, Temperature, CO2). Instead, it confirms that the device reportedly meets these implicit criteria.

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

The document does not provide details on the sample size used for the test set.
It also does not specify the data provenance (e.g., country of origin, retrospective or prospective). The testing appears to be internal validation by Philips Medical Systems.

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

This information is not provided in the document. The testing described is verification, validation, and performance testing, which typically involves comparing device outputs to a known reference standard (e.g., calibration equipment, established measurement methods) rather than human expert-established ground truth in a clinical scenario.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of the device (monitoring physiological parameters), "adjudication" in the sense of resolving discrepancies between human experts for a diagnostic task is unlikely to be relevant here. The testing would involve comparing device readings against a gold standard or reference measurement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Improvement

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is typically for diagnostic imaging devices where human readers interpret images with and without AI assistance. The SureSigns VS3 and VS4 are vital signs monitors, not diagnostic imaging devices designed for human interpretation of complex data patterns in the same way. The modifications focus on software connectivity, a new temperature measurement accessory, and feature enhancements.

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

The document describes "system level tests, performance tests, and safety testing from hazard analysis." This strongly implies that standalone performance testing was done for the device's functions (e.g., accuracy of NBP, SpO2, Temperature measurements). The software verification and validation, along with compliance to specific standards for temperature measurement, suggest testing of the algorithms and hardware in a standalone capacity against reference standards. However, "standalone" in the context of an AI algorithm is typically for diagnostic or predictive AI, which this device, in its described modifications, does not appear to be. It's a measurement device.

7. The Type of Ground Truth Used

The ground truth used for these types of physiological parameter monitors would typically involve:

• Reference standard instruments: Highly accurate and calibrated devices used to establish the true value of parameters like NBP, SpO2, and Temperature.
• Known physical inputs: For example, precise temperature probes, blood pressure simulators, or SpO2 simulators with defined oxygen saturation levels.
• Compliance with industry standards: The mention of ISO 80601-2-56 for temperature indicates that the ground truth for temperature measurement accuracy would be established by following the specific protocols and reference measurements outlined in that standard.

The document does not explicitly state "expert consensus" or "pathology" as ground truth, which are more relevant for diagnostic AI applications.

8. The Sample Size for the Training Set

The document does not describe any "training set." This is because the device, based on the provided information, is a vital signs monitor and not an AI/ML-based diagnostic or predictive system that requires a training set for model development. The focus is on measurement accuracy and software functionality.

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

Since there is no mention of a training set, this information is not applicable/provided.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring and recording of, and to generate alarms for, multiple physiological parameters of adults and pediatrics in hospital environments and during transport inside hospitals.

The name of this device is the MX40 patient monitor.

The provided text describes a 510(k) premarket notification for the Philips MX40 patient monitor. However, the information regarding specific acceptance criteria, detailed study design, sample sizes, expert qualifications, adjudication methods, or specific performance metrics is very limited.

Here's an analysis of what can be extracted and what is missing, presented in the requested format:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The document mentions "system level tests, performance tests, and safety testing," but does not indicate whether these involved clinical data, simulated data, or a combination, nor does it mention the country of origin or whether it was retrospective or prospective.

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

• Number of Experts: Not mentioned.
• Qualifications of Experts: Not mentioned.
• Ground Truth Establishment: Not mentioned. Given the nature of a patient monitor, "ground truth" would likely involve comparison to established reference standards or expert clinical assessment, but this is not detailed.

4. Adjudication method for the test set

• Adjudication Method: Not mentioned.

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, this is not an MRMC comparative effectiveness study. The device is a patient monitor, not an AI-assisted diagnostic tool for human readers. Therefore, the concept of "human readers improve with AI" is not applicable here.

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

• This device is a patient monitor, and its performance would inherently be "standalone" in terms of its physiological measurement and alarm functions. However, the text does not explicitly refer to 'algorithm-only' or 'human-in-the-loop' performance. The evaluations were focused on the device's ability to "monitor and record" and "generate alarms," which are intrinsic functions of the device itself.

7. The type of ground truth used

• Type of Ground Truth: Not explicitly stated. For a patient monitor, ground truth would typically refer to the true physiological state or precise measurements obtained from a gold standard reference device, or expert clinical assessment. The document states "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics," suggesting an engineering and performance testing approach rather than an explicit "ground truth" in the clinical evaluation sense, although clinical validity would be part of overall performance.

8. The sample size for the training set

• Sample Size for Training Set: This device is a physiological monitor, not a machine learning or AI-driven system that would typically have a "training set" in the common understanding of those terms. Therefore, this information is not applicable or provided.

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

• Ground Truth for Training Set: Not applicable (as explained in point 8).

Summary of the Study that Proves the Device Meets Acceptance Criteria:

The document broadly states that "Verification, validation, and testing activities" were conducted. These activities involved:

• System level tests: Evaluation of the integrated system.
• Performance tests: Assessment of the device's functional capabilities (e.g., accuracy of physiological parameter measurement, alarm generation).
• Safety testing from hazard analysis: Ensuring the device meets safety requirements.

The acceptance criteria for these tests were based on the existing "specifications cleared for the predicate device" (TRx4841A, IntelliVue Telemetry System Transceiver). The study concluded that the new device, MX40 patient monitor, exhibited "substantial equivalence" to the predicate device and "meets all defined reliability requirements and performance claims."

In essence, the provided text describes a regulatory submission where the new device's equivalence to a cleared predicate device was established through a series of internal verification and validation tests, rather than a detailed clinical trial with extensive ground truth establishment and expert review. The focus was on demonstrating that the new device performs as reliably and effectively as its legally marketed predecessors according to their established specifications.

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Diagnostic ultrasound imaging and fluid flow analysis of the human body as follows: Ophthalmic, Fetal/Obstetric, Abdominal, Intra-operative (vascular/epicardial), Intra-operative (Neuro), Laparoscopic, Pediatric, Small Organ (thyroid, scrotum, prostate, breast), Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Trans-urethral, Trans-esoph. (non-Card.), Intra-luminal, Other (Gynecological), Cardiac Adult, Cardiac Pediatric, Trans-esoph. (Cardiac), Other (Fetal), Peripheral vessel, Musculo-skel (conventional), Musculo-skel (superficial).

The HD7 is a diagnostic ultrasound device. It consists of a system console containing the power supply and electronic circuitry required to generate the image, a display screen, and a connection to the separate transducers. The system circuitry generates an electronic voltage pulse, which is transmitted to the transducer. In the transducer, a piezo-electric array converts the electronic pulse into an ultrasonic pressure wave. When coupled to the body, the pressure wave transmits through body tissues. The differing acoustic properties of the tissues in the body reflect some of the transmitted energy back to the transducer, where it is converted back to electrical signals and sent back to the system. In the system, advanced signal processing technologies convert the returned signals into images of the tissues. The Doppler functions of this system process the Doppler shift frequencies from the echoes of moving targets (such as blood), to detect and graphically display the Doppler shifts of these tissues as flow.

The provided document is a 510(k) premarket notification for the Philips HD7 Diagnostic Ultrasound System and its associated transducers. This type of submission is for demonstrating substantial equivalence to a legally marketed predicate device, not typically for reporting performance metrics against specific acceptance criteria from a new clinical study. Therefore, much of the requested information regarding detailed study design, sample sizes, ground truth establishment, expert qualifications, and comparative effectiveness (MRMC) studies is not present in this document.

The document focuses on establishing equivalence based on:

• Intended Use: Both the predicate and the HD7 are indicated for diagnostic ultrasonic imaging and fluid flow analysis.
• Technological Characteristics: They have the same gray-scale and Doppler capabilities, use similar technologies for imaging, Doppler functions, and signal processing.
• Acoustic Output: Both have acoustic output levels below Track 3 FDA limits.
• Manufacturing and Biosafety: Both are manufactured under equivalent quality systems, of materials with equivalent biosafety, and designed/manufactured to the same electrical and physical safety standards.

The tables provided in sections {5}-{17} are "Indications for Use Forms" which describe the clinical applications and modes of operation for which the device and each transducer are indicated. They categorize indications as:

• N: New indication
• P: Previously cleared by FDA (referring to previous 510(k) clearances for the predicate device).
• E: Added under Appendix E (also referring to indications previously cleared, but specifically in an appendix related to extensions/modifications).

These tables are not performance reports, but rather statements of intended use within the scope of existing clearances.

Based on the provided text, here's what can be extracted:

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

The document does not specify quantitative acceptance criteria or detailed performance metrics. The "performance" assessment is qualitative, stating that the HD7 system and transducers function in a manner identical to other Philips ultrasound systems and transducers and are substantially equivalent to the predicate device (M2540/EnVisor ultrasound systems, cleared in K014191) in terms of safety and effectiveness.

• Identical intended use.
• Same gray-scale and Doppler capabilities.
• Essentially same technologies.
• Acoustic output below Track 3 FDA limits.
• Equivalent quality systems and biosafety materials.
• Designed and manufactured to same electrical and physical safety standards. |
  | Acoustic output levels below Track 3 FDA limits. | Acoustic output levels are below the Track 3 FDA limits. |

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

Not applicable. This 510(k) submission establishes substantial equivalence through comparison to a predicate device and engineering analysis, rather than new clinical performance data from a specific test set. No separate "test set" or data provenance information is mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. The document does not describe a clinical study requiring expert-established ground truth for a test set.

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

Not applicable. No test set or expert adjudication is described.

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 is not an AI-enabled device. No MRMC study or AI assistance is mentioned.

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

Not applicable. This is a diagnostic ultrasound system, not an algorithm, and its performance is inherently linked to human operation.

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

Not applicable. No new clinical performance study requiring a specific "ground truth" is reported in this summary. The basis for clearance is substantial equivalence to a predicate, which implies the predicate's established performance serves as the benchmark.

8. The sample size for the training set

Not applicable. No machine learning or AI component requiring a "training set" is described for this device.

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

Not applicable. See point 8.

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The M2376A DeviceLink System is indicated for use in data collection and clinical information management either directly or through networks with independent bedside devices.

The M2376A is not intended for monitoring purposes, nor is the M2376A intended to control any of the clinical devices (independent bedside devices / information systems) it is connected to.

The M2376A DeviceLink System receives digital data produced by external devices through device specific cables, converts that data into the HL7 format and transmits that information to any networked Clinical Information System.

This document describes the Philips M2376A DeviceLink System, a device intended for electronic data collection and clinical information management from external medical devices. However, the provided text does not contain information typically associated with acceptance criteria and studies proving device performance in the context of diagnostic or therapeutic medical devices.

Instead, this is a 510(k) summary filed with the FDA, primarily focusing on proving substantial equivalence to previously marketed devices. The key elements of this filing are:

• Device Description: What the DeviceLink system is and what it does (receives digital data from external devices, converts it to HL7, and transmits it to a Clinical Information System).
• Intended Use: For electronic data collection and clinical information management. Critically, it explicitly states it is not for monitoring purposes and does not control attached source devices. This clarifies its role as a data intermediary, not a diagnostic or treatment device itself.
• Predicate Devices: The 510(k) process relies on demonstrating that a new device is "substantially equivalent" to legally marketed predicate devices. The document lists several types of externally connected devices (e.g., blood pressure computers, cardiac monitors, infusion pumps) that the DeviceLink system can interface with.

Given this context, the questions you've asked about acceptance criteria and performance studies (sample size, ground truth, expert opinions, MRMC studies, standalone performance, training sets) are generally applicable to devices that make a diagnostic claim, provide a therapy, or have a direct impact on patient outcomes based on their own internal performance. The DeviceLink System, as described, is a data integration tool. Its "performance" would likely be evaluated on:

• Data Integrity: Does it accurately transmit the data received from the external devices?
• Data Format Conversion: Does it correctly convert data to HL7?
• Interoperability: Does it successfully connect and communicate with specified external devices and Clinical Information Systems?
• Reliability/Availability: Does it operate consistently without significant downtime or data loss?

The 510(k) summary provided does not detail specific studies or acceptance criteria for these types of performance metrics. The FDA's letter primarily acknowledges the review of the submission and the determination of substantial equivalence, which is a regulatory classification, not an endorsement of detailed performance study results in the common sense of device efficacy or diagnostic accuracy.

Therefore, I cannot populate the table or answer most of your detailed questions because the provided text does not contain that information for this specific type of device (a data integration system).

If this were a diagnostic AI device, for example, the information would be structured very differently, with explicit sensitivity, specificity, accuracy, and ROC curve analyses. For a data integration device, the "acceptance criteria" and "study" would likely refer to internal validation tests for data accuracy, throughput, and interoperability, which are not typically detailed in a publicly available 510(k) summary in the way you've outlined.

In summary, the provided document is a regulatory submission demonstrating substantial equivalence for a data integration device, not a performance study report for a diagnostic or therapeutic medical device.

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