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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

Study Details

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

   • Total enrolled subjects: 620
   • Subjects who completed the investigation: 573 (620 - 47 did not complete)
   • Test set size for sensitivity/specificity calculation: 202 (True Positives + False Negatives) for sensitivity; 268 (True Negatives + False Positives) for specificity. The total for these calculations is 470 subjects.
   • Data Provenance:
     • Country of Origin: Not explicitly stated, but the sponsor information lists "Samsung Research America, Mountain View, CA 94043 USA," suggesting the study was conducted within the United States.
     • Retrospective or Prospective: Prospective, as it states "A non-randomized, open-label, multi-center, single-blind study was conducted in an enriched adult population with an accredited sleep lab recruiting and enrolling subjects..."

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

   • The ground truth was established by "physician's assessment on corresponding PSG from an FDA-cleared PSG device as the clinical gold standard."
   • The text does not specify the number of physicians or their individual qualifications (e.g., years of experience, specific board certifications), only that it was a "physician's assessment." The study was conducted in an "accredited sleep lab," implying appropriately qualified professionals.

3. Adjudication method for the test set:

   • The text does not explicitly state a formal adjudication method like "2+1" or "3+1" for reading the PSG results to establish ground truth. It refers to "physician's assessment," which typically implies a clinical standard interpretation, likely by qualified sleep physicians within the accredited labs.

4. 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, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not conducted or reported. This study assessed the standalone performance of the AI device against a physician-interpreted PSG gold standard.

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

   • Yes, a standalone performance study was done. The study "compared the result from the Samsung Sleep Apnea Feature, the Device Under Test (DUT), with physician's assessment on corresponding PSG from an FDA-cleared PSG device as the clinical gold standard." This indicates the device's algorithm generated results independently, which were then compared to the ground truth.

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

   • The primary ground truth used was physician's assessment on corresponding Polysomnography (PSG) results from an FDA-cleared PSG device, referred to as the "clinical gold standard." This falls under the category of "expert assessment" based on comprehensive physiological data.

7. The sample size for the training set:

   • The development of the machine-learned algorithms utilized datasets from "over 1000 subjects, split into separate training, tuning, and testing datasets." The exact sample size specifically for the training set is not provided, only the total number of subjects for development phases.

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

   • The text states that "datasets from representative populations were utilized from over 1000 subjects" for development, and these datasets were "maintained independently from the final verification and validation activities." It is implied that the ground truth for these development datasets would have also been established using PSG or similar gold standard methods, consistent with clinical practice for sleep apnea diagnosis, but the precise methodology for establishing ground truth specifically for the training set is not detailed.

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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 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.

Note: The primary effectiveness endpoint reported (+0.12 m/s) did not explicitly meet the stated objective of "≥0.14 m/s". However, the FDA's clearance indicates that the overall evidence was sufficient for substantial equivalence. The document highlights that the exploratory endpoints showed larger improvements with the device, and that the "study subjects achieved a mean clinically significant improvement (MCID) in gait speed as measured by the 10MWT, and in walking endurance as measured by the 6MWT."

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

• Test Set Sample Size: The clinical study enrolled 53 subjects, of whom 41 completed the entire protocol. This effectively serves as the "test set" for the device's performance.
• Data Provenance: The study was conducted in the United States (Shirley Ryan AbilityLab, Northwestern University, Chicago, IL). It was a prospective, single arm, interventional, open-label, single center study.

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

This question is not applicable in the context of this device. The "ground truth" for the device's performance is objective physiological measurements (gait speed, walking endurance) and adverse event reporting, observed by medical professionals (licensed physical therapists and physicians) during rehabilitation sessions. It's not a diagnostic AI where experts label images or data for truth.

4. Adjudication Method for the Test Set

Not applicable. This is not an imaging or diagnostic study requiring adjudication of expert interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is an exoskeleton for physical assistance, not an AI for human reader enhancement.

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

This question is not directly applicable. The device itself performs its function (providing mechanical assistance), and its performance is evaluated with human-in-the-loop (the patient wearing it and being supervised by a healthcare professional). The document states the device is intended to assist ambulatory function "under the supervision of a trained healthcare professional." There is no "standalone algorithm" performance to report in this context.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance was based on:

• Objective functional mobility measures: 10-Meter Walk Test (10MWT) for gait speed and 6-Minute Walk Test (6MWT) for walking endurance. These are standardized, objective measures of physical performance.
• Adverse Event reporting: Clinical observation and documentation of any adverse events during the study.

8. The Sample Size for the Training Set

Not applicable, as this is not an AI model requiring a training set in the typical sense. The clinical study of 41 subjects who completed the protocol served as the primary evidence.

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

Not applicable for the same reason as #8. The data collected from the 41 patients in the clinical study are the direct evidence of the device's performance in a real-world (rehabilitation) setting.

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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:

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 Samsung ECG Monitor Application is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone. The app is intended to create, record, store, transfer, and display a single channel electrocardiogram (ECG), similar to a Lead I ECG, for informational use only in adults 22 years and older. Classifiable traces are labeled by the app as either atrial fibrillation (AFib) or sinus rhythm with the intention of aiding heart rhythm identification; it is not intended to replace traditional methods of diagnosis or treatment. The app is not intended for users with other known arrhythmias and users should not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.

The Samsung ECG Monitor Application consists of a pair of mobile medical apps: one on a compatible Samsung wearable and the other on a compatible Samsung phone. The compatible Samsung wearable application captures bioelectrical signals from the user and generates single lead ECG signals, calculates average heart rate and classifies the rhythm. The wearable application securely transmits the obtained data to the phone application on the paired phone device. The phone application shows the ECG measurement history and generates the PDF file for the received ECG signals which can be shared by the user.

The Samsung ECG Monitor Application was proven to be non-inferior to the predicate (Apple ECG App, DEN180044) in terms of rhythm classification accuracy and ECG signal quality sufficiency.

Here's the breakdown of the acceptance criteria and study details:

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

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

• Sample Size: 544 subjects.
  • 268 AFib patients
  • 261 Sinus Rhythm (SR) patients
  • 15 with other arrhythmias
• Data Provenance: The document does not explicitly state the country of origin. However, the manufacturer is Samsung Electronics Co., Ltd in Korea. The study structure implies prospectively collected data for this clinical validation.

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

• Number of Experts: Unspecified number of cardiologists and three blinded, independent ECG technicians.
• Qualifications of Experts:
  • Cardiologists: Used for comparing the ECG App algorithm detection of AFib and SR to 12-lead ECG reference strips, and for interpreting the ECG Monitor App strips. No specific experience level provided.
  • ECG Technicians: Three blinded, independent ECG technicians were used for fiducial point annotation. No specific experience level provided.

4. Adjudication method for the test set

• For rhythm classification, the ground truth was established by cardiologists' read of 12-lead ECG reference strips. This implies a consensus or authoritative read by these experts.
• For signal quality interpretability and concordance, cardiologists' interpretation served as the reference.
• For fiducial point annotation, three blinded, independent ECG technicians marked the points, implying their individual annotations were compared against the reference or potentially against each other for a form of consensus, though this is not explicitly detailed.

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

The document describes a clinical study where the algorithm's performance (Samsung ECG Monitor App) was compared to a reference standard (cardiologist-read 12-lead ECG), and also against a predicate device (Apple ECG App). It does not describe an MRMC comparative effectiveness study evaluating how human readers improve with AI vs without AI assistance. The focus was on the algorithm's standalone performance compared to expert ground truth and its non-inferiority to an existing cleared device.

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

Yes, a standalone study was done. The clinical study directly evaluated the Samsung ECG Monitor App algorithm's performance in detecting AFib and Sinus Rhythm against a cardiologist-read 12-lead ECG reference strip. The reported sensitivity, specificity, and inconclusive rates are for the algorithm's performance alone.

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

The primary ground truth used was expert consensus / expert interpretation from:

• Cardiologists (for rhythm classification based on 12-lead ECG reference strips and for interpretability and concordance studies).
• Blinded, independent ECG technicians (for fiducial point annotation).

8. The sample size for the training set

The document does not specify the sample size for the training set. It only details the clinical validation study (test set).

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, as it focuses solely on the clinical validation (test set) and device performance evaluation.

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The GC85A 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 GC70 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 GU60A & GU60A-65 Digital X-ray Imaging Systems are 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 GF50 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 GF50A 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 GR40CW Digital X-ray Imaging System is intended for use in general projection radiographic applications wherever conventional screen-film systems or CR systems may be used. This device is not intended for mammographic applications.

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.

GC70, GU60&GU60A-65, GF50, GF50A, GR40CW, GM85 and GC85A are 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, SAMSUNG digital X-ray operation software, and sent to the Picture Archiving & Communication System (PACS) sever for reading images.

The IPE operates, from the input image, the roles of a region-of-interest extraction, tonescale mapping, noise reduction and texture restoration. The IPE employing an advanced noise reduction algorithm (hereinafter "new IPE") is shown that the image quality of PA radiograph for average adult chest, exposed at the condition of 50% lower dose at Entrance Skin Exposure (ESE) in comparison with the condition of the conventional noise reduction algorithm (hereinafter "old IPE"), is substantially equivalent.

The provided text describes the acceptance criteria and a study proving the device meets those criteria, specifically concerning dose reduction capabilities of the Image Post-processing Engine (IPE) with an advanced noise reduction algorithm in Samsung Digital X-ray Systems (GC70, GU60A, GU60A-65, GF50, GF50A, GR40CW, GM85, and GC85A).

Here is the requested information:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The core acceptance criterion is the ability of the new IPE to reduce X-ray dose while maintaining image quality comparable to the old IPE for diagnostic confidence. The specific dose reduction percentages are the performance metrics.

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

• Adult Abdominal Radiograph Test Set:

  • Anatomical phantom images: Number of images not specified, but taken at "various exposure condition." The study states, "the new IPE with an advanced noise reduction algorithm retained the quality of images captured at 47.5% reduced exposure in comparison with the old IPE."
  • Clinical images: Number of images not specified, but used to "confirm that it was possible to reduce the dose in clinical images as well."
  • Provenance: Not explicitly stated, but the submission is from Samsung Electronics Co., LTD. Republic of Korea. The clinical testing was conducted at "one medical site."
  • Retrospective or Prospective: Not specified.

• Pediatric Population Test Set (Chest, Abdomen, Skull):

  • Number of images: "Series of dose-simulated images" for each body part.
  • Number of patients: Not specified explicitly, but mentioned as "each patient."
  • Provenance: Not explicitly stated, but the submission is from Samsung Electronics Co., LTD. Republic of Korea. The clinical testing was conducted at "one medical site."
  • Retrospective or Prospective: Not specified.

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

• Adult Abdominal Radiograph Test Set:

  • Anatomical phantom images were reviewed by three professional radiologists.
  • Clinical images were reviewed by two professional radiologists.
  • Qualifications: "Professional radiologists" (no further details on experience given).

• Pediatric Population Test Set:

  • Three experienced pediatric radiologists.
  • Qualifications: "Experienced pediatric radiologists" (no further details on experience given).

4. Adjudication Method for the Test Set

The adjudication method is not explicitly detailed. However, for both adult and pediatric studies, images were "scored by the 5-point grading scale" for assessment of image quality. This implies individual scoring, and for the pediatric study, "Three experienced pediatric radiologists assessed the series of dose-simulated images to decide the optimal dose for each patient." The decision for the "optimal dose" for pediatric cases suggests a consensus or agreement among these experts, but the exact method (e.g., majority vote, discussion to reach consensus) is not specified.

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

No MRMC comparative effectiveness study was done to evaluate human readers' improvement with AI vs. without AI assistance. The study focused on the device's standalone performance in enabling dose reduction while maintaining image quality as assessed by human readers. The new IPE is a component within the imaging system, not an AI assistance tool for human readers.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, the studies evaluated the standalone performance of the new IPE algorithm in terms of enabling dose reduction while maintaining image quality. The performance was assessed by comparing images processed by the new IPE at reduced doses against images from the old IPE or a reference, with human experts providing the assessment of image quality and diagnostic appropriateness.

7. The Type of Ground Truth Used

The ground truth for both adult and pediatric studies was expert consensus/assessment of image quality and diagnostic appropriateness.

• For adult abdominal radiographs: Expert radiologists scored images based on a 5-point grading scale, considering anatomical regions, physical parameters, sharpness, and visualization.
• For pediatric populations: Experienced pediatric radiologists assessed dose-simulated images to determine the "optimal dose" at which image quality remained appropriate for diagnosis.

Additionally, phantom studies (TOR CDR radiography phantom and anthropomorphic phantom) were used to quantitatively assess image quality metrics like Contrast to Noise Ratio (CNR), Detail Compacted Contrast (DCC), and Modulation Transfer Functions (MTF).

8. The Sample Size for the Training Set

The document does not provide information about the training set size for the Image Post-processing Engine (IPE) algorithm. It focuses on the validation of the algorithm's dose reduction capabilities.

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 for the IPE algorithm.

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