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The GM85 Digital Mobile X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GM85 Digital Mobile X-ray Imaging System is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process on the S-Station, which is the Operation Software (OS) of Samsung Digital Diagnostic X-ray System, and save in DICOM file, a standard for medical imaging. The captured images are tuned up by an Image Post-processing Engine (IPE) which is exclusively installed in S-Station, and send to the Picture Archiving & Communication System (PACS) sever for reading images.

The GM85 Digital Mobile X-ray imaging System was previously cleared with K222353, and through this premarket notification, we would like to add more configurations in the previously cleared GM85 as a detector, accessories are newly added and software is updated for user convenience.

The new detector added in the proposed device is designed to achieve a higher IP rating of Dust and Water and reduce weight while maintaining durability, functionality and operation like the detector of the predicate device. The new detector and predicate device's detector are both an x-ray conversion device using an amorphous silicon flat panel and absorb incident x-rays, converts it to a digital signal, and then transmits it to the Samsung Digital X-ray System like that of the predicate device.

The provided text is a 510(k) Premarket Notification for the GM85 Digital Mobile X-ray Imaging System. It describes changes made to a previously cleared device (K222353) and argues for substantial equivalence.

Based on the provided text, the device is the GM85 Digital Mobile X-ray Imaging System. It is an X-ray imaging system, and the study focuses on the performance of a new detector (F4343-AW) and other accessories and software updates compared to the predicate device, also named GM85.

Here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state "acceptance criteria" in a quantitative format for specific imaging metrics. Instead, it focuses on demonstrating that the proposed device, with its new detector, accessories, and software, is substantially equivalent to the predicate device (GM85, K222353). The performance is assessed by comparing technical specifications and qualitative evaluation by experts.

The key performance characteristics and comparisons are as follows:

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

• Test Set Sample Size: The document refers to "Anthropomorphic phantom images" but does not specify the number of phantom images used for evaluation. It also notes that clinical data was not required.
• Data Provenance: The study is non-clinical. The "Anthropomorphic phantom images" would have been generated in a controlled testing environment, likely at the manufacturer's facility. The country of origin of the data is implicitly South Korea, where SAMSUNG ELECTRONICS Co., Ltd. is located. The study is a prospective evaluation of the new detector and modified system against the predicate.

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

• Number of Experts: The document states that phantom images "were evaluated by professional radiologists." It does not specify the number of radiologists who participated in this evaluation.
• Qualifications of Experts: The experts are described as "professional radiologists." No further details on their experience level (e.g., years of experience, subspecialty) are provided.

4. Adjudication Method for the Test Set

The document states that phantom images "were evaluated by professional radiologists and found to be equivalent to the predicate devices" and that there was "no significant difference in the average score of image quality evaluation." This suggests a comparative scoring or assessment. However, the specific adjudication method (e.g., consensus, majority vote, independent reads with statistical comparison) is not described.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly done, nor was there a "human readers improve with AI vs without AI assistance" component. The study for substantial equivalence focused on comparing the image quality of the proposed device (with new detector) against the predicate device using phantom images, evaluated by radiologists. The purpose was to show equivalence of the device's output, not the improvement of human readers with AI assistance.

6. Standalone Performance Study (Algorithm Only)

The provided text describes the device as a "Digital Mobile X-ray Imaging System," which includes hardware (X-ray generator, detector) and software for image processing (IPE, S-Station). The evaluation primarily focuses on the entire system's ability to generate radiographic images with equivalent quality to the predicate, particularly with the new detector.

While software features are mentioned (S-Share, S-Enhance, SimGrid, PEM, QAP, Bone Suppression, Remote View, Mirror View, RFID, Value-up Package), and software was updated for user convenience, the study does not report a standalone algorithm-only performance (without human-in-the-loop performance) in terms of diagnostic accuracy or reader improvement for specific diagnostic tasks. The "phantom image evaluation" evaluates the quality of the images produced by the overall system, not an AI algorithm's diagnostic output.

7. Type of Ground Truth Used

The ground truth for the phantom image evaluation was established based on expert consensus/evaluation of image quality metrics. The "professional radiologists" evaluated the anthropomorphic phantom images. This is not pathology, nor outcomes data.

8. Sample Size for the Training Set

The document does not provide information on the sample size for the training set. Training data would typically be applicable if this were an AI/CADe device with a specific machine learning model for diagnostic tasks, which is not the primary focus of this 510(k) for a basic X-ray imaging system with incremental changes. The "Image Post-processing Engine (IPE)" and features like "Bone Suppression" would have been developed using some form of training data previously, but details are absent here for this particular submission.

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

As no training set sample size is provided, the method for establishing ground truth for a training set is not mentioned in this document.

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The GF85 Digital X-ray Imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GF85 digital X-ray imaging system is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process on the S-station, which is the Operation Software (OS) of Samsung Digital Diagnostic X-ray System, and save in DICOM file, a standard for medical imaging. The captured images are tuned up by an Image Post-processing Engine (IPE) which is exclusively installed in S-station, and sent to the Picture Archiving & Communication System (PACS) sever for reading images. The GF85 Digital X-ray Imaging System includes two models, namely the GF85-3P and GF85-SP. These two models differ primarily in terms of their respective configurations of High Voltage Generators (HVGs). Specifically, the GF85-3P features capacities of 80 kW, 65 kW and 50 kW with 3 phases, whereas the GF85-SP provides a capacity of 40 kW with a single phase. However, all other specifications remain consistent across both models.

The provided text describes the GF85 Digital X-ray Imaging System and its substantial equivalence to predicate devices (GC85A and GM85). The key study mentioned for demonstrating equivalence is a non-clinical phantom image evaluation.

Here's a breakdown of the requested information based on the provided text, with "N/A" for information not present:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a table format. Instead, it relies on a comparison with predicate devices and a general statement that "All test results were satisfying the standards" and that the phantom image evaluations "found to be equivalent to the predicate device."

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

The document states "The phantom image evaluations were performed...".

• Sample size for test set: The document does not specify the number of phantom images used in the evaluation.
• Data provenance: Phantom images are synthetic data, not from human subjects. The country of origin for the data is not specified, but the submission is from Samsung Electronics Co., Ltd. in South Korea.

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

The document states, "...and were evaluated by three different professional radiologists..."

• Number of experts: Three.
• Qualifications of experts: "professional radiologists." Specific experience levels (e.g., "10 years of experience") are not provided.

4. Adjudication method for the test set

The document states the images "were evaluated by three different professional radiologists and found to be equivalent to the predicate device." It does not specify a formal adjudication method (e.g., 2+1, 3+1 consensus). It implies a collective agreement on equivalence.

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. The study conducted was a non-clinical phantom image evaluation to compare the device's image quality to a predicate device, as evaluated by radiologists. It was not a comparative effectiveness study involving human readers' performance with and without AI assistance.
• Effect size of human reader improvement with AI: N/A, as no such study was conducted or reported.

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

• The primary evaluation described (phantom image reviews) involved human radiologists assessing images generated by the device. The device itself is an X-ray imaging system, not an AI algorithm performing diagnostic interpretation. While the software features list "Lunit INSIGHT CXR Triage" as a new feature for the GF85, the provided text does not detail any standalone performance study specifically for this AI component or any other algorithmic component of the GF85. The "phantom image evaluations" are about the device's image quality, not an algorithm's diagnostic performance.

7. The type of ground truth used

For the phantom image evaluation, the ground truth is implicitly defined by the known characteristics and standards of the phantom images themselves, as well as the comparison against the predicate device. It is not expert consensus on pathology, or outcomes data.

8. The sample size for the training set

• Training set sample size: N/A. The document describes a traditional X-ray imaging system and its non-clinical evaluation for substantial equivalence. It does not refer to a machine learning model's training set. While there are "Software Features" like "Lunit INSIGHT CXR Triage" which would involve AI, the document does not discuss their training data.

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

• Ground truth for training set: N/A, as no training set for a machine learning model is directly discussed for the GF85 system itself in the context of its substantial equivalence.

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The Samsung ECG app with IHRN is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the feature is intended to opportunistically acquire pulse rate data when the user is still and analyze the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single channel ECG, similar to a Lead I ECG. Classifiable traces are labeled by the app as sinus rhythm. AFib. high heart rate (non-AFib), or AFib with high heart rate with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.

The Samsung ECG App v1.3 is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor cardiac signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Acceptance Criteria and Device Performance for Samsung ECG App v1.3

1. Acceptance Criteria and Reported Device Performance

Note: The reported performance for Samsung ECG App v1.3's "Sinus rhythm (HR 50-150 BPM)" and "AFib (HR 50-150 BPM)" is presented with the same 95% CI: (94.0%, 97.8%). This might be a transcription error in the document, as specificity and sensitivity for different conditions would typically have distinct confidence intervals. Assuming independent calculations, these values are presented as they appear in the source.

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

• Sample Size: 1,013 subjects. These subjects contributed to 453 AFib recordings (heart rate 50 to 150 BPM) and 691 Sinus rhythm recordings (heart rate 50 to 150 BPM) for the primary endpoint analysis.
• Data Provenance: The study was a multi-center study, implying data from multiple locations, likely within the US given the FDA submission context and the racial demographics provided (predominantly Caucasian). The study was likely prospective as it involved recruiting subjects and collecting data for validation.

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

The document does not explicitly state the "number of experts" or their specific "qualifications" used to establish the ground truth for the test set. It mentions "Clinical Validation showing comparable clinical performance...compared to the reference device" and that the "ECG function accurately classified...compared against the standard Lead I ECG," implying that comparison was made to physician-adjudicated or expertly interpreted ECGs, but the details of the ground truth establishment are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing the ground truth for the test set.

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

There is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study being done, or any effect size of how much human readers improve with AI vs without AI assistance. The study focuses on the standalone performance of the device's ECG rhythm classification compared to a reference device.

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

Yes, a standalone study was conducted. The "Clinical Validation" section details the performance of the "ECG rhythm classification of the Samsung ECG App v1.3" in terms of sensitivity and specificity against a clinical ground truth, without explicit human-in-the-loop interaction for the classification task itself. The device "accurately classified" recordings.

7. Type of Ground Truth Used

The ground truth used was clinical diagnosis based on "446 subjects diagnosed with AFib, 536 subjects without AFib, and 31 subjects diagnosed with another type of irregular rhythm." The performance was evaluated by comparing the device's classifications against "standard Lead I ECG" interpretation, implying expert consensus (from qualified healthcare professionals interpreting the standard ECGs) or clinical diagnosis as the ground truth.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It focuses on the validation study.

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

The document does not provide information on how the ground truth for the training set was established.

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The Sleep Apnea Feature is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone.

This feature is intended to detect signs of moderate to severe obstructive sleep apnea in the form of significant breathing disruptions in adult users 22 years and older, over a twonight monitoring period. It is intended for on demand use.

This feature is not intended for users who have previously been diagnosed with sleep apnea. Users should not use this feature to replace traditional methods of diagnosis and treatment by a qualified clinician. The data provided by this device is also not intended to assist clinicians in diagnosing sleep disorders.

The Samsung Sleep Apnea Feature leverages wrist-worn PPG and actigraphy technology to create an over-the-counter (OTC) assessment of moderate-to-severe obstructive sleep annea for adults. When enabled, the device utilizes the wearable platform's PPG-derived SpO2 to monitor the user's sleep for repetitive, relative decreases in their blood oxygenation indicative of significant breathing disruptions associated with sleep apnea. Each on-demand assessment period requires two successful nights of observation within 10 days. After two qualifying assessment nights. the device will display the result on the wearable, after which, the user is guided to the phone for additional information. This provides the user with health information so that they may seek out medical attention. No raw signal data, including the SpO2 signal, is provided to the user nor is it able to be shared with clinicians.

The Samsung Sleep Apnea Feature consists of two mobile medical applications, one on the wearable (e.g., Samsung Galaxy Watch) and the other on the connected mobile phone (e.g., Samsung Galaxy Phone), both commercial off-the-shelf general computing platforms. Communications between the two devices are accomplished by encrypted Bluetooth/BLE connection via standard protocols for data transfer. The wearable component of the Sleep Apnea Feature runs in the wearable's operating system allowing it to verify the identification/qualification of the hardware, request SpO2/accelerometer signals via private APIs, display information on the screen display, and send data and receive commands to the phone Sleep Apnea Feature on the associated phone. The phone component of the Sleep Apnea feature provides a UI for onboarding, labeling, and status as well as the ability for device updates.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

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The Samsung ECG Monitor Application with Irregular Heart Rhythm Notification is an over-the-counter (OTC) softwareonly, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present, rather the feature is intended to opportunistically acquire pulse rate data when the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single-channel ECG. similar to a Lead I ECG. Classifiable traces are labeled by the app as either AFib or sinus rhythm with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device of the of a qualified healthcare professional.

The Samsung ECG Monitor App with Irregular Heart Rhythm Notification (IHRN) Feature is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor bio-photonic signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone device. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Here's a breakdown of the acceptance criteria and the study details for the Samsung ECG Monitor Application with Irregular Heart Rhythm Notification Feature, based on the provided text:

Acceptance Criteria and Device Performance

The document states that Samsung's algorithm performance for the IHRN function is substantially equivalent to the predicate device (Apple IRN Feature DEN180042) at both subject and tachogram levels, indicating these reported values met the acceptance criteria. For the ECG function, the device inherited the performance from the previously cleared Samsung ECG Monitor App (K201168) and thus the reported values were assumed to meet their prior acceptance criteria.

Study Details

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

• IHRN Clinical Validation (PPG-based notification):

  • Analyzable Dataset for primary and secondary endpoints: 810 subjects (from 888 enrolled).
  • Tachogram-level assessment: 98 subjects with AFib episodes (over an hour) and 101 subjects with less than an hour of AFib or no AFib were randomly selected from the cardiologist-reviewed subjects. Up to 25 positive tachograms with reference ECG data were randomly selected from these subjects.
  • Data Provenance: The document does not explicitly state the country of origin, but it is a clinical study. The phrasing "All recruited subjects were at risk for AFib and had experienced symptoms..." suggests prospective data collection.

• ECG Function (on-demand):

  • No new clinical, human factors, or ECG database tests were conducted as the function was unchanged from the K201168 clearance. Therefore, a new test set was not used for this specific clearance.

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

• IHRN Clinical Validation:
  • Subject-level ground truth: "clinician-adjudicated and cardiologist-reviewed patch ECG data." The exact number of clinicians/cardiologists for this overarching adjudication is not specified, but it implies multiple experts.
  • Tachogram-level ground truth: "Two board-certified cardiologists reviewed each reference ECG for annotation with a third cardiologist serving as tie-breaker."
  • Qualifications: "Board-certified cardiologists."

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

• Tachogram-level ground truth: 2+1 (Two board-certified cardiologists reviewed, with a third serving as a tie-breaker).
• Subject-level ground truth: Not explicitly stated as a specific numerical method (e.g., 2+1), but referred to as "clinician-adjudicated and cardiologist-reviewed," implying a consensus or expert-driven process.

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 involving human readers with and without AI assistance was mentioned or conducted. The study evaluated the device's performance (IHRN feature) against a clinical ground truth, not the improvement of human readers using the device.

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

• Yes, the clinical validation study for the Irregular Heart Rhythm Notification (IHRN) feature primarily assesses the standalone performance of the PPG-based algorithm in identifying irregular rhythms and generating notifications. The "subject-level irregular rhythm notification accuracy" and "tachogram-level positive predictive value" are metrics of the algorithm's performance without direct human interpretation being part of the primary output.

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

• IHRN Clinical Validation: Expert consensus using reference ECG patch data reviewed and adjudicated by clinicians and board-certified cardiologists.

8. The sample size for the training set:

• The document does not specify the sample size for the training set. It focuses on the validation study.

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

• The document does not specify how the ground truth for the training set (if any) was established. It only details the ground truth establishment for the test/validation set.

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The GM85 Digital Mobile X-ray imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GM85 Digital Mobile X-ray Imaging System is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process on the S-Station, which is the Operation Software (OS) of Samsung Digital Diagnostic X-ray System, and save in DICOM file, a standard for medical imaging. The captured images are tuned up by an Image Post-processing Engine (IPE) which is exclusively installed in S-Station, and send to the Picture Archiving & Communication System (PACS) sever for reading images.

The provided text describes a 510(k) premarket notification for the GM85 Digital Mobile X-ray Imaging System. The primary purpose of this notification is to add a new detector configuration to an already cleared device (K220175). As such, the study described focuses on demonstrating the substantial equivalence of the new detector and its integration, rather than establishing primary performance metrics from scratch for a completely new device.

Here's a breakdown of the acceptance criteria and the study conducted based on the provided information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to demonstrating equivalence to the predicate device's existing performance, particularly for the new detector. The non-clinical and clinical (phantom image evaluation) data serve to show that the new detector maintains similar performance characteristics.

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

• Sample Size for Test Set: The document mentions "Anthropomorphic phantom images were provided." It does not specify the exact number of phantom images or the number of distinct phantom cases used in the evaluation.
• Data Provenance: The nature of phantom images means the data is synthetic/simulated clinical data (using phantoms to mimic human anatomy) rather than human patient data. The country of origin for the data is not explicitly stated, but it would have been generated as part of the manufacturer's internal testing as advised by FDA guidance. The study is prospective in the sense that the evaluations were specifically conducted for this submission.

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

• Number of Experts: The document states that phantom images "were evaluated by professional radiologists." It does not specify the exact number of radiologists involved.
• Qualifications of Experts: They are described as "professional radiologists." Specific lengths of experience or subspecialties are not provided.

4. Adjudication Method for the Test Set

• The document implies a consensus or comparison approach: "They were evaluated by professional radiologists and found to be equivalent to the predicate devices. There is no significant difference in the average score of image quality evaluation between the proposed device and the predicate device." However, a specific adjudication method (e.g., 2+1, 3+1 decision rule) is not explicitly stated. It seems to be a comparative evaluation aiming for no significant performance degradation.

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. This submission focuses on hardware modification (new detector) to an existing device, not an AI-assisted diagnostic tool. Therefore, the concept of improving human readers with AI assistance is not applicable here.

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

• This is not an AI/algorithm-only device. The GM85 is an X-ray imaging system. The "standalone" performance here refers to the device's ability to produce images with acceptable quality on its own. Non-clinical data (MTF, DQE measurements) and phantom images contribute to demonstrating this standalone image performance. The evaluation by radiologists of the phantom images is more about confirming the quality of the "output" of the device in a clinical context, rather than evaluating an algorithm's diagnostic capability.

7. The Type of Ground Truth Used

• For the phantom image evaluation, the "ground truth" is established by the known characteristics of the anthropomorphic phantoms. The purpose of the radiologists' evaluation is to determine if the images produced by the new detector (compared to the predicate's detector) adequately represent these known phantom structures and maintain diagnostic quality. In essence, the ground truth is the known physical properties of the phantom, and the evaluation confirms the device's ability to render these properties accurately in images.

8. The Sample Size for the Training Set

• This submission describes a modification to an existing medical device's hardware (a detector), not an AI or machine learning algorithm. Therefore, there is no "training set" in the context of AI model development that would typically be described here. The device itself is "trained" or designed through engineering and validated through performance testing.

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

• As there is no AI training set as commonly understood in this context, the concept of establishing ground truth for a training set does not apply. The device's design and engineering would be based on established physics principles of X-ray imaging and medical imaging standards.

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The GM85 Digital Mobile X-ray imaging System is intended for use in generating radiographic images of human anatomy by a qualified/trained doctor or technician. This device is not intended for mammographic applications.

The GM85 Digital Mobile X-ray Imaging System is used to capture images by transmitting X-ray to a patient's body. The X-ray passing through a patient's body is sent to the detector and then converted into electrical signals. These signals go through the process of amplification and digital data conversion in the signal process on the S-Station, which is the Operation Software (OS) of Samsung Digital Diagnostic X-ray System, and save in DICOM file, a standard for medical imaging. The captured images are tuned up by an Image Post-processing Engine (IPE) which is exclusively installed in S-Station, and send to the Picture Archiving & Communication System (PACS) sever for reading images.

The GM85 Digital Mobile X-ray imaging System was previously cleared with K181626, and through this premarket notification, we would like to add more configurations in the previously cleared GM85 as an optional collapsible column type with a manual collimator, a tube, four detectors, and exposure switches (two wired and one wireless types) are optionally added, and software including the Image Post-processing Engine (IPE) is changed in order to support new hardware and apply new software features.

The provided document is a 510(k) Premarket Notification for the Samsung GM85 Digital Mobile X-ray Imaging System. It describes the updated device and compares it to a legally marketed predicate device (K181626, also GM85). The document primarily focuses on demonstrating substantial equivalence rather than presenting detailed acceptance criteria and a comprehensive study for de novo device performance.

However, based on the information provided, here's an attempt to extract and synthesize what is available regarding acceptance criteria and the supporting study, focusing on the changes made to the device:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with numerical targets and corresponding device performance for the overall system in a typical format found in clinical studies. Instead, it describes differences and then asserts that these differences do not "contribute any adverse impact to the device's safety and effectiveness" or "do not impact safety and/or effectiveness."

The closest to acceptance criteria are the statements made about the new detectors and software features, aiming for equivalent or improved performance compared to the predicate device.

2. Sample Size for Test Set and Data Provenance

• Sample Size for Test Set: Not explicitly stated as a numerical sample size for "cases" in the traditional sense of a clinical study. The document mentions "Anthropomorphic phantom images were provided." The number of phantom images or specific phantom types isn't detailed. For software elements, "Software System Test Case for verification and validation" was performed, but no numerical count is given.
• Data Provenance: The study used "Anthropomorphic phantom images" and non-clinical data (MTF, DQE measurements, Software System Test Cases). This indicates testing in a controlled environment (laboratory/phantom studies). The country of origin for the data is not specified, but the manufacturer is based in the Republic of South Korea. The data is non-clinical/phantom-based, so it's not strictly "retrospective or prospective" in the human clinical trial sense.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: "A professional radiologist" (singular)
• Qualifications of Experts: "professional radiologist" (no specific years of experience or subspecialty mentioned beyond being a radiologist).

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or not specified. A single "professional radiologist" evaluated the phantom images, implying no consensus or multi-reader adjudication process as there was only one reviewer mentioned.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated or described. The evaluation involved a single radiologist reviewing phantom images.
• Effect Size: Not applicable, as no MRMC study was conducted.

6. Standalone (Algorithm only without human-in-the-loop performance) Study

• Standalone Study: Yes, performance data such as MTF and DQE measurements (IEC 62220-1) are inherent standalone technical performance metrics of the detectors. Software System Test Cases for verification and validation would also be considered standalone testing. The "anthropomorphic phantom images" evaluation by a single radiologist could be considered a form of standalone performance evaluation for the system's image quality output, as it assesses the device's generated images directly.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the phantom image evaluation was implicitly the expected characteristics and quality of images of anthropomorphic phantoms, as assessed by a professional radiologist for equivalence to predicate devices. For technical metrics like MTF and DQE, the ground truth is against the measurement standards (IEC 62220-1). There was no pathology or outcomes data.

8. Sample Size for the Training Set

• Sample Size for Training Set: The document does not provide information regarding the sample size of a training set for any machine learning components. While the device includes an "Image Post-processing Engine (IPE)" and features like "SimGrid," if these involve learned algorithms, their training data is not discussed. This submission is for an X-ray system, and the focus is on hardware and general image processing functionality rather than an AI/CADe device where training data would typically be detailed.

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

• How Ground Truth for Training Set was Established: Not applicable or not provided, as training set details are absent.

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The GEMS-H is a robotic exoskeleton that fits orthotically on the wearer's waist and thighs, outside of clothing. The device is intended to help assist ambulatory function in rehabilitations under the supervision of a trained healthcare professional for the following population:

· Individuals with stroke who have gait deficits and exhibit gait speeds of at least 0.4 m/s and are able to walk at least 10 meters with assistance from a maximum of one person.

The trained healthcare professional must successfully complete a training program prior to use of the device. The device is not intended for sports.

The GEMS-H is a lightweight, robotic exoskeleton designed to help assist ambulatory function of stroke patients who meet the assessment criteria, in rehabilitations under the supervision of a trained healthcare professional. The GEMS-H device provides assistance to the patient during hip flexion and extension.

The device is worn over clothing around the wearer's waist and fastened with Velcro straps to assists hip flexion and extension. The device weighs 4.7 lbs (2.1 kg) and has two motors that run on a single rechargeable battery. The device is equipped with joint angle and electrical current sensors to monitor hip joint angle and torque output, respectively.

The assist torque is transmitted to the wearer's thighs via thigh support frames. A trained healthcare professional, who operates the device, can change assist settings through software that runs on the tablet PC.

This document describes the premarket notification (510(k)) for the Samsung GEMS-H, a powered lower extremity exoskeleton. The information provided primarily focuses on establishing substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria related to an AI's performance.

Based on the provided text, the device itself (GEMS-H exoskeleton) is the subject of the regulatory review, and the "study" described is a clinical trial to assess its safety and effectiveness in assisting ambulatory function in stroke patients. There is no mention of an AI component requiring specific performance acceptance criteria for an algorithm or model.

Therefore, many of the requested points regarding AI acceptance criteria, ground truth establishment, expert adjudication, and MRMC studies are not applicable directly to this document's content, as it's not about an AI-powered diagnostic or predictive device.

However, I can extract information related to the device's clinical performance and the study design:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the device are defined in terms of safety and effectiveness, based on a clinical trial.

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The CUBE Air Purifier is a device intended for medical purposes that is used to destroy bacteria and viruses in the air by exposure to ultraviolet radiation.

The CUBE Air Purifier has been demonstrated to destroy the following MS2 bacteriophage, Staphylococus epidermidis, Escherichia coli entrained on the filter of the under the following exposure conditions:

| Organisms | Name | Average Maximum log reduction/
exposure time (hours) | |
|-----------|----------------------------|---------------------------------------------------------|--|
| | | Room temperature test | |
| Virus | MS2 bacteriophage | 5.33±0.23 /60 mins | |
| Virus | Phi-X174 bacteriophage | 5.34±0.11 /60 mins | |
| Bacteria | Staphylococcus epidermidis | 5.36±0.28 /60 mins | |
| Bacteria | Escherichia coli | 5.17±0.05 /60 mins | |

The CUBE Air Purifier employs a photocatalytic oxidation (PCO) ultraviolet air purification technology that destroys bacteria and viruses in air in medical facilities. The CUBE Air Purifier includes a pre-filter, a dust collecting filter, UV-A LED lights (320-400 nm), and a catalytic filter.

The device is intended to be placed in medical and healthcare facilities.

The provided text describes the acceptance criteria and the study that proves the CUBE Air Purifier meets those criteria. Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document describes non-clinical performance testing conducted in a controlled laboratory environment. The "sample size" here refers to the number of tests performed on the device itself, rather than human subjects or a dataset of medical images.

• Sample Size for performance testing: The document does not explicitly state the number of repetitions for each performance test (e.g., how many times the aerosolized bacteria/virus reduction was measured). It typically presents average results with standard deviations, implying multiple runs were conducted.
• Data Provenance: The studies were conducted as part of the device's premarket notification (510(k)) submission to the FDA. This indicates that the data was generated specifically for regulatory purposes in a controlled laboratory setting (e.g., environmental bioaerosol chamber). The country of origin for the studies is not explicitly stated, but the submitter (Samsung Electronics Co., Ltd.) is based in South Korea. The studies are prospective in nature, as they involve actively testing the device's performance under specified conditions.

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

This information is not applicable (N/A) to this type of device (air purifier). The "ground truth" for air purifiers is established through direct laboratory measurements of microbial reduction and filter efficiency, not through human expert interpretation of data like in medical imaging.

4. Adjudication Method for the Test Set

This is not applicable (N/A). Adjudication methods (e.g., 2+1, 3+1 consensus) are typically used in studies involving human interpretation (like in diagnostic imaging studies) to resolve disagreements among readers. For an air purifier, the "ground truth" is determined by objective scientific measurements in a lab.

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

This is not applicable (N/A). MRMC studies are relevant for diagnostic devices where human readers (e.g., radiologists) interpret cases (e.g., medical images), often with or without AI assistance. This document describes the performance of an air purifier in reducing airborne pathogens, not a diagnostic tool requiring human interpretation.

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

This concept is not directly applicable in the context of an air purifier. The "device performance" is the standalone performance. The machine itself is performing the intended function (destroying bacteria and viruses). There isn't an "algorithm only" that then needs to be compared to a human-in-the-loop performance, as the device's function is purely mechanical and chemical (UV-A and photocatalytic oxidation). The tests performed (e.g., log reduction of microbes in a chamber) are measures of its standalone efficacy.

7. The Type of Ground Truth Used

The ground truth used in these studies is based on direct laboratory measurements of microbial viability and particle count reduction.

• For microbial reduction: The ground truth is the measured decrease in the concentration of specific bacteria and viruses (MS2 bacteriophage, Phi-X174 bacteriophage, Staphylococcus epidermidis, Escherichia coli) in a sealed chamber after exposure to the CUBE Air Purifier. This is a quantitative outcome data obtained through standard microbiological assay techniques.
• For filter efficiency: The ground truth is the measured percentage of particles (0.3 to 1.0 micron size) removed by the filter, according to ASHRAE 52.2 standards, which is a performance standard/outcome data.

8. The Sample Size for the Training Set

This is not applicable (N/A). The CUBE Air Purifier is a physical device employing UV and photocatalytic oxidation for air purification. It is not an AI/ML-based device that requires a "training set" of data to learn or develop an algorithm. Its performance is based on established physical and chemical principles.

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

This is not applicable (N/A), as there is no "training set" described for this device.

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The Auto Lung Nodule Detection is computer-aided detection software to identify and mark regions in relation to suspected pulmonary nodules from 10 to 30 mm in size. It is designed to aid the physician to review the PA chest radiographs of adults as a second reader and be used as part of S-Station, which is operation software installed on Samsung Digital X-ray Imaging systems. Auto Lung Nodule Detection cannot be used on the patients who have lung lesions other than abnormal nodules.

Auto Lung Nodule Detection is a Computer-Aided Detection (CADe) device that is designed to perform CAD processing in Chest X-ray images for indication of locations for high nodule probability, which has an effective detection sizes from 10 mm to 30 mm.

Auto Lung Nodule Detection receives images acquired with SAMSUNG Digital X-ray Imaging Systems as an input and identifies suspected nodules, and then sends information of suspected nodules to the visualization part of S-Station, which is installed on all kinds of SAMSUNG Digital X-ray Imaging Systems, to generate output images with circular marks. The CAD performed images, are displayed on the screen by S-Station without defeat of original images and used as a second reader only after the initial read is completed.

Here's a breakdown of the acceptance criteria and study details for the Auto Lung Nodule Detection device, based on the provided FDA 510(k) Premarket Notification:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes the clinical study's objective as demonstrating that the device improves human readers' nodule detection performance. While specific numerical acceptance criteria (e.g., minimum sensitivity, maximum FPPI) are not explicitly called out in a "table of acceptance criteria," the clinical performance testing section clearly states that the results have demonstrated that all readers' nodule detection performances using the proposed device have increased with statistical significance. This implicitly defines the acceptance criteria: a statistically significant improvement in nodule detection performance metrics.

2. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated. The document mentions "a test dataset containing both normal and diseased images."
• Data Provenance: Not explicitly stated (e.g., country of origin). The study is described as a "clinical evaluation," implying real patient data. It is not specified if the data was retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated.
• Qualifications of Experts: Not explicitly stated. However, given the context of a CADe device for radiological use, these would typically be board-certified radiologists or pulmonary specialists.

4. Adjudication Method for the Test Set

The document mentions that "Readers were asked to mark their region of nodule suspicion on the images while also providing confidence scores on each decision they have made." It then states, "After independent reading, readers were allowed to adjust their confidence scores after reviewing the ALND's detection results." This describes a workflow, but does not explicitly describe the ground truth adjudication method (e.g., consensus reading by multiple experts, pathology confirmation). The term "independent reading" before ALND review suggests individual reads prior to any potential adjudication or consensus if it occurred.

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

Yes, an MRMC comparative effectiveness study was conducted.

• Effect Size of Improvement: The document states, "The results have demonstrated that all readers' nodule detection performances using the proposed device have increased with statistical significance." While specific numerical effect sizes (e.g., percentage increase in sensitivity, reduction in FPPI, or change in JAFROC FOM) are not provided, the key finding is the statistical significance of the improvement when radiologists used ALND as an assistant tool.

6. Standalone (Algorithm Only) Performance

The document does not explicitly report standalone performance of the algorithm without human-in-the-loop. The clinical evaluation focuses on the human-in-the-loop performance (with ALND assistance vs. without).

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated. It can be inferred that it involved expert consensus or a gold standard based on follow-up, but the document does not specify if it was pathology, follow-up imaging, or expert consensus.

8. Sample Size for the Training Set

The sample size for the training set is not provided in this document.

9. How Ground Truth for the Training Set was Established

The method for establishing ground truth for the training set is not provided in this document.

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