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The Digital Prism Correction Feature (DPCF) is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription.

DPCF is available over-the-counter (OTC) for users with prism in their eyeglass prescription. When a prescription also includes other parts (e.g., sphere, cylinder, ADD), which can be fulfilled by optical inserts, the DPCF fulfills the prism part of the prescription while using Apple Vision Pro.

DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimension (i.e., base-up, base-down, base-in, base-out), per eye.

The Digital Prism Correction Feature (DPCF) is intended to provide a high quality visual experience in Apple Vision Pro spatial computing applications for users with a prism prescription. Specifically, the DPCF is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription, in the horizontal and/or vertical dimensions. DPCF fulfills the prism part of an eyeglass prescription. When an eyeglass prescription also includes other parts (e.g., sphere, cylinder, ADD), DPCF fulfills the prism part of the prescription, while prescription optical inserts fulfill the other parts of the prescription.

The DPCF achieves its intended use by converting a user's prism prescription into digital image adjustment parameters that are utilized by the spatial computing image system to automatically provide digital image adjustments in horizontal and/or vertical dimensions in accordance with a user's prism prescription. At this time, DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimensions (i.e, base-up, base-down, base-in, base-out), per eye.

The DPCF is available over-the-counter (OTC).

The provided FDA 510(k) clearance letter and summary for the Apple Digital Prism Correction Feature (DPCF) primarily discuss its substantial equivalence to a predicate device and its move from prescription to over-the-counter (OTC) use. It does not contain an in-depth study proving the device meets acceptance criteria in the typical sense of a clinical trial for diagnostic AI.

However, based on the information provided, we can extract details about the acceptance criteria and the type of study conducted to support the device's performance, particularly focusing on the "Summary of Non-Clinical Testing."

Here's an analysis of the requested information:

Acceptance Criteria and Reported Device Performance

Study Details

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

• Test Set Sample Size: For the human factors and usability study, the sample size was 30 subjects.
• Data Provenance: The document does not explicitly state the country of origin. The study was conducted as non-clinical testing to assess "self-selection and use-related risks associated with use of the DPCF as an OTC device." The nature of "bench validation testing" mentioned for prism tolerance requirements suggests it's a controlled engineering test rather than a patient data-driven study.

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

• The document does not specify the number or qualifications of experts involved in establishing ground truth for the human factors study. For “bench validation testing” which measured prism tolerance, the ground truth would be based on metrological standards and precision instruments, rather than expert judgment.

4. Adjudication method for the test set:

• The document does not describe any adjudication method. The human factors study assessed self-selection and use-related risks, which would typically involve observing user interactions and collecting feedback, rather than a diagnostic accuracy adjudication process. The bench testing involves direct measurement against a standard.

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 explicitly mentioned or performed. The DPCF is a "Digital Prism Correction Feature" designed to "provide digital image adjustments" based on a user's existing prism prescription. It's not a diagnostic AI device that assists human readers in interpreting medical images or data. Therefore, the concept of "human readers improving with AI vs without AI assistance" does not directly apply to the described function of this device, which seems to be a corrective rather than diagnostic tool.

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

• Yes, in essence. The "bench validation testing that demonstrated DPCF meets prism tolerance requirements specified in ISO 8980-1:2017" represents a standalone evaluation of the algorithm's output (the digital image adjustment) against a predefined standard, independent of human interaction for interpretation. The human factors study, while involving humans, evaluates the usability and safety of the interface for an OTC product, not the diagnostic performance of an algorithm.

7. The type of ground truth used:

• For prism tolerance requirements: The ground truth is based on standardized metrological requirements as specified in ISO 8980-1:2017. This is a technical standard for ophthalmic optics.
• For human factors and usability: The "ground truth" would be the assessment of use-related risks against predefined safety and usability thresholds, determined through observation and feedback collection in a controlled user study.

8. The sample size for the training set:

• The document does not provide information regarding a training set sample size. This is consistent with the device being a "Digital Prism Correction Feature" that applies a known optical principle (prism correction) digitally, rather than a machine learning model trained on a large dataset to perform a diagnostic or predictive task. The device likely uses algorithms based on physics and geometry to implement the prism correction, rather than being a trained AI model in the typical sense.

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

• As no training set is mentioned in the context of machine learning, there is no information provided on how ground truth for a training set was established. The device functions as a digital implementation of an established optical correction principle.

Summary of Device Functionality Context: The DPCF appears to be a software feature that digitally applies prism correction within the Apple Vision Pro. Its "AI" component, if any, is not a diagnostic or predictive model in the typical sense seen in many FDA-cleared AI pathology or radiology devices. Instead, it seems to be a precise digital enactment of a known optical principle to correct for existing prism prescriptions. The studies described focus on whether this digital implementation meets optical accuracy standards and whether its use as an over-the-counter device is safe and usable.

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Prismira is a digital therapeutic indicated to improve attention function in adults ages 22-55 years old with primarily inattentive or combined-type Attention Deficit and Hyperactivity Disorder (ADHD). Patients who engage with Prismira demonstrate improvements in a digitally assessed measure of sustained and selective attention, Test of Variables of Attention (TOVA), and may not display benefits in typical behavioral symptoms, such as hyperactivity.

Prismira should be considered for use as part of a therapeutic program that may include clinician directed therapy, medication, and/or educational programs, which further address symptoms of the disorder.

Prismira is software-as-a-medical device (SaMD) that resides on the user's mobile device and can be executed at home.

Prismira is a prescription digital therapeutic indicated to improve attention function in patients 22 and older with primarily inattentive or combined type ADHD. Patients who engage with Prismira demonstrate improvements in attention functioning and may not display benefits in other behavioral symptoms such as hyperactivity. Prismira is intended to be used as part of a therapeutic program that may include clinician-directed therapy, medication, and/or educational programs, which further address symptoms of the disorder.

The Prismira App also includes an Engagement System to promote compliance with the treatment. The Engagement System gives the patient encouragement and feedback on progress at multiple timescales: performance on the most recent gameplay and historical game performance, completion of the daily assignment, progress toward the weekly target in the current week, and historical assignment completion.

Each game includes visual and optional auditory stimulus presentation. The basic input from the patient includes physical interaction with a touch screen accomplished by using the mobile device. The device's feedback within each game includes visual and optional auditory responses to the inputs. Output from each game includes a score earned by playing the game. Each gameplay is brief, typically between 1 and 5 minutes in duration, depending on the game. The treatment program guides patients through an ordered list of games (the "script") that enables subjects to engage in the recommended daily regimen (approximately 15 minutes per day).

Here's a breakdown of the acceptance criteria and study details for the Prismira device, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria (as implied by study success and substantial equivalence claims):

• Primary Endpoint: Statistically significant improvement in TOVA ACS in the LLM-001 therapy arm compared to the control arm.
• Safety: No new or increased safety risks compared to the predicate device, and a favorable safety profile with minimal adverse events.

• Control Change from Baseline Mean: 0.3 points (SD=3.6)
• LSM Difference (LLM-001 - Control): 0.78 points (SE=0.32)
• LSM Difference p-value: 0.0149 (statistically significant)
• LSM improvement of 1.09 points was less than the recognized MCID of 1.4 points, but notably greater than the predicate device (0.93 points). |
  | Secondary Endpoint: CGI-I | Improvement over control (desirable for clinical benefit). | Superior clinical benefit for the LLM-001 arm over control (p=0.0087). (For LLM-001 subjects, 32.1% were assessed as "very much improved" on the CGI-I). |
  | Secondary Endpoint: ADHD-RS Total Score | Improvement over control (desirable for clinical benefit). | 30.4% of LLM-001 subjects experienced greater than 30% reduction in their ADHD symptoms based on ADHD-RS total score. (Numerically favored LLM-001, but no statistically meaningful differences between groups for the continuous measure). |
  | Adverse Events (AEs) | Minimal; no serious AEs; low treatment-related AE rate. | - No serious adverse events reported.
• Only two treatment-related AEs (frustration) out of 275 LLM-001 treated subjects (AE rate of 0.7%).
• No subjects exited early for medical or gameplay-related complaints. |
  | Treatment Compliance | High, demonstrating feasibility of use. | Mean of 874.7 minutes (97.2%) of 900 expected minutes. |

2. Sample Size and Data Provenance for the Test Set (Clinical Study)

• Sample Size: 560 subjects were enrolled in The GAMES Study.
  • Safety Population: All 560 subjects.
  • Intent-to-Treat (ITT) Population: 456 subjects (224 randomized to LLM-001 active therapy, 232 to control therapy) who successfully completed gameplay treatment and Day 63 follow-up assessments.
• Data Provenance: Not explicitly stated (e.g., country of origin). However, it was a "multi-center" clinical trial, suggesting diverse locations. It was a prospective, randomized, double-blind, parallel-group, sham-controlled clinical trial.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• The study used blinded clinicians to assess the CGI-I (Clinical Global Impression-Improvement), a global measure of clinical improvement.
• Qualifications of Experts: Not explicitly stated beyond "blinded clinicians." Their expertise would be in diagnosing and assessing ADHD.

4. Adjudication Method for the Test Set

• The primary effectiveness measure (TOVA ACS) was a digitally assessed measure, so it wouldn't typically require human adjudication in the same way imaging studies might.
• For the CGI-I, it was assessed by "blinded clinicians." This implies independent assessment rather than a consensus/adjudication process among multiple experts for each individual case's outcome on this specific measure. The "blinded" nature is the key control here.
• There's no mention of a 2+1, 3+1, or other specific adjudication method for the clinical endpoints.

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

• No, an MRMC comparative effectiveness study was not done. This type of study typically applies to diagnostic imaging devices where human readers interpret medical images with and without AI assistance.
• This study was a randomized controlled clinical trial evaluating a digital therapeutic, focusing on its direct effect on patient outcomes (attention function) compared to a control.

6. Standalone Performance Study

• Yes, a standalone (algorithm only) performance study was done in the context of the clinical trial.
• The "LLM-001 active therapy" arm represents the device's performance when used by the patient as intended, without direct human-in-the-loop interaction from the algorithm's perspective. The clinicians were involved in diagnosis and overall patient care, but the daily therapeutic delivery was directly from the device to the patient.
• The primary endpoint, "change from baseline in the digitally assessed measure of sustained and selective attention, the Test of Variables of Attention (TOVA®)," is a direct measure of the algorithm's effect on attention function.

7. Type of Ground Truth Used (Clinical Study)

• The primary ground truth for effectiveness was derived from the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS), a digitally assessed, objective measure of sustained and selective attention. This is a standardized, quantitative neuropsychological assessment tool.
• Secondary ground truths included:
  • ADHD-RS: ADHD Rating Scale, likely a clinician-rated symptom scale or patient-reported outcome.
  • CGI-I: Clinical Global Impression-Improvement, a clinician-assessed global measure of improvement.
  • BRIEF-A: Behavior Rating Inventory of Executive Function – Adult, likely a patient-reported or clinician-rated scale.
  • WFIRS-S: Weiss Functional Impairment Rating Scale – Self Report, a patient-reported outcome.
  • AAQoL: Adult ADHD Quality of Life Questionnaire, a patient-reported outcome.
• Diagnosis of ADHD (combined or inattentive type) was confirmed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria and confirmed by the Mini-International Neuropsychiatric Interview (MINI) for ADHD adult version.

8. Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set used to develop the Prismira algorithm. The provided clinical study (The GAMES Study) is the pivotal validation study designed to test the already developed device.
• The device description mentions an "Adaptive algorithm and Engagement System." Adaptive algorithms are often trained on large datasets, but the details of that training process and dataset size are not included in this 510(k) summary. References ¹ and ² hint at large-scale cognitive training data and online trials, which might relate to the algorithm's development.

9. How Ground Truth for the Training Set Was Established

• Similar to the training set sample size, the document does not explicitly describe how ground truth for the training set was established.
• Given that it's an "adaptive algorithm," training data would likely involve cognitive performance metrics and potentially diagnostic information from previous patient populations. This could involve using standardized cognitive tests (like TOVA or similar measures) as the "ground truth" to train the algorithm to improve performance on those measures.

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The Digital Prism Correction Feature (DPCF) is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription.

DPCF is available for users with prism in their eyeglass prescription also includes other parts (e.g., sphere, cylinder, ADD), which can be fulfilled by optical inserts, the DPCF fulfills the prism part of the prescription while using Apple Vision Pro.

DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimensions (i.e., baseup, base-down, base-in, base-out), per eye.

The Digital Prism Correction Feature (DPCF) is intended to provide a high quality visual experience in Apple Vision Pro spatial computing applications for users with a prism prescription. Specifically, the DPCF is software that is intended to provide digital image adjustments in Apple Vision Pro in accordance with a user's prism prescription, in the horizontal and/or vertical dimensions. DPCF fulfills the prism part of an eyeglass prescription. When an eyeglass prescription also includes other parts (e.g., sphere, cylinder, ADD), DPCF fulfills the prism part of the prescription, while prescription optical inserts fulfill the other parts of the prescription.

The DPCF achieves its intended use by converting a user's prism prescription into digital image adjustment parameters that are utilized by the spatial computing image system to automatically provide digital image adjustments in horizontal and/or vertical dimensions in accordance with a user's prism prescription. At this time, DPCF supports prism prescriptions up to 7.75 Prism Diopters (PD) in the horizontal and/or vertical dimensions (i.e. base-down, base-in, base-out), per eye.

The provided document, a 510(k) summary for Apple's Digital Prism Correction Feature (DPCF), outlines the acceptance criteria and the study conducted to prove the device meets these criteria.

Here's the breakdown:

1. Table of Acceptance Criteria and Reported Device Performance:

Summary of the Study Proving Device Meets Acceptance Criteria:

The study conducted was Non-Clinical Testing, specifically focusing on Bench Validation Testing and Software Verification and Validation. No clinical testing was performed or submitted.

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

• Sample Size for Test Set: The document does not explicitly state a sample size for the bench validation testing. It mentions "results" from the testing but doesn't quantify the number of instances or measurements taken.
• Data Provenance: The document does not specify the country of origin for the data or whether it was retrospective or prospective. It implies the testing was conducted by Apple Inc., an American company, but no further details are given. The nature of "bench validation testing" suggests prospective testing conducted specifically for this submission.

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

• The document does not mention the use of experts to establish ground truth for the bench validation testing. The ground truth was established by standardized prism tolerance requirements (ISO 8980-1:2017), which are technical and objective metrics, not requiring expert human adjudication in the typical sense for medical imaging AI.

4. Adjudication Method for the Test Set:

• None. As the ground truth was established by objective technical standards (ISO 8980-1:2017), there was no need for human adjudication (e.g., 2+1, 3+1). The testing involved measuring the device's output against these predefined technical tolerances.

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

• No, an MRMC study was NOT done. The document explicitly states: "No clinical testing data has been submitted." The device is a "software-only" device that modifies digital images based on a user's prism prescription, and its performance was evaluated against technical standards, not by human readers comparing performance with and without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done:

• Yes, in essence. The "Bench Validation Testing" evaluated the DPCF's ability to meet "standardized prism tolerance requirements" as an independent function. While the DPCF operates within the Apple Vision Pro, the testing focused on the device's algorithmic output (digital image adjustments) against an objective standard, rather than its effect on human performance. The assessment that the "general purpose spatial computing platform... do not adversely affect the DPCF's ability to meet standardized prism tolerances" further supports this standalone assessment of the DPCF's core function.

7. The Type of Ground Truth Used:

• The ground truth used was objective technical standards/specifications, specifically the prism tolerance requirements specified in ISO 8980-1:2017. This is a well-established international standard for ophthalmic optics.

8. The Sample Size for the Training Set:

• The document does not specify a training set sample size. The DPCF is described as software that converts a user's prism prescription into digital image adjustment parameters. This implies a rule-based or calculative approach based on optical principles rather than a machine learning model that would typically require a large training dataset. The "software engineering practices" and "bench validation testing" validate the implementation of these optical principles.

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

• Not applicable in the typical sense for machine learning training sets. Given the nature of the device (applying prism corrections based on a prescription), the "ground truth" for its function is rooted in established optical principles and formulas for prism correction in optics. The software's "design" (as mentioned in "design controls") would incorporate these principles. The validation then confirms that the software's output aligns with these physical optical truths as defined by ISO standards.

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The CATS-L® Tonometer Prism is intended to be used with Goldmann type tonometers for the measurement of intraocular pressure of the human eye.

The CATS-L Tonometer Prism is used as an optical image prism for Goldmann applanation style tonometers. The CATS-L prism is made of PMMA, the corneal contact diameter is 6.28 mm, and the total length of the prism is 29.28 mm.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Five samples of the CATS-L tonometer prisms were evaluated.
• Data Provenance: The document does not specify the country of origin. The study is described as "design verification bench testing," implying a prospective, controlled laboratory setting rather than retrospective patient data.

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

This information is not provided in the document. The testing described is bench testing against specified engineering standards, not a clinical study involving human experts establishing ground truth from patient data.

4. Adjudication method for the test set:

This information is not applicable as the testing was bench testing against engineering standards, not a clinical study requiring expert adjudication of results.

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:

A multi-reader multi-case (MRMC) comparative effectiveness study was not done. This device is a tonometer prism, which is a physical accessory for measuring intraocular pressure, not an AI or imaging diagnostic tool. Therefore, the concept of human readers improving with AI assistance is not relevant to this device.

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

A standalone performance study of an algorithm was not done. This device is a physical medical device accessory, not a software algorithm. The "performance data" refers to bench testing of the physical properties against engineering standards.

7. The type of ground truth used:

The ground truth used was based on engineering standards specified in ANSI Z80.10-2018. Specifically, sections A1.1, A1.2, and A1.6 for various physical and optical properties of the tonometer prism.

8. The sample size for the training set:

This information is not applicable. This is a physical medical device, not an AI or machine learning model that requires a training set.

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

This information is not applicable as there is no training set for this device.

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The MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal and oblique cross-sectional images, spectroscopic images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/or spectra and the physical parameters derived from the images and/or spectra when interpreted by a trained physician yield information that may assist in diagnosis.

The MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room displays and MR Safe biopsy needles.

The subject devices, MAGNETOM Aera (including MAGNETOM Aera Mobile), MAGNETOM Skyra, MAGNETOM Prisma, MAGNETOM Prisma™, MAGNETOM Vida, MAGNETOM Lumina with software syngo MR XA60A, consist of new and modified software and hardware that is similar to what is currently offered on the predicate device, MAGNETOM Vida with syngo MR XA50A (K213693).

This FDA 510(k) summary describes several updates to existing Siemens Medical Solutions MRI systems (MAGNETOM Vida, Lumina, Aera, Skyra, Prisma, and Prisma fit), primarily focusing on software updates (syngo MR XA60A) and some modified/new hardware components. The document highlights the evaluation of new AI features, specifically "Deep Resolve Boost" and "Deep Resolve Sharp."

Here's an analysis of the acceptance criteria and the study details for the AI features:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a general overview of the evaluation metrics used but does not explicitly state acceptance criteria in a quantitative format (e.g., "Deep Resolve Boost must achieve a PSNR of X" or "Deep Resolve Sharp must achieve Y SSIM"). Instead, it describes the types of metrics used and qualitative assessments.

• Impact characterized by PSNR and SSIM. | The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Most importantly, the performance was evaluated by visual comparisons to evaluate e.g., aliasing artifacts, image sharpness and denoising levels. |
  | Deep Resolve Sharp | - Preservation of image quality (image sharpness) compared to original.
• Impact characterized by PSNR, SSIM, and perceptual loss.
• Verification and validation by visual rating and evaluation of image sharpness by intensity profile comparisons. | The impact of the network has been characterized by several quality metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and perceptual loss. In addition, the feature has been verified and validated by inhouse tests. These tests include visual rating and an evaluation of image sharpness by intensity profile comparisons of reconstructions with and without Deep Resolve Sharp. |

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

• Deep Resolve Boost: The document doesn't explicitly state a separate "test set" size. It mentions the "Training and Validation data" which includes:
  • TSE: more than 25,000 slices
  • HASTE: pre-trained on the TSE dataset and refined with more than 10,000 HASTE slices
  • EPI Diffusion: more than 1,000,000 slices
  • Data Provenance: The data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. No specific country of origin is mentioned, but the manufacturer (Siemens Healthcare GmbH) is based in Germany, and Siemens Medical Solutions USA, Inc. is the submitter. The data was "retrospectively created from the ground truth by data manipulation and augmentation."
• Deep Resolve Sharp: The document doesn't explicitly state a separate "test set" size. It mentions "Training and Validation data" from "on more than 10,000 high resolution 2D images."
  • Data Provenance: Similar to Deep Resolve Boost, the data covered a broad range of body parts, contrasts, fat suppression techniques, orientations, and field strength. Data was "retrospectively created from the ground truth by data manipulation." No specific country of origin is mentioned.

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

Not specified. The document states that the acquired datasets "represent the ground truth." There is no mention of expert involvement in establishing ground truth for the test sets. The focus is on technical metrics (PSNR, SSIM) and "visual comparisons" or "visual rating" which implies expert review, but the number and qualifications are not provided.

4. Adjudication Method for the Test Set

Not explicitly stated. The document mentions "visual comparisons" for Deep Resolve Boost and "visual rating" for Deep Resolve Sharp. This suggests subjective human review, but no specific adjudication method (like 2+1 or 3+1 consensus) is 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

No MRMC comparative effectiveness study is described for the AI features. The studies mentioned (sections 8 and 9) focus on evaluating the technical performance and image quality of the AI algorithms themselves, not on their impact on human reader performance.

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

Yes, standalone performance evaluation of the algorithms was conducted. The "Test Statistics and Test Results Summary" for both Deep Resolve Boost and Deep Resolve Sharp detail the evaluation of the network's impact using quantitative metrics (PSNR, SSIM, perceptual loss) and qualitative assessments ("visual comparisons," "visual rating," "intensity profile comparisons"). This represents the algorithm's performance independent of a human reader's diagnostic accuracy.

7. The Type of Ground Truth Used

The ground truth used for both Deep Resolve Boost and Deep Resolve Sharp was the acquired datasets themselves, representing the original high-quality or reference images/slices.

• For Deep Resolve Boost, input data was "retrospectively created from the ground truth by data manipulation and augmentation," including undersampling k-space lines, lowering SNR, and mirroring k-space data. The original acquired data serves as the target "ground truth" for the AI to reconstruct/denoise.
• For Deep Resolve Sharp, input data was "retrospectively created from the ground truth by data manipulation," specifically by cropping k-space data to create low-resolution input, with the original high-resolution data serving as the "output / ground truth" for training and validation.

8. The Sample Size for the Training Set

• Deep Resolve Boost:
  • TSE: more than 25,000 slices
  • HASTE: pre-trained on the TSE dataset and refined with more than 10,000 HASTE slices
  • EPI Diffusion: more than 1,000,000 slices
• Deep Resolve Sharp: more than 10,000 high resolution 2D images.

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

The ground truth for the training set was established as the acquired, unaltered (or minimally altered, e.g., removal of k-space lines to simulate lower quality input from high quality ground truth) raw imaging data.

• For Deep Resolve Boost: "The acquired datasets (as described above) represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation and augmentation." This implies that the original, high-quality scans were considered the ground truth, and the AI was trained to restore manipulated, lower-quality versions to this original quality.
• For Deep Resolve Sharp: "The acquired datasets represent the ground truth for the training and validation. Input data was retrospectively created from the ground truth by data manipulation. k-space data has been cropped such that only the center part of the data was used as input. With this method corresponding low-resolution data as input and high-resolution data as output / ground truth were created for training and validation." Similar to Boost, the original, higher-resolution scans served as the ground truth.

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Prism is a neurofeedback software device intended for relaxation and stress reduction through the use of EEG biofeedback. The device is indicated as an adjunctive treatment of symptoms associated with posttraumatic stress disorder (PTSD), to be used under the direction of a healthcare professional, together with other pharmacological and/or non-pharmacological interventions.

Prism is a software as medical device, to be prescribed for treatment of patients with PTSD by clinicians as adjunct to standard of care. Prism is a software device running on a laptop that uses EEG signal input from an EEG device (g.Nautilos PRO (K171669)). Prism therapy consists of 15, 30-minute sessions and optional periodic refresher sessions. During a session, the patient is connected to 8 or more EEG channels and views an interactive audio/visual interface.

Acceptance Criteria and Device Performance Study for Prism Device

The Prism device is a neurofeedback software device intended for relaxation and stress reduction through the use of EEG biofeedback, specifically indicated as an adjunctive treatment for symptoms associated with Posttraumatic Stress Disorder (PTSD). The acceptance criteria for its effectiveness were defined by the primary performance measure of a prospective, single-arm, open-label, unblinded study.

The primary effectiveness hypothesis was that from baseline to the 3-month follow-up, at least 50% of study participants would experience a response to the treatment, defined as a 6-point reduction in the Clinician Administered PTSD Scale (CAPS-5) score from baseline.

1. Acceptance Criteria and Reported Device Performance

Table: Acceptance Criteria and Reported Device Performance

• 54.55% of participants showed a ≥10-point reduction in CAPS-5 at 3 months follow-up.
• 50.00% of participants showed a ≥13-point reduction in CAPS-5 at 3 months follow-up. |

Safety Acceptance Criteria (Implicit):
The pre-specified safety goals of the study were met, indicating an acceptable safety profile. This would implicitly mean that the incidence and severity of adverse events were within acceptable clinical limits for the target population and device type.

2. Sample Size and Data Provenance

• Test Set (Clinical Study Participants):

  • Screened: 101 subjects
  • Full Analysis Dataset: 79 subjects
  • Efficacy Analysis Set: 66 subjects (This is the primary sample size for evaluating the effectiveness against the acceptance criteria).
  • Per-Protocol Analysis Set: 63 subjects

• Data Provenance: The study was a prospective, single-arm, open-label, unblinded study. It was conducted at 4 sites outside the United States (OUS) in Israel and one US site. This indicates a combination of Israeli and US data.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used to establish ground truth for the test set. However, it mentions:

• Diagnosis of PTSD: Established according to the DSM-5 criteria and CAPS-5.
• Clinician assessments: Performed and documented by the investigator or qualified and trained designee.

This implies that the ground truth for PTSD diagnosis and symptom severity (CAPS-5 scores) was established by licensed healthcare professionals (investigators or their designees) who were qualified to administer and interpret DSM-5 and CAPS-5. While specific qualifications (e.g., "radiologist with 10 years of experience") are not provided, "qualified and trained designee" suggests adherence to clinical standards for diagnostic assessment.

4. Adjudication Method for the Test Set

The document does not describe a formal adjudication method (like 2+1 or 3+1 consensus) for the initial PTSD diagnosis or CAPS-5 scoring. The assessments were performed by the "investigator or qualified and trained designee." This implies that the initial assessment by a single qualified professional served as the ground truth.

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

There is no indication of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance. The study described is a single-arm clinical study evaluating the device's adjunctive efficacy.

6. Standalone (Algorithm Only) Performance

The study evaluates the Prism software in conjunction with an EEG device (g.Nautilos PRO) as an adjunctive treatment for PTSD. Therefore, this is not a study of standalone algorithm performance without human-in-the-loop, as the device provides visual feedback to the patient to aid in learning to control EEG activity, and it is used under the direction of a healthcare professional.

7. Type of Ground Truth Used

The ground truth for effectiveness was based on:

• Clinical Assessments: Clinician Administered PTSD Scale (CAPS-5) scores. CAPS-5 is a structured interview administered by a trained clinician and is considered the gold standard for PTSD diagnosis and severity assessment.
• Self-Report Questionnaires: (Secondary measures, not primary ground truth for the acceptance criteria) PTSD Checklist for DSM-5 (PCL-5), Emotion Regulation Questionnaire (ERQ), Patient Health Questionnaire (PHQ-9), Clinical Global Impression (CGI).

8. Sample Size for the Training Set

The document does not provide information regarding the training set size or how the device's algorithms were trained. The provided text describes the clinical study for device validation, not the development or training phase of the software.

9. How Ground Truth for Training Set was Established

As information on the training set is not provided, the method for establishing its ground truth is also not described in this document.

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The Prismaflex ST set is a single use device that provides blood purification through a semipermeable membrane.

The Prismaflex ST set is for use only in conjunction with the PrismaFlex control unit or with the PrisMax control unit (in countries where PrisMax is cleared or registered).

All treatments administered via the PrismaFlex ST sets must be prescribed by a physician. The size, weight, state of uremia, cardiac status, and general physical condition of the patient must be carefully evaluated by the prescribing physician before each treatment.

If patients suffer from acute kidney injury and / or volume overload, the Prismatlex ST set is indicated for continuous renal replacement therapies (CRRT), in modalities such as:

• · Slow Continuous UltraFiltration (SCUF)
• · Continuous Veno-Venous Hemofiltration (CVVH)
• · Continuous Veno-Venous HemoDialysis (CVVHD)
• Continuous Veno-Venous HemoDiaFiltration (CVVHDF)

to perform fluid management and reduction of uremic toxins.

The Prismaflex ST100 and ST150 set is indicated for use in patients with a body weight equal or greater than 30 kg (661b) and Prismaflex ST60 set is indicated to patients with a body weight greater than 11kg (24lb).

The Prismaflex ST60/ST100/ST150 set is a disposable, extracorporeal circuit for use with the PrismaFlex system, or with the PrisMax system. The Prismaflex ST60/ST100/ST150 set consists of an AN69 ST hollow fiber haemofilter/dialyser and tubing system.

These Prismaflex disposable sets allow the following fluid management and renal replacement therapies to be performed :

• SCUF: Slow Continuous Ultrafiltration
• CVVH: Continuous Veno-Venous Hemofiltration
• CVVHD: Continuous Veno-Venous Hemodialysis
• CVVHDF: Continuous Veno-Venous Hemodiafiltration

The fluid pathways of the Prismaflex set are guaranteed sterile and pyrogen-free. The Prismaflex set is sterilized by ethylene oxide (EO).

The shelf life of the Prismaflex ST60/ST100/ST150 sets is 24 months from the date of sterilization. The device is intended for single use.

The provided text describes a 510(k) premarket notification for the "Prismaflex ST set" (ST60/ST100/ST150 sets), a high permeability hemodialysis system. The submission aims to demonstrate substantial equivalence to legally marketed predicate devices. The document focuses on non-clinical testing to support this claim.

Here's an analysis of the acceptance criteria and the study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding device performance values like a clinical trial report would for specific endpoints (e.g., sensitivity, specificity for diagnostic devices, or specific measurable outcomes for therapeutic devices).

Instead, it states that the device was evaluated against established international standards and internal performance requirements. The "reported device performance" is described qualitatively as meeting these standards and requirements.

Here's a synthesized representation based on the information provided, inferring "acceptance criteria" from the standards and tests mentioned:

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

The provided text describes non-clinical bench and pre-clinical testing. These are laboratory-based tests of the device's physical and chemical properties, as well as its interaction with biological models (e.g., blood, cell cultures).

• Sample Size for Test Set: The document does not specify the exact number of units/sets tested for each performance characteristic. In bench testing for medical devices, this often involves a pre-defined number of samples per batch or according to a statistical sampling plan to ensure reliability and representativeness for the specific test (e.g., n=3, n=5, n=10 per test condition). However, these specific numbers are not disclosed in this summary.
• Data Provenance: The data provenance is from non-clinical (bench and pre-clinical) laboratory testing. There is no mention of human subject data, retrospective, or prospective studies involving patients.

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

This question is not applicable to the type of study described. "Ground truth" established by experts (like radiologists for image analysis) is relevant for clinical studies, especially those involving human interpretation or diagnostic accuracy. The studies detailed here are non-clinical, focusing on the device's physical and chemical performance, where "ground truth" is typically defined by objective measurements against established engineering specifications and international standards, not expert consensus.

4. Adjudication Method for the Test Set

This question is not applicable. Adjudication methods (e.g., 2+1, 3+1) refer to procedures for resolving disagreements among multiple human readers/experts in clinical studies, particularly in diagnostic accuracy assessments. As this is a non-clinical device performance study, such a method would not be used.

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

No, an MRMC comparative effectiveness study was not done. The document explicitly states that the safety and performance were evaluated through non-clinical testing. There is no mention of human readers, AI assistance, or comparative effectiveness studies involving human-in-the-loop performance.

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

This question is not applicable. The device described, the "Prismaflex ST set," is a medical device for blood purification (hemodialysis/hemofiltration), not an AI algorithm or a diagnostic tool that would typically have a "standalone" algorithmic performance. The "performance" here refers to its physical and functional operation.

7. Type of Ground Truth Used

The "ground truth" for these non-clinical tests is established by:

• Objective Measurements: Directly measuring physical and chemical properties (e.g., ultrafiltration rate, clearances, pressure drop) using calibrated equipment.
• International Standards: Compliance with recognized international standards (ISO 8637-1, ISO 8638, ISO 10993 series) which define acceptable ranges and methodologies.
• Device Specifications: Meeting internal design specifications for the device.

There is no use of expert consensus, pathology, or outcomes data as "ground truth" in these non-clinical tests.

8. Sample Size for the Training Set

This question is not applicable. The Prismaflex ST set is a hardware medical device; its development and validation do not involve "training sets" in the context of machine learning or AI algorithms. The "training" that occurs is in the manufacturing and quality control processes to ensure consistency and adherence to specifications.

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

This question is not applicable, as there is no "training set" in the context of an AI/machine learning algorithm for this device.

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Promogran Prisma™, when used without ActiV.A.C.™ Negative Pressure Wound Therapy, is intended for the management of exuding wounds. Under the supervision of a health care professional, Promogran Prisma™ may be used for the management of:

• Diabetic ulcers
• Venous ulcers
• Pressure ulcers
• Ulcers caused by mixed vascular etiologies
• Full-thickness & partial thickness wounds
• Donor sites and other bleeding surface wounds
• Abrasions
• Traumatic wounds healing by secondary intention
• Dehisced surgical wounds.

Promogran Prisma™ when used with ActiV.A.C.TM Negative Pressure Wound Therapy is intended for the management of exuding wounds. Under the supervision of a health care professional, Promogran Prisma™ with ActiV.A.C.™ Negative Pressure Wound Therapy may be used only for the management of:

• Diabetic ulcers
• Venous ulcers
• Pressure ulcers
• Partial-thickness burns
• Traumatic wounds healing by secondary intention
• Dehisced surgical wounds.

Compression therapy may only be used with Promogran Prisma™ under professional healthcare supervision. Compression therapy may not be used when Promogran Prisma™ is used with ActiV.A.C.TM Negative Pressure Wound Therapy.

3MTM Promogran Prisma™ is comprised of a sterile, freeze-dried composite of 44% oxidized regenerated cellulose (ORC), 55% collagen and 1 % silver ORC. Silver ORC contains 25% w/w ionically bound silver.

It is a primary dressing that can be cut with scissors to fit the wound and used in combination with either a semiocclusive or non-occlusive secondary dressing. The dressing is hexagonal in shape, provided in two sizes (28 cm² and 123 cm²) that are packaged in a hexagonal thermoformed tray and sterilized by gamma irradiation.

As described in the product labeling, when used with the ActiV.A.C.TM Negative Pressure Wound Therapy System, seven slits are cut into the 3M™ Promogran Prisma™ by the health care provider before applying the dressing and the components of the ActiV.A.C.™ Negative Pressure Wound Therapy System.

After reviewing the provided document, it is not possible to describe the acceptance criteria and the study that proves the device meets the acceptance criteria as requested.

The document is an FDA 510(k) clearance letter and its associated summary for the PROMOGRAN PRISMA Matrix wound dressing. This document primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device, rather than providing detailed acceptance criteria and performance data for an AI/ML-driven medical device.

Here's why the requested information cannot be extracted:

• Device Type: PROMOGRAN PRISMA Matrix is a wound dressing, a physical medical device, not an AI/ML-driven diagnostic or therapeutic system.
• Study Focus: The "Performance Data" section (page 11) explicitly states:
  • "Summary of non-clinical tests conducted for determination of substantial equivalence": This refers to biocompatibility and bench studies for the physical dressing and its compatibility with Negative Pressure Wound Therapy, not an algorithm's performance.
  • "Summary of clinical tests conducted for determination of substantial equivalence": This states "No clinical tests were necessary to demonstrate acceptable use of the Promogran Prisma™ with ActiV.A.C.™ Negative Pressure Wound Therapy." It mentions a "human factors engineering assessment" with 30 subject nurses and doctors to ensure changes to labeling for combined use are safe and effective. This is not a study proving an AI/ML device's diagnostic performance.
• Absence of AI/ML Specifics: There is no mention of algorithms, machine learning models, image analysis, diagnostic performance metrics (e.g., sensitivity, specificity, AUC), ground truth establishment by experts, test sets, training sets, or MRMC studies, all of which are pertinent to AI/ML device evaluations.

Therefore, the requested tables and details pertaining to AI/ML device acceptance criteria and performance studies are not present in this document. The document describes a traditional medical device's clearance process based on substantial equivalence.

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The CATS®-D Tonometer Prism is intended to be used with Goldmann type tonometers for the measurement of intraocular pressure of the human eye.

The CATS®-D Tonometer Prism is used as an optical image prism for Goldmann applanation style tonometers. It is made of PMMA, the corneal contact diameter is 6.5 mm and the total length of the prism is 30 mm.

This document does not contain information related to AI/ML device performance or clinical studies typical for AI/ML-based medical devices. The submission (K203850) is for a CATS®-D Tonometer Prism, which is a physical accessory for Goldmann type tonometers, not an AI/ML algorithm.

The core of the submission focuses on demonstrating substantial equivalence to a predicate device (CATS® Reusable Tonometer prism K173904) through performance data related to:

• Design verification: Ensuring the device meets its design specifications.
• Sterilization validation and shelf-life testing: Confirming the sterility and stability of the disposable prism.
• Biocompatibility testing: Cytotoxicity, sensitization, and irritation tests according to ISO 10993 standards.

There is no mention of:

• Acceptance criteria for an algorithm's performance (e.g., accuracy, sensitivity, specificity).
• A test set with ground truth or expert annotations.
• Sample sizes for AI/ML model testing or training.
• The number or qualifications of experts.
• MRMC studies or human reader improvement with AI assistance.
• Standalone algorithm performance.

Therefore, I cannot provide a response to the prompt's specific requirements, as they are geared towards AI/ML device evaluations, which are not relevant to this 510(k) submission.

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Your MAGNETOM system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces transverse, sagittal, coronal, and oblique cross sectional images and/or spectra, and that displays the internal structure and/or function of the head, body, or extremities. Other physical parameters derived from the images and/or spectra may also be produced. Depending on the region of interest, contrast agents may be used. These images and/ or spectra and the physical parameters derived from the images and/or spectra, when interpreted by a trained physician, vield information that may assist in diagnosis.

Your MAGNETOM system may also be used for imaging during interventional procedures when performed with MR compatible devices such as in-room display and MR-safe biopsy needles.

MAGNETOM Aera, MAGNETOM Skyra and MAGNETOM Prisma/ Prisma/ with software syngo MR XA30A, include new and modified hardware and software compared to the predicate device, MAGNETOM Vida with software syngo MR XA20A. A high-level summary of the new and modified hardware and software is provided below:

Hardware

• New Computer

Software
New Features and Applications

• SVS EDIT is a special variant of the SVS SE pulse sequence type, which acquires two different spectra (one with editing pulses on resonance, one with editing pulses off resonance) within a single sequence.
• BEAT_FQ_nav is a pulse sequence that allows the user to make use of navigator echo based respiratory gating for flow imaging to acquire 4D flow data. Both navigator echo based respiratory gating and flow imaging are cleared features available on the predicate device. However, the combination of the two is new.
• Injector coupling is a software application that allows the connection of certain contrast agent injectors to the MR system for simplified, synchronized contrast iniection and examination start.
• The Prostate Dot Engine provides an assisted and guided workflow for prostate imaging. This automated workflow leads to higher reproducibility of slice angulation and coverage based on the segmentation algorithm described and cleared with syngo.via VB40; this may support exams not having to be repeated.

Modified Features and Applications

• An optimized high bandwidth inversion recovery pulse is combined with gradient echo readout to improve diagnostic image quality when imaging myocardial tissue.
• The AbsoluteShim mode is a shimming procedure based on a 3-echo gradient echo protocol.

Other Modifications and / or Minor Changes

• Elastography-AddIn synchronizes settings between the Elastography sequence and the active driver.
• HASTE MoCo is an image-based motion correction function in the averagedimension for the HASTE pulse sequence type.
• Coil independent pulse sequences remove the coil information from the pulse sequences and generate this information during run-time from automatic coil detection and localization.

The provided document is a 510(k) summary for the Siemens MAGNETOM Aera, MAGNETOM Skyra, and MAGNETOM Prisma/Prismafit MR systems with syngo MR XA30A software. It details the device's substantial equivalence to a predicate device but does not describe specific acceptance criteria for a new feature's performance or a study demonstrating the device meets such criteria.

The document primarily focuses on demonstrating substantial equivalence by outlining:

• Device Description: New and modified hardware/software features (e.g., SVS EDIT, BEAT_FQ_nav, Injector coupling, Prostate Dot Engine, optimized high bandwidth inversion recovery pulse, AbsoluteShim mode, Elastography-AddIn, HASTE MoCo, Coil independent pulse sequences).
• Nonclinical Tests: These include "Sample clinical images," "Image quality assessments using sample clinical images," "Performance bench test," and "Software verification and validation." The results "demonstrate that the devices perform as intended and are therefore, substantially equivalent to the predicate device to which it has been compared."
• Clinical Tests/Publications: No clinical tests were conducted to support substantial equivalence for the subject devices. However, "sample clinical images were provided," and "clinical publications were referenced to provide information on the use of the following features and functions" (listing publications for SVS_EDIT and Prostate Dot Engine).

Therefore, I cannot extract the specific information requested because it is not present in the provided text. The document does not contain:

• A table of acceptance criteria and reported device performance.
• Details on sample sizes, data provenance, number of experts for ground truth, or adjudication methods for any specific test set.
• Information regarding MRMC comparative effectiveness studies or standalone algorithm performance.
• Details on the type of ground truth used for specific features.
• Training set sample size or how ground truth was established for a training set.

The document's purpose is to show that the new/modified features are substantially equivalent to existing ones and perform as intended through verification and validation activities, rather than presenting a performance study against predefined acceptance criteria for novel functionalities.

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