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The OASIS MRI System is an imaging device, and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross-sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-lattice relaxation time (T), spin-spin relaxation time (T2), and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

The OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

The provided document is a 510(k) Premarket Notification from Hitachi Healthcare Americas for their OASIS MRI System. It primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (OASIS MRI System K202030) rather than presenting a detailed clinical study with acceptance criteria for a new device's performance.

Therefore, the document does not contain details about acceptance criteria or a study that specifically proves the device meets those criteria in the way typically expected for an AI/ML medical device.

However, I can extract information related to performance evaluation and testing that was conducted to support the substantial equivalence claim.

Here's an analysis based on the provided text:

No specific acceptance criteria and detailed study proving direct device performance against those criteria are provided in the document. The document primarily focuses on demonstrating substantial equivalence to a predicate device by evaluating changes and ensuring they do not affect safety or effectiveness.

Here's what can be extracted regarding the type of performance evaluation done:

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, no explicit table of acceptance criteria with corresponding device performance metrics is provided in the document for the new features. The evaluation is primarily framed in terms of demonstrating that new features perform as intended and do not raise new questions of safety or effectiveness compared to the predicate device.

The document states:

• "Performance bench testing was conducted on the applicable new features. Test data confirmed that each new feature perform as intended for diagnostic use."
• "Clinical image examples are provided for each applicable new feature and or coil that we judged to be sufficient to evaluate clinical usability."
• "Clinical images were collected and analyzed, to ensure that images from the new feature meet user needs."

These are qualitative statements about performance rather than quantitative acceptance criteria.

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

• Test Set Sample Size: Not specified. The document states "Clinical image examples are provided for each applicable new feature..." This suggests a qualitative review of examples rather than a statistically powered study with a defined sample size.
• Data Provenance: Not specified. The context implies these are images generated by the OASIS MRI system itself, but no details about the patient population, imaging sites, or whether the data is retrospective or prospective are given.

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

• Number of Experts: Not specified. The evaluations were "judged to be sufficient to evaluate clinical usability" and to "meet user needs," implying expert review, but the number or specific roles of these experts are not detailed.
• Qualifications of Experts: Not specified. The indications for use state that "When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination." This indirectly suggests that the "judges" and "users" are likely trained physicians or radiologists, but their specific qualifications (e.g., years of experience, subspecialty) are not mentioned.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the qualitative nature of the review ("judged to be sufficient," "meet user needs"), it's likely a consensus-based or individual expert assessment rather than a formal adjudicated process (e.g., 2+1, 3+1).

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

• MRMC Study: No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The document focuses on demonstrating that the device (i.e., the MRI system itself) with new features is substantially equivalent to the predicate, not on how human readers' performance improves with or without AI assistance. The OASIS MRI System is an imaging device, and the changes described (coils, software functions) are enhancements to image acquisition and processing, not an AI-assisted diagnostic tool.

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

• Standalone Performance: The described device (OASIS MRI System) is an imaging system, not an algorithm intended for standalone diagnostic output. Therefore, a standalone performance evaluation in the context of an "algorithm only" is not applicable or described. The performance testing conducted for the new software functions (IP-Recon, IP-Scan, AutoPose Spine, AutoClip) would relate to their intended function within the MRI system (e.g., image reconstruction accuracy, scan automation effectiveness), not as standalone diagnostic algorithms.

7. The Type of Ground Truth Used

• Ground Truth Type: Not explicitly stated as "ground truth." The evaluation seems to rely on clinical usability and meeting user needs as assessed by qualified individuals (implicitly, physicians/radiologists). This would fall under a form of expert consensus/opinion regarding the quality and utility of the images produced by the new features. There's no mention of pathology, outcomes data, or other objective sources of ground truth.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable/not specified. The document describes an MRI system and its software/hardware enhancements. It does not mention a "training set" in the context of machine learning model development. The software functions like IP-Recon, IP-Scan, AutoPose Spine, and AutoClip are likely rule-based or optimized algorithms, not necessarily deep learning models requiring a distinct "training set" in the common sense.

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

• Ground Truth Establishment for Training Set: Not applicable/not specified, as no training set for a machine learning model is mentioned.

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This ARETTA 65 is intended for use in a Healthcare facility (hospital, private medical office, etc.) by Healthcare trained personnel (doctor, sonographer, etc.) for the diagnostic ultrasound evaluation of Fetal, Abdominal, Intra-operative (Spec.), Intra-operative (Neuro.), Laparoscopic, Pediatric, Small Organ (Spec.), Neonatal Cephalic, Trans-rectal, Trans-vaginal, Trans-esoph. (non-Card.), Musculo-skel. (Convent.), Musculoskel. (Superfic.), Other (spec.) -Gynecological, Other (spec.) - Wound, Cardiac Adult, Cardiac Pediatric, Trans-esophageal (card.), Peripheral vessel, clinical applications.

The Modes of Operation are B mode, PW mode (Pulsed Wave Doppler), CW mode (Continuous Wave Dopler), Color Doppler, Power Doppler (Color Flow Angiography), TDI (Tissue Doppler Imaging), 3D Imaging, 4D Imaging.

The ARIETTA 65 is a multi-functional ultrasound diagnostic scanner in which Doppler. Color Flow Mapping, etc. are provided and all circuits related to image quality are fully digitalized. This device can be utilized with linear, convex, radial and phased array scan type probes for usage with a variety of clinical applications.

The ARIETTA 65 can be used for individual or combined display in the image display model listed below.

• B mode is a display mode in which the tomographic imaqe is formed with plural . ultrasound beams by the methods mentioned above. During the process of creating the tomographic image, adaptive filters (HI REZ) that modify the characteristics of each echo filter are used to produce a clear image.
• M mode is a display mode of ultrasound beams received sequentially and repeatedly on ● the screen from the same direction. It indicates these reflected echoes in one direction from the interior of the patient's body's on time-series scale.
• There are two types of D (Doppler) mode: PW Doppler mode and CW Doppler mode. ● PW Doppler mode displays bloodstream information consecutively at a sample point that is detected by pulsed Doppler sonography. CW Doppler mode displays bloodstream information continuously in the single-direction ultrasound beam that is detected by the CW Doppler method.
• Color Doppler mode receives ultrasound from the same direction and detects any . changes that occur over time to identify three types of bloodstream information: its direction, its speed, and its inconsistency. The mode then colors that information and displays it as an overlay on B mode or M mode. Color Flow Mode, Power Doppler Mode, High-Resolution Power Doppler (eFlow) Mode can be used with this instrument according to need.

The 4 methods of electronic scanning are as follows.

• Linear Scanning Method:
  By this method, the ultrasound beam from the ultrasound probe is emitted in a straight line (linearly) and draws a tomographic image of the test subject.

• Convex Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially and draws a tomographic image of the test subject.

• . Sector Scanning Method:

By this method, the ultrasound beam from the ultrasound probe is emitted in a fan shape (sector) and draws a tomographic image of the test subject.

• . Trapezoidal Scanning Method:
  By this method, the ultrasound beam from the ultrasound probe is emitted radially without regard to the form of the probe head and draws a tomographic image of the patient.

The provided document describes the ARIETTA 65 ultrasound diagnostic system. It does not contain information about acceptance criteria or a study that specifically proves the device meets such criteria in terms of diagnostic performance metrics like sensitivity or specificity.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (ARIETTA 65 (K181376) and ALOKA ARIETTA 850 (K173739)) based on technological characteristics, safety standards, and intended use. The rationale is that since the new device has equivalent safety and effectiveness to a previously cleared device, it does not require new comprehensive clinical performance studies in the same way a novel device might.

Therefore, many of the requested details about acceptance criteria and clinical study specifics are not available in this regulatory submission.

Here's a breakdown based on the information available in the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not specify quantitative acceptance criteria for diagnostic performance (e.g., specific sensitivity, specificity, or accuracy thresholds) or provide reported device performance metrics against such criteria. The "performance comparison" mentioned refers to demonstrating conformance with special controls or recognized standards and comparing technological characteristics to a predicate device, not clinical performance metrics.

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

Not applicable. The document states "Clinical testing was not required." The "Validation Testing - Bench" was conducted, but it's not described as a clinical performance test with a "test set" in the context of diagnostic accuracy.

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

Not applicable, as no clinical test set with ground truth establishment is described.

4. Adjudication Method for the Test Set:

Not applicable.

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

No. The document explicitly states "Clinical testing was not required."

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

Not applicable. This is an ultrasound system, not an AI algorithm intended for standalone diagnostic interpretation.

7. The Type of Ground Truth Used:

Not applicable, as no clinical study requiring ground truth is described.

8. The Sample Size for the Training Set:

Not applicable. This document describes a medical device (ultrasound system), not an AI algorithm that typically has a "training set."

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

Not applicable.

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The OASIS MRI System is an imaging device, and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross-sectional images that display the internal structure of the head, body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-latice relaxation time (T1), spin-spin relaxation time (T2), and flow. When interpreted by a trained physician, these images provide information that can be useful in diagnosis determination.

The OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

The provided text describes a 510(k) premarket notification for the Hitachi OASIS MRI System, focusing on demonstrating substantial equivalence to a predicate device (OASIS MRI System K192851). The document primarily outlines changes made to the device and provides a rationale for why these changes do not affect safety or effectiveness, rather than presenting a detailed study with specific acceptance criteria and performance metrics in the typical sense of a diagnostic AI device.

Therefore, the requested information, particularly regarding specific numerical acceptance criteria, performance metrics, sample sizes for test/training sets, expert qualifications, and adjudication methods, is not explicitly available in the provided text as it would be for an AI/CADe device. The submission focuses on demonstrating that modifications to an existing MRI system (hardware, coils, software updates) do not negatively impact its performance compared to the predicate.

Here's an analysis based on the information available in the document, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in a quantifiable table as requested. The document asserts "substantial equivalence" as the primary "acceptance criterion" indirectly. The performance evaluation is focused on demonstrating that new features and changes "perform as intended for diagnostic use" and that the device modifications "do not raise different questions of safety and effectiveness."

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

The document mentions "Clinical image examples are provided for each applicable new feature." It describes these examples as "sufficient to evaluate clinical usability."

• Sample Size: Not specified. It's implied to be a collection of "examples" rather than a statistically powered test set for quantitative performance.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). It likely refers to internal testing data.

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

• Number of Experts: Not specified. The phrase "we judged to be sufficient to evaluate clinical usability" suggests internal evaluation.
• Qualifications of Experts: Not specified.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and effect size of human readers improvement with AI vs without AI assistance

• MRMC Study: No, this is not an AI/CADe device. The submission concerns an MRI system itself and its modifications, not an AI-powered diagnostic aid. Therefore, no MRMC study comparing human readers with and without AI assistance was performed or needed.

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

• Standalone Performance: No, this is not an AI/CADe device. The assessment is on the MRI system's ability to produce diagnostic images. The "standalone" performance here refers to the system meeting technical standards (NEMA, IEC) and producing images deemed clinically usable.

7. The type of ground truth used

The "ground truth" implicitly used for the clinical image examples is expert judgment of clinical usability of the images. This is not pathology, outcomes data, or a pre-established consensus for specific findings, but rather an assessment that the images produced by the modified system remain suitable for diagnosis by a trained physician.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This device is an MRI system, not a machine learning algorithm that requires a "training set" in the context of AI.

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

• Training Set Ground Truth Establishment: Not applicable, as it's not an AI/ML device.

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The SCENARIA View system is indicated to acquire axial volumes of the whole body including the head. Images can be acquired in axial, helical, or dynamic modes. The SCENARIA View system can also be used for interventional needle guidance.

Volume datasets acquired by a SCENARIA View system can be post-processed in the SCENARIA View system to provide additional information. Post-processing capabilities of the SCENARIA View software include, multi-planar reconstruction (MPR), and volume rendering.

Volume datasets acquired by a SCENARIA View system can be transferred to external devices via a DICOM standard interface.

The Low Dose CT Lung Cancer Screening Option for the SCENARIA View system is indicated for using low dose CT for lung cancer screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body, a professional medical society, and/or Hitachi.

The SCENARIA View is a multi-slice computed tomography system that uses x-ray data to produce cross-sectional images of the body at various angles.

The SCENARIA View system uses 128-slice CT technology. where the X-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 64 slices of data simultaneously. The X-ray sub-system features a high frequency generator, X-ray tube, and collimation system that produces a fan beam X-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the X-ray tube/detector assembly rotates around the patient, data is collected at multiple angles.

The collected data is then reconstructed into cross-sectional images by a high-speed reconstruction sub-system. The images are displayed on a Computer Workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system.

The SCENARIA View system consists of a Gantry, Operator's Workstation, Patient Table, High-Frequency X-ray Generator, and accessories.

The provided text describes a 510(k) premarket notification for the Hitachi SCENARIA View computed tomography x-ray system (K200498). However, it focuses heavily on demonstrating substantial equivalence to a predicate device (SCENARIA View K190841) rather than detailing specific acceptance criteria and a study proving those criteria are met for this new iteration of the device, especially concerning any new AI features.

The document does mention two new features: "Volume Shuttle Scan" and "HiMAR Plus," and a "clinical image study to assess the image quality of the images reconstructed by using FBP and the two new features (HiMAR Plus, Intelli IPV)." It's unclear if "Intelli IPV" is a typo for "Volume Shuttle Scan" or another unmentioned feature.

Given the information provided, I cannot fully answer all aspects of your request as the document does not contain explicit acceptance criteria and detailed study results in the format you've requested for the new features. It primarily states that the overall device performance is "similar to the predicate device" and that "evaluation results confirm the performance characteristics of the SCENARIA View are comparable to the predicate device."

Here's an attempt to organize the available information based on your request, with significant gaps noted:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in a quantitative manner for new features or a direct comparison to specific performance metrics for the subject device (K200498) against pre-defined thresholds. Instead, it relies on demonstrating comparability to a predicate device (K190841) and general compliance with standards.

For the overall system, the document states:

• "This device complies with all applicable requirements for Dose Profile, Noise, Mean CT number and Uniformity, Spatial Resolution, Tomographic Section Thickness and Sensitivity Profile, Tomographic Plane Location, and CT dose index."
• "The evaluation results confirm the performance characteristics of the SCENARIA View are comparable to the predicate device."

For the new features (HiMAR Plus, Volume Shuttle Scan, and potentially Intelli IPV), specific acceptance criteria and their met performance are not detailed. The phrase "clinical image study to assess the image quality of the images reconstructed by using FBP and the two new features" implies an evaluation, but the results or acceptance criteria for this evaluation are not provided.

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

The document mentions a "clinical image study" for the new features. However, it does not provide any specific information on:

• Sample size (number of images or patients) used for this test set.
• Data provenance (e.g., country of origin, retrospective or prospective nature).

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

This information is not provided in the document.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. It briefly notes a "clinical evaluation comparison was conducted with the SCENARIA View system and the SCENARIA Phase 3 System (K150595) and found to be substantially equivalent," but this appears to be a general comparison of overall system performance rather than a specific MRMC study involving human readers with/without AI assistance.

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

The document does not specify if a standalone performance study was conducted. The mentioned "clinical image study" likely involves subjective evaluation of reconstructed images, which could imply a standalone assessment of image quality, but details are lacking.

7. Type of Ground Truth Used

The document does not specify the type of ground truth used for the "clinical image study." Given the context of image quality assessment for new CT reconstruction features, the ground truth would likely be based on:

• Expert Consensus: Radiologist interpretation of image quality parameters.
• Physical Phantoms: Quantitative measurements using known phantom properties (though this tends to be more for technical performance aspects like noise and spatial resolution rather than a "clinical image study").

The document mentions evaluations for "dose profile, image noise, Modulation Transfer Function (MTF), slice thickness and sensitivity profile, slice plane location, and CT dose index," which would typically use physical phantoms. However, the "clinical image study" for "image quality of the images reconstructed by using FBP and the two new features" suggests a different type of assessment.

8. Sample Size for the Training Set

The document does not provide any information regarding a training set sample size. This is expected as the document describes a CT system (hardware and associated software features) rather than a deep learning AI model that typically requires extensive training data. The "new features" like HiMAR Plus (HItachi's Metal Artifact Reduction) are often based on algorithmic improvements rather than learned models from large datasets.

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

As no training set is mentioned in the context of typical AI/ML development, this information is not applicable/provided.

Summary of Gaps:

The FDA 510(k) summary provided is primarily focused on demonstrating substantial equivalence to a predicate device rather than detailing specific, quantitative acceptance criteria and the rigorous testing (especially for any AI components) that would fully answer your questions. While it mentions new features and a clinical image study, critical details such as sample sizes, expert qualifications, and specific results are absent in this public summary. These details would typically be found in the full submission to the FDA.

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The OASIS MRI System is an imaging device, and is intended to provide the physician with physiological and clinical information, obtained non-invasively and without the use of ionizing radiation. The MR system produces transverse, coronal, sagittal, oblique, and curved cross-sectional images that display the internal structure of the head. body, or extremities. The images produced by the MR system reflect the spatial distribution of protons (hydrogen nuclei) exhibiting magnetic resonance. The NMR properties that determine the image appearance are proton density, spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and flow. When interpreted by a trained physician. these images provide information that can be useful in diagnosis determination.

The OASIS is a Magnetic Resonance Imaging System that utilizes a 1.2 Tesla superconducting maqnet in a qantry design.

After reviewing the provided text, it's immediately clear that this document is a 510(k) Premarket Notification from the FDA for a new version of an MRI system (OASIS MRI System).

The key takeaway is that this document DOES NOT describe a study involving an "AI" or "algorithm" with human-in-the-loop performance, nor does it provide detailed quantitative acceptance criteria and study results for such a device.

Instead, it's a submission for the OASIS MRI System w/ V6.0F being substantially equivalent to its predicate device, the OASIS MRI System (K093044). The "performance evaluation" mentioned focuses on demonstrating that new features (like updated coils, operating systems, and software versions) do not negatively impact the device's safety and effectiveness and that the system performs as intended for diagnostic use.

The "performance evaluation" is primarily rational analysis and bench testing, with "clinical image examples" provided for usability, rather than a rigorous clinical performance study with defined quantitative acceptance criteria. There's no mention of:

• Specific quantitative metrics like sensitivity, specificity, AUC.
• Sample sizes for test sets in the typical sense of a clinical trial.
• Ground truth establishment by experts for specific diagnostic performance.
• MRMC studies or human reader improvement with AI assistance.
• Standalone algorithm performance.

Therefore, I cannot fulfill most of your request directly from the provided text, as the information regarding the acceptance criteria and the study design for an AI/algorithm-based device is not present. The document focuses on demonstrating substantial equivalence of a general MRI system, not the performance of an AI application within it.

I can, however, extract information about the predicate device and the general approach to demonstrating equivalence, which might give you some insight into the FDA 510(k) process for MRI systems, but not for an AI/algorithm.

Based on the provided document, here's what can be extracted, and where limitations exist regarding your specific request:

Limitations: The document does not describe the acceptance criteria and a study for an AI/algorithm-based device. It is a 510(k) submission for a new version of an MRI system (hardware and software updates) demonstrating substantial equivalence to a legally marketed predicate device. Therefore, many of the requested points related to AI/algorithm performance, ground truth establishment, expert readers, and MRMC studies are not applicable or not detailed in this context.

Information Extracted (with caveats for non-AI focus):

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

   • Acceptance Criteria (Implied for Substantial Equivalence): The primary "acceptance criterion" for this 510(k) is that the modified OASIS MRI System (V6.0F) is substantially equivalent to its predicate (OASIS K093044) in terms of safety, effectiveness, and intended use, despite specific updates in coils, operating system, CPU, and application software. This is demonstrated by showing that new features "perform as intended for diagnostic use" and that differences do not "raise different questions of safety and effectiveness."
   • Reported Device Performance: The "performance" is qualitative, focusing on whether new features function correctly and that fundamental safety/performance characteristics (like signal-to-noise ratio, uniformity, acoustic noise, electrical safety, EMC) remain acceptable or are not negatively impacted.
   • Table 1: Performance Analysis

     Testing Type	Rationale Analysis	Reported Device Performance
     Performance Testing - Bench	Performance bench testing was conducted on the applicable new features.	Test data confirmed that each new feature perform as intended for diagnostic use.
     Performance Testing - Clinical	Clinical image examples are provided for each applicable new feature and that we judged to be sufficient to evaluate clinical usability.	[Details of usability are not quantified, but the judgment was "sufficient"]

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

   • The document mentions "clinical image examples" for usability but does not specify a sample size for a clinical test set in the way one would for an AI performance study.
   • Data Provenance: Not specified. The clinical images are "examples" and likely collected by Hitachi, but whether they are retrospective or prospective, or from specific countries, is not stated.

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

   • Not applicable directly. This document is for an MRI system, not an AI/algorithm that requires expert-established ground truth for diagnostic accuracy. The "clinical image examples" were "judged to be sufficient to evaluate clinical usability," which implies interpretation by presumably qualified personnel (likely radiologists or technologists), but the number and qualifications are not specified nor is there a formal "ground truth" establishment process described for a test set.

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

   • Not applicable. There is no formal adjudication method described for a test set, as this is not a study assessing diagnostic performance of an algorithm.

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, an MRMC comparative effectiveness study was NOT done. This document does not describe a study involving AI assistance for human readers.

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

   • No, a standalone algorithm performance study was NOT done. This document describes a medical imaging device (MRI system), not an AI algorithm.

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

   • Not applicable in the context of an AI algorithm. For the MRI system itself, the "ground truth" for demonstrating substantial equivalence relies on established industry standards (NEMA, IEC) for image quality, safety parameters (e.g., acoustic noise, SAR), and the system's ability to produce images useful for diagnosis, interpreted by a "trained physician". This is not a ground truth for a specific diagnostic outcome.

8. The sample size for the training set:

   • Not applicable. This document describes an MRI system, not an AI model requiring a training set. The changes are primarily software version updates and new coils for an existing hardware platform.

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

   • Not applicable. See point 8.

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The SCENARIA View system is indicated to acquire axial volumes of the whole body including the head. Images can be acquired in axial, helical, or dynamic modes. The SCENARIA View system can also be used for interventional needle guidance.

Volume datasets acquired by a SCENARIA View system can be post-processed in the SCENARIA View system to provide additional information. Post-processing capabilities of the SCENARIA View software include, multi-planar reconstruction (MPR), and volume rendering.

Volume datasets acquired by a SCENARIA View system can be transferred to external devices via a DICOM standard interface.

The Low Dose CT Lung Cancer Screening Option for the SCENARIA View system is indicated for using low dose CT for lung cancer screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body, a professional medical society, and/or Hitachi.

The SCENARIA View is a multi-slice computed tomography system that uses x-ray data to produce cross-sectional images of the body at various angles. The SCENARIA View system uses 128-slice CT technology, where the X-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 64 slices of data simultaneously. The X-ray sub-system features a high frequency generator, X-ray tube, and collimation system that produces a fan beam X-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the X-ray tube/detector assembly rotates around the patient, data is collected at multiple angles. The collected data is then reconstructed into cross-sectional images by a high-speed reconstruction sub-system. The images are displayed on a Computer Workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system. The SCENARIA View system consists of a Gantry, Operator's Workstation, Patient Table, High-Frequency X-ray Generator, and accessories.

The provided text details the 510(k) premarket notification for the Hitachi SCENARIA View computed tomography x-ray system (K190841). The core of the submission focuses on demonstrating substantial equivalence to a predicate device, the SCENARIA Phase 3 (K150595).

Based on the provided document, here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly present a table of quantitative acceptance criteria for the overall device performance in the sense of a new AI/software component with specific clinical performance metrics (e.g., sensitivity, specificity for a diagnostic task). Instead, the acceptance criteria are implicitly tied to demonstrating that the SCENARIA View system performs comparably to its predicate device (SCENARIA Phase 3) and meets established industry standards for CT systems.

The performance characteristics evaluated and reported are typically those for CT imaging systems, rather than AI-specific metrics. The document states:

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

The document mentions "a clinical image study of the head, chest, abdomen, and shoulder" and "a clinical study was conducted with the Intelli IPV, FBP, and HiMAR features". However, it does not specify the sample size (number of patients or images) used in these clinical studies or bench tests for the test set.

The data provenance (e.g., country of origin, retrospective or prospective) is also not specified in the provided text.

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

The document states, "Therefore, based on a thorough analysis and of the clinical images Hitachi believes the SCENARIA View is diagnostic and meet the user needs." This implies an expert review, but it does not specify the number of experts, their qualifications, or the methodology they used to establish ground truth or evaluate the "diagnosticity" or "user needs."

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

The document does not specify any adjudication method used for reviewing the clinical images or test sets.

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 does not indicate that an MRMC study was performed. The evaluation focuses on the system's inherent performance and its substantial equivalence to a predicate, not on human reader performance with or without AI assistance. The "Intelli IPV" and "HiMAR" features are described as new iterative reconstruction and metal artifact reduction techniques, respectively, which are intrinsic image processing methods of the CT system rather than assistive AI tools for human interpretation in the common sense of an AI CAD (Computer-Aided Detection) or CADx (Computer-Aided Diagnosis) system.

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

The entire submission focuses on the performance of the integrated CT system (SCENARIA View) which includes its image acquisition and reconstruction algorithms. Therefore, the "evaluations were conducted for dose profile, image noise, Modulation Transfer Function (MTF), slice thickness and sensitivity profile, slice plane location, and CT dose index" and "bench testing...in terms of image quality metrics such as CT number accuracy, uniformity, noise" can be considered forms of standalone algorithmic performance assessment based on objective phantom and image quality metrics. The "clinical image study" also evaluates the system's output directly.

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

For the technical performance evaluations (dose profile, noise, MTF, etc.), the ground truth relies on physical phantom measurements and engineering specifications/standards.

For the "clinical image study," the ground truth implicitly relies on expert subjective assessment of image diagnosticity and ability to meet user needs, "based on a thorough analysis and of the clinical images Hitachi believes the SCENARIA View is diagnostic and meet the user needs." There is no mention of pathology or outcomes data as ground truth.

8. The sample size for the training set

The document does not describe a "training set" in the context of an AI model being developed from scratch. The "Intelli IPV" and "HiMAR" are advanced image reconstruction techniques, which are typically developed using a combination of physics models, empirical data, and potentially machine learning techniques, but the document does not disclose details about any training data or its sample size for these specific features. The device's substantial equivalence is demonstrated against an existing predicate CT system, which implies that its fundamental design and performance are already established, rather than being a de novo AI system requiring a separate training and validation set disclosure in this context.

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

As no specific training set for a new AI model is described, there's no information on how its ground truth was established. The performance of the image reconstruction algorithms (Intelli IPV, HiMAR) would have been validated against various phantom and clinical datasets to ensure they produce images of acceptable diagnostic quality, likely benchmarked against existing reconstruction methods and assessed by image quality experts. However, the specifics of this process are not detailed in the provided K190841 summary.

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The ARIETTA 750 is intended for use by trained personnel (doctor, Sonographer, etc.) for the diagnostic ultrasound evaluation of Fetal, Abdominal, Intra-operative (Spec.), Intra-operative (Neuro.), Laparoscopic, Pediatric, Small Organ (Spec.), Neonatal Cephalic, Adult Cephalic, Trans-vaginal, Trans-esoph. (non-Card.), Musculo-skel. (Convent.), Musculo-skel. (Superfic.), Wound (Cavernous), Gynecology, Cardiac Adult, Cardiac Pediatric, Trans-esophageal (card.), Peripheral vessel clinical applications.

The ARIETTA 750 is a multi-functional ultrasound diagnostic scanner in which Doppler. Color Flow Mapping, etc. are provided and all circuits related to image quality are fully digitalized. This device can be utilized with linear, convex, radial and phased array scan type probes for usage with a variety of clinical applications.

The ARIETTA 750 can be used for individual or combined display in the image display model listed below.

• B mode is a display mode in which the tomographic imaqe is formed with plural ultrasound beams by the methods mentioned above. During the process of creating the tomographic image, adaptive filters (HI REZ) that modify the characteristics of each echo filter are used to produce a clear image.
• M mode is a display mode of ultrasound beams received sequentially and repeatedly on the screen from the same direction. It indicates these reflected echoes in one direction from the interior of the patient's body's on time-series scale.
• There are two types of D (Doppler) mode: PW Doppler mode and CW Doppler mode. PW Doppler mode displays bloodstream information consecutively at a sample point that is detected by pulsed Doppler sonography. CW Doppler mode displays bloodstream information continuously in the single-direction ultrasound beam that is detected by the CW Doppler method.
• Color Doppler mode receives ultrasound from the same direction and detects any . changes that occur over time to identify three types of bloodstream information: its direction, its speed, and its inconsistency. The mode then colors that information and displays it as an overlay on B mode or M mode. Color Flow Mode, Power Doppler Mode, High-Resolution Power Doppler (eFlow) Mode can be used with this instrument according to need.

The 5 methods of electronic scanning are as follows.

• Linear Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted in a straight line (linearly) and draws a tomographic image of the test subject.
• Convex Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially and draws a tomographic image of the test subject.
• Sector Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted in a fan shape (sector) and draws a tomographic image of the test subject.
• Radial Scanning Method: By this method, the ultrasound probe emits a 360 degree (radial) ultrasound beam and draws a tomographic image of the test subject.
• Trapezoidal Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially without regard to the form of the probe head and draws a tomographic image of the patient.

This document is a 510(k) Premarket Notification for the Hitachi ARIETTA 750 ultrasound system. It claims substantial equivalence to several predicate devices. While it discusses performance characteristics and standards met, it does not include acceptance criteria or a study that proves the device meets specific performance metrics in the way you've outlined for AI/CADe systems.

The document states "Clinical testing was not required" and "Analysis confirms the performance characteristics of the ARIETTA 750 are comparable to the predicate device and support our conclusion that the subject system is substantially equivalent." This indicates that the primary method of demonstrating acceptable performance for this traditional diagnostic ultrasound device is through comparison to already cleared devices and adherence to established safety and performance standards for ultrasound equipment, rather than a clinical trial with specific performance metrics for diagnostic accuracy.

Therefore, I cannot extract the detailed information requested in points 1 through 9 for an AI/CADe system's performance study because this document pertains to a traditional diagnostic ultrasound device that establishes substantial equivalence through different criteria.

Key takeaway for this specific document: The performance proof is based on demonstrating the new device is comparable to existing, legally marketed predicate devices and adheres to relevant safety standards. It's not a study proving improved diagnostic accuracy via an algorithm against a ground truth dataset.

If you are looking for information regarding the acceptance criteria and study proving performance for a software algorithm (like AI/CADe), you would typically look for a different type of FDA submission document (e.g., a De Novo request or a 510(k) for a novel AI-enabled device) that would specifically include clinical performance data to demonstrate acceptable diagnostic accuracy.

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This ARIETTA 50 is intended for use by trained personnel (doctor, sonographer, etc.) for the diagnostic ultrasound evaluation of Fetal, Abdominal, Intra-operative (Spec.), Pediatric, Small Organ (Spec.), A Small Organ (Spec.), Adult Cephalic, Trans-rectal, Trans-vaginal, Trans-esoph. (non-Card.), Musculo-skel. (Convent.), Musculo-skel. (Superfic.), Other (Wound), Cardiac Adult, Cardiatric, Transesophageal (card.), Peripheral vessel, Other (Gynecological), clinical applications.

The ARIETTA 50 is a multi-functional ultrasound diagnostic scanner in which Doppler. Color Flow Mapping, etc. are provided and all circuits related to image quality are fully digitalized. This device can be utilized with linear, convex, radial and phased array scan type probes for usage with a variety of clinical applications.

The ARIETTA 50 can be used for individual or combined display in the image display model listed below.

• B mode is a display mode in which the tomographic imaqe is formed with plural . ultrasound beams by the methods mentioned above. During the process of creating the tomographic image, adaptive filters (HI REZ) that modify the characteristics of each echo filter are used to produce a clear image.
• M mode is a display mode of ultrasound beams received sequentially and repeatedly on ● the screen from the same direction. It indicates these reflected echoes in one direction from the interior of the patient's body's on time-series scale.
• There are two types of D (Doppler) mode: PW Doppler mode and CW Doppler mode. ● PW Doppler mode displays bloodstream information consecutively at a sample point that is detected by pulsed Doppler sonography. CW Doppler mode displays bloodstream information continuously in the single-direction ultrasound beam that is detected by the CW Doppler method.
• Color Doppler mode receives ultrasound from the same direction and detects any . changes that occur over time to identify three types of bloodstream information: its direction, its speed, and its inconsistency. The mode then colors that information and displays it as an overlay on B mode or M mode. Color Flow Mode, Power Doppler Mode, High-Resolution Power Doppler (eFlow) Mode can be used with this instrument according to need.

The 4 methods of electronic scanning are as follows.

• Linear Scanning Method: ●
  By this method, the ultrasound beam from the ultrasound probe is emitted in a straight line (linearly) and draws a tomographic image of the test subject.

• . Convex Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially and draws a tomographic image of the test subject.

• . Sector Scanning Method:

By this method, the ultrasound beam from the ultrasound probe is emitted in a fan shape (sector) and draws a tomographic image of the test subject.

• . Trapezoidal Scanning Method:
  By this method, the ultrasound beam from the ultrasound probe is emitted radially without regard to the form of the probe head and draws a tomographic image of the patient.

The ARIETTA 50 is an ultrasound diagnostic scanner.

1. Acceptance criteria and reported device performance:

The device did not set explicit performance acceptance criteria in the provided document. Instead, the manufacturer, Hitachi, Ltd., argues for substantial equivalence to a predicate device (ARIETTA 65, K181376) by demonstrating comparable performance characteristics. The lack of an acceptance criteria table stems from this approach.

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

No test set was used for clinical performance evaluation. The submission explicitly states "Clinical testing was not required."

3. Number of experts and qualifications for ground truth:

Not applicable, as no clinical testing was performed and therefore no ground truth was established from clinical data for performance evaluation.

4. Adjudication method for the test set:

Not applicable, as no clinical testing was performed.

5. Multi-reader multi-case (MRMC) comparative effectiveness study:

No MRMC study was conducted. The document states that clinical testing was not required.

6. Standalone performance (algorithm only without human-in-the-loop performance):

Not applicable. The device is an ultrasound system and there is no mention of an algorithm or AI component performing standalone interpretations. Performance evaluation was based on demonstrating substantial equivalence to a predicate device.

7. Type of ground truth used:

Not applicable for performance evaluation. The substantial equivalence argument relies on the established safety and effectiveness of the predicate device (ARIETTA 65) and demonstrating that the ARIETTA 50 has comparable physical and performance characteristics, along with adherence to applicable safety standards.

8. Sample size for the training set:

Not applicable. The document does not describe any machine learning or AI components that would require a training set.

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

Not applicable, as there is no mention of a training set or AI component in the document.

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The Supria True64 System is indicated for head, whole body, and vascular X-ray Computed Tomography applications in patients of all ages. The images can be acquired in either axial, helical, or dynamic modes. The volume datasets acquired by the Supria can be post processed by the system to provide additional information. Post processing capabilities included in the Supria software include CT angiography (CTA), Multi-planar reconstruction (MPR) and volume rendering. Volume datasets acquired by the Supria can be transferred to external devices via a DICOM standard interface. The guideShot Option adds a remote in-room display and controls to support interventional imaging. The device output can provide an aid to diagnosis when used by a qualified physician.

The Supria True64 is a multi-slice computed tomography system designed to perform multi-slice CT scanning supported by 64-detector technology. The system allows optimum clinical applications ranging from routine exams in response to the diversified circumstances in imaging whole body regions.

The device in question is the Supria True64 Whole-body X-ray CT System, intended for head, whole body, and vascular X-ray Computed Tomography applications in patients of all ages. This 510(k) submission (K183291) focuses on comparing it to the legally marketed predicate device, Supria True64 Whole-body X-ray CT System (K171738), particularly highlighting the addition of an ECG Prospective Scan (Axial) feature.

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly present a table of "acceptance criteria" in the typical sense of quantitative targets for clinical endpoints (e.g., sensitivity, specificity for a diagnostic algorithm). Instead, the acceptance criteria are implicitly defined by conformance to regulatory standards and demonstration of substantial equivalence to a predicate device, particularly regarding physical and performance characteristics and the new ECG Prospective Scan feature.

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

The document explicitly states: "Validation Testing - Clinical: None required".
This implies that no dedicated clinical test set (i.e., patient data) was used for proving the performance of the modified device (specifically the ECG Prospective Scan feature). The assessment primarily relied on bench testing and comparison to the predicate device's established performance. Therefore, there is no sample size for a clinical test set, nor specific data provenance (country of origin, retrospective/prospective) for such a set discussed.

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

Since no clinical testing was performed and the evaluation was based on non-clinical performance and substantial equivalence to a predicate, there were no experts used to establish ground truth for a clinical test set. The "ground truth" for the non-clinical tests would be defined by engineering specifications and physical measurements, rather than clinical expert consensus.

4. Adjudication Method for the Test Set:

Given that no clinical test set was used, there was no adjudication method applied. The evaluation was based on conformance to engineering specifications and regulatory standards in bench testing.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:

No MRMC comparative effectiveness study was done or reported. The submission explicitly states "Clinical: None required" for validation testing. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance, as AI assistance is not the primary subject of this submission, nor is a reader study presented.

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

Yes, a standalone performance evaluation was conducted, in the sense that non-clinical bench tests were performed on the device itself.

• "Validation Testing - Bench" was conducted. The report states: "Based on the results of the ECG Prospective Scan Performance Testing Report contained in Verification and Validation Documentation of Section 9, Hitachi judged that Supria True64 with ECG Prospective Scan (Axial) performs to specifications."
• "ECG Prospective Scan feature was subject to performance tests, which confirmed that the X-ray was in synchronization with the ECG trigger and the acquired images. The results confirmed that images which are synchronized with ECG R-wave signal can be acquired by using ECG Prospective Scan."

These tests evaluate the intrinsic technical performance of the device's new feature, independent of a human operator's interpretation, making it akin to a standalone performance assessment for that specific function.

7. The type of ground truth used:

For the non-clinical bench testing, the ground truth was based on engineering specifications and physical validation of the device's functionality, particularly the synchronization of the X-ray with the ECG trigger and the resulting image acquisition. For the substantial equivalence argument, the ground truth was the established performance and safety of the predicate device (Supria True64 K171738) and compliance with recognized industry standards.

8. The sample size for the training set:

Not applicable. This submission describes a hardware device (CT system) and a software feature (ECG Prospective Scan) that is a modification to an existing cleared device. It is not an AI/ML algorithm that requires a "training set" in the computational sense. The "training" for such a system would involve engineering design and calibration, not data-driven model training.

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

Not applicable. As explained above, there is no AI/ML training set in this context. The "ground truth" for developing and calibrating such a CT system and its features would be based on established physics, engineering principles, and phantom measurements to ensure accurate imaging and synchronization.

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The ALOKA ARIETTA 850 is intended for use by trained personnel (doctor, Sonographer, etc.) for the diagnostic ultrasound evaluation of Fetal, Abdominal, Intra-operative (Spec.), Intraoperative (Neuro.), Laparoscopic, Pediatric, Small Organ (Spec.), Neonatal Cephalic, Adult Cephalic, Trans-rectal, Trans-vaginal, Trans-esoph. (non-Card.), Musculo-skel. (Convent.), Musculo-skel. (Superfic.), Wound (Cavernous/Non-Cavernous), Gynecology, Cardiac Adult, Cardiac Pediatric, Trans-esophageal (card.), Peripheral vessel, Endoscopy clinical applications.

The ALOKA ARIETTA 850 is a multi-functional ultrasound diagnostic scanner in which Doppler, Color Flow Mapping, etc. are provided and all circuits related to image quality are fully digitalized. This device can be utilized with linear, convex, radial and phased array scan type probes for usage with a variety of clinical applications.

The ALOKA ARIETTA 850 can be used for individual or combined display in the image display model listed below.

• B mode is a display mode in which the tomographic image is formed with plural ultrasound beams by the methods mentioned above. During the process of creating the tomographic image, adaptive filters (HI REZ) that modify the characteristics of each echo filter are used to produce a clear image.
• M mode is a display mode of ultrasound beams received sequentially and repeatedly on the screen from the same direction. It indicates these reflected echoes in one direction from the interior of the patient's body's on time-series scale.
• There are two types of D (Doppler) mode: PW Doppler mode and CW Doppler mode. PW Doppler mode displays bloodstream information consecutively at a sample point that is detected by pulsed Doppler sonography. CW Doppler mode displays bloodstream information continuously in the single-direction ultrasound beam that is detected by the CW Doppler method.
• Color Doppler mode receives ultrasound from the same direction and detects any changes that occur over time to identify three types of bloodstream information: its direction, its speed, and its inconsistency. The mode then colors that information and displays it as an overlay on B mode or M mode. Color Flow Mode. Power Doppler Mode, High-Resolution Power Doppler (eFlow) Mode can be used with this instrument according to need.

The 5 methods of electronic scanning are as follows.

• Linear Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted in a straight line (linearly) and draws a tomographic image of the test subject.
• Convex Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially and draws a tomographic image of the test subject.
• Sector Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted in a fan shape (sector) and draws a tomographic image of the test subject.
• Radial Scanning Method: By this method, the ultrasound beam emits a 360 degree (radial) ultrasound beam and draws a tomographic image of the test subject.
• Trapezoidal Scanning Method: By this method, the ultrasound beam from the ultrasound probe is emitted radially without regard to the form of the probe head and draws a tomographic image of the patient.

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