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Vista OS is an accessory to 1.5T and 3.0T whole-body magnetic resonance diagnostic devices (MRDD). It is intended to operate alongside, and in parallel with, the existing MR console to acquire traditional, real-time and accelerated images.

Vista OS software controls the MR scanner to acquire, reconstruct and display static and dynamic transverse, coronal, sagittal, and oblique cross-sectional images that display the internal structures and/or functions of the entire body. The images produced reflect the spatial distribution of nuclei exhibiting magnetic resonance. The magnetic resonance properties that determine 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 may assist in the determination of a diagnosis.

Vista OS is intended for use as an accessory to the following MRI systems:

Manufacturers: GE Healthcare (GEHC), Siemens Healthineers
Field Strength: 1.5T and 3.0T
GE Software Versions: 12, 15, 16, 23, 24, 25, 26, 30
Siemens Software Versions: N4/VE; NX/VA

The Vista AI "Vista OS" product provides a seamless user experience for performing MRI studies on GE and Siemens scanners. The underlying software platform that we use to accomplish this task is called "RTHawk".

RTHawk is a software platform designed from the ground up to provide efficient MRI data acquisition, data transfer, image reconstruction, and interactive scan control and display of static and dynamic MR imaging data. It can control MR pulse sequences provided by Vista AI and, on scanners that support it, it can equally control MR pulse sequences provided by the scanner vendor. Scan protocols can be created by the user that mix and match among all available sequences.

RTHawk is an accessory to clinical 1.5T and 3.0T MR systems, operating alongside, and in parallel with, the MR scanner console with no permanent physical modifications to the MRI system required.

The software runs on a stand-alone Linux-based computer workstation with color monitor, keyboard and mouse. It is designed to operate alongside, and in parallel with, the existing MR console with no hardware modifications required to be made to the MR system or console. This workstation (the "Vista Workstation") is sourced by the Customer in conformance with specifications provided by Vista AI, and is verified prior to installation.

A private Ethernet network connects the Vista Workstation to the MR scanner computer. When not in use, the Vista Workstation may be detached from the MR scanner with no detrimental, residual impact upon MR scanner function, operation, or throughput.

RTHawk is an easy-to-use, yet fully functional, MR Operating System environment. RTHawk has been designed to provide a platform for the efficient acquisition, control, reconstruction, display, and storage of high-quality static and dynamic MRI images and data.

Data is continuously acquired and displayed. By user interaction or data feedback, fundamental scan parameters can be modified. Real-time and high-resolution image acquisition methods are used throughout RTHawk for scan plane localization, for tracking of patient motion, for detection of transient events, for on-the-fly, sub-second latency adjustment of image acquisition parameters (e.g., scan plane, flip angle, field-of-view, etc.) and for image visualization.

RTHawk implements the conventional MRI concept of anatomy- and indication-specific Protocols (e.g., ischemia evaluation, valvular evaluation, routine brain, etc.). Protocols are pre-set by Vista AI, but new protocols can be created and modified by the end user.

RTHawk Apps (Applications) are composed of a pulse sequence, predefined fixed and adjustable parameters, reconstruction pipeline(s), and a tailored graphical user interface containing image visualization and scan control tools. RTHawk Apps may provide real-time interactive scanning, conventional (traditional) batch-mode scanning, accelerated scanning, or calibration functions, in which data acquired may be used to tune or optimize other Apps.

When vendor-supplied pulse sequences are used in Vista OS, parameters and scan planes are prescribed in the Vista interface and images reconstructed by the scanner appear on the Vista Workstation. RTHawk Apps and vendor-supplied sequences can be mixed within a single protocol with a unified user experience for both.

Here's a breakdown of the acceptance criteria and study information for Vista OS, Vista AI Scan, and RTHawk, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes several clinical verification studies for new AI-powered features. Each feature has specific acceptance criteria.

2. Diagnostic quality of denoised data judged superior to paired non-denoised series in > 80% of test cases. | Meets or exceeds |
   | Automatic Brain Localizer Prescriptions | Mean error in plane angulation

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The B-Scan module is used for imaging the internal structure of the eye, including the opaque media and posterior pathology, for the purpose of diagnosing pathological or traumatic conditions in the eye. The probe is intended to be used on both adult and pediatric patients that require imaging of the eye.

The B-Scan device is designed as an ultrasound B-Scan, which uses pulsed echo ultrasound to image the structure of the eye. It utilizes an eye-contact probe to generate and receive the ultrasound pulse signals and provides a graphic display of returning pulse echoes to indicate the various structures.

All of the critical functions of the B-Scan are calculated in the same manner as in the predicate device, B-Scan Plus. The software algorithms for clinically critical functions remain the same as in the predicate device. However, the user interface and the workflow of B-Scan have enhancement to support cybersecurity implementation.

Both the subject device and predicate device are compatible with the Connect Software (K070943, K123349) and 4Sight (K152573). The software improvements in Connect were focused on enhancing features that optimize integration with the personal computer's processing, data storage, display, and printing capabilities.

The energy source for the B-Scan is USB power as in the predicate device, B-Scan Plus.

The software utilized on both the Connect and 4Sight platforms is fundamentally identical in core clinical functions, including image scanning, rendering, IOL calculations, data storage, and report formatting. Additionally, the key features within the B-Scan module do not differ significantly between the two systems, nor do they make an impact on the established clinical workflow.

Track 1 is being followed for this 510(k) submission.

Here's a breakdown of the acceptance criteria and the study details for the B-Scan Ultrasonic Imaging System, based on the provided FDA 510(k) clearance letter:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary primarily focuses on demonstrating substantial equivalence to a predicate device rather than explicitly stating pre-defined "acceptance criteria" for a novel device performance. However, we can infer acceptance criteria from the comparison table (Table 2) and the performance data section, where the subject device's performance is either "Same" or "SE" (Substantially Equivalent) to the predicate, or explicitly lists performance metrics.

For quantitative metrics, the "acceptance criteria" for the subject device can be interpreted as performing comparably or within acceptable limits relative to the predicate device, or meeting specific new specifications.

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

The document does not explicitly state a sample size for patients or images used in any clinical or test set. The performance data mostly refers to bench testing and validation against standards.

• The only mention of a "sample" related to performance is "average time recorded across 5 probes" for the image preview time. This refers to hardware units, not patient data.
• Data Provenance: Not applicable, as no external data set or clinical study on patients is described beyond bench tests. The focus is on device specifications and in-house validation.

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

Not applicable. The submission does not describe a study involving expert readers establishing ground truth for a test set of images for diagnostic performance evaluation. The "Performance" section outlines bench tests and compliance with recognized standards.

4. Adjudication Method for the Test Set

Not applicable. Since there's no mention of a clinical test set requiring human expert review to establish ground truth, there is no adjudication method described.

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

No. The document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study, nor does it quantify improvement in human readers with AI assistance. The device is purely an imaging system, and there is no mention of AI assistance for image interpretation.

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

The device described is an "B-Scan Ultrasonic Imaging System," which is a diagnostic tool that produces images for human interpretation. It does not contain an AI algorithm for standalone diagnostic performance. Its performance is related to image acquisition parameters, accuracy of measurements, and adherence to safety/design standards.

7. Type of Ground Truth Used

For the quantitative performance claims (e.g., Clinical Accuracy Line/Area), the ground truth was established by physical measurements against known standards or calibrated references during bench testing ("Physical Accuracy and Range Test"). For other aspects, the ground truth is adherence to technical specifications, safety standards, and validated manufacturing/reprocessing procedures.

8. Sample Size for the Training Set

Not applicable. This device is an ultrasound imaging system, not an AI-driven diagnostic algorithm that requires a "training set" of data in the typical machine learning sense. The software aspects mentioned are primarily for device control, image rendering, data storage, and report formatting, enhanced for cybersecurity and usability – not for learning from data to perform a diagnostic task.

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

Not applicable, as there is no training set mentioned for an AI algorithm.

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This device is indicated to acquire and display cross-sectional volumes of the whole body (abdomen, pelvis, chest, extremities, and head) of adult patients.

TSX-501R has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

CT Scanner TSX-501R/1 V11.1 employs a next-generation X-ray detector unit (photon counting detector unit), which allows images to be obtained based on X-rays with different energy levels. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided 510(k) clearance letter.

It's important to note that a 510(k) summary typically doesn't provide the full, granular detail of a clinical study report. The information often indicates what was tested and the conclusion, but less about the specific methodologies, statistical thresholds for acceptance, or detailed performance metrics.

Understanding the Context: 510(k) Clearance

This document is a 510(k) clearance letter for a new CT scanner (CT Scanner TSX-501R/1 V11.1). The primary goal of a 510(k) submission is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to prove absolute safety and effectiveness through extensive new clinical trials (which is more typical for a PMA - Premarket Approval). Therefore, the "acceptance criteria" and "study" described here are geared towards demonstrating this equivalence.

The core technology difference is the shift from an Energy Integrating Detector (EID) in the predicate to a Photon Counting Detector in the new device. The testing focuses on ensuring this new detector performs equivalently or better in terms of image quality and safety.

Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) for a CT scanner's hardware update (new detector), the "acceptance criteria" are implicitly tied to demonstrating equivalent or improved image quality and safety compared to the predicate device. The performance is assessed through bench testing with phantoms and review of clinical images.

Table of Acceptance Criteria and Reported Device Performance:

• Contrast-to-Noise Ratios (CNR)
• CT Number Accuracy
• Uniformity
• Pulse Pile Up
• Slice Sensitivity Profile (SSPz)
• Modulation Transfer Function (MTF)
• Standard Deviation of Noise and Pulse Pile
• Noise Power Spectra (NPS)
• Low Contrast Detectability (LCD) | "It was concluded that the subject device demonstrated equivalent or improved performance, compared to the predicate device, as demonstrated by the results of the above testing." |
  | Fundamental Properties of the Photon Counting Detector (using phantoms) | Effectiveness and equivalent performance compared to expected or predicate device for:
• Detector resolution and noise properties (MTF and DQE)
• Artifact analysis
• Count rate vs. current curve
• Pulse pileup or maximum count rate
• Lag/residual signal levels
• Stability over time
• Bad pixel map | "These bench studies utilized phantom data and achieved results demonstrative of equivalent performance in comparison with the predicate device." |
  | Clinical Image Quality (Human Review) | Reconstructed images using the subject device are of diagnostic quality. | "It was confirmed that the reconstructed images using the subject device were of diagnostic quality." |
  | Safety & Standards Conformance | Conformance to relevant electrical, radiation, software, and cybersecurity standards and regulations. | "This device is in conformance with the applicable parts of the following standards [list provided]... Additionally, this device complies with all applicable requirements of the radiation safety performance standards..." |
  | Risk Analysis & Verification/Validation | Established specifications for the device have been met, and risks are adequately managed. | "Risk analysis and verification/validation activities conducted through bench testing demonstrate that the established specifications for the device have been met." |
  | Software Documentation & Cybersecurity | Adherence to FDA guidance documents for software functions and cybersecurity. | "Software Documentation for a Basic Documentation Level... is included... Cybersecurity documentation... was included..." |

Study Details:

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

   • Test Set (Clinical Images): The specific number of clinical images/cases reviewed is not provided. The text states "Representative chest, abdomen, brain and MSK diagnostic images." This implies a selection of images from various body regions.
   • Data Provenance: The document does not specify the country of origin for the clinical images. It also does not explicitly state whether the data was retrospective or prospective, though for a 510(k) supporting equivalence, retrospective data collection for image review is common.

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

   • Number of Experts: The document states "reviewed by American Board-Certified Radiologists." The specific number is not provided.
   • Qualifications: "American Board-Certified Radiologists." This indicates a high level of qualification and experience in medical imaging interpretation.

3. Adjudication Method for the Test Set:

   • The document does not specify an adjudication method (like 2+1 or 3+1) for the clinical image review. It simply states they were "reviewed by American Board-Certified Radiologists" and "it was confirmed that the reconstructed images using the subject device were of diagnostic quality." This implies a consensus or individual assessment of diagnostic quality, but the process is not detailed.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done? No, a formal MRMC comparative effectiveness study demonstrating how human readers improve with AI vs. without AI assistance was not conducted or described for this submission. This makes sense as the device is a CT scanner itself, not an AI-assisted diagnostic software. The clinical image review was to confirm diagnostic quality of the images produced by the new scanner, not to assess reader performance with or without an AI helper.

5. Standalone (Algorithm Only) Performance:

   • Was it done? Yes, in a sense. The "bench testing" focusing on Objective Image Quality Evaluations and Fundamental Properties of the Photon Counting Detector can be considered "standalone" performance for the device's imaging capabilities. These tests used phantoms and measured technical specifications without human interpretation as the primary endpoint. The device's stated function is to acquire and display images, so its "standalone" performance is its ability to produce good images.

6. Type of Ground Truth Used:

   • Bench Testing (Phantoms): The ground truth is the physical properties of the phantoms and the expected performance characteristics based on established physics and engineering principles (e.g., a known object size for MTF, known density for CT number accuracy).
   • Clinical Images: The ground truth for confirming "diagnostic quality" is expert consensus/opinion from American Board-Certified Radiologists. It's an assessment of whether the image contains sufficient information and clarity for diagnostic purposes, not necessarily a comparison to a biopsy or long-term outcome.

7. Sample Size for the Training Set:

   • The document does not mention a training set in the context of typical AI/machine learning development. This device is a CT scanner hardware system, not an AI diagnostic algorithm that learns from training data. Therefore, the concept of a "training set" as it relates to AI models is not applicable here.

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

   • As stated above, the concept of a "training set" as applied to AI/machine learning development does not directly apply to this CT scanner hardware submission. The device's performance is based on its physical design and engineering, not on learning from a large dataset.

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ScanDiags Ortho L-Spine MR-Q software is an image-processing and measurement software tool that provides quantitative spine measurements from previously-acquired DICOM lumbar spine Magnetic Resonance (MR) images for users' review, analysis, and interpretation. It provides the following functionality to assist users in visualizing, and documenting area and distance measurements of relevant anatomical structures (vertebral body, intervertebral disc, neuroforamina, thecal sac) of the lumbar spine:

Feature Segmentation;

Feature measurement; and

Export of measurement results to a PDF report containing measurement results and overlay images for user's review, revise and approval.

ScanDiags Ortho L-Spine MR-Q software does not produce or recommend any type of medical diagnosis or treatment. Instead, it simply helps users to more easily identify and classify features in lumbar MR images and compile a report. The user is responsible for reviewing and verifying the software-generated measurements and approving draft report content using their medical judgment and discretion.

The device is intended to be used only by hospitals and other medical institutions.

Only DICOM images of MRI acquired from lumbar spine exams of patients aged 22 and above are considered to be valid input. ScanDiags Ortho L-Spine MR-Q software does not support DICOM images of patients that are pregnant, undergo MRI scan with contrast media, have post-operational complications or infections.

ScanDiags Ortho L-Spine MR-Q software is a software as a medical device (SaMD) intended for visualization, and quantification of lumbar spine anatomical structures including vertebral bodies, intervertebral discs, neuroforamina, thecal sacs from a set of standard lumbar spine MRI images in DICOM (Digital Imaging and Communications in Medicine) format. The semi-automatic segmentation of these structures forms the bases for the distance and area measurement outputs. The software has features for log-in, viewing, revising, and saving measurement results in addition to generating PDF reports. The PDF report includes images, measurements.

ScanDiags Ortho L-Spine MR-Q software includes a viewing application (ScanDiags DICOM Viewer) to visualize, review, and apply corrections to the measurement results shown as overlay on the original lumbar spine MRI images. Pre-existing MR images of the lumber spine are uploaded into the software for analysis. The semi-automatic segmentations are based on deep convolutional neural networks (DCNN) which are developed by applying well-established supervised deep learning methods on unstructured MRI scans (DICOM image format). ScanDiags Ortho L-Spine MR-Q combines deep learning, image analysis, as well as regression-based machine methods. The segmentations and distance measurements are user modifiable. Results are reviewed and approved by the radiologist's user before a PDF report is generated. Once approved, the result PDF report is sent to the clinician's PACS system. The PACS system stores the PDF report within the corresponding patient MRI study.

ScanDiags Ortho L-Spine MR-Q does not interface directly with any MR or data collection equipment; instead, the software uploads data files previously generated by such equipment. Its functionality is independent of the vendor type of the acquisition equipment. The analysis results are available on the ScanDiags DICOM Viewer screen and can be edited, saved, and approved. The approved measurement results are sent back to the PACS system as a Measurement Result PDF Report. The software does not perform any functions that could not be accomplished by a trained user with manual tracing methods; the purpose of the software is to save time and automate the tedious manual task of segmentation and distance measurement.

ScanDiags Ortho L-Spine MR-Q software is an adjunct tool and is not intended to replace a radiologist's review of the MRI study, nor is it intended to replace his or her clinical judgment, and it does not detect, diagnose or identify any abnormalities. Radiologists must not use the generated output as a primary interpretation.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) submission information:

Acceptance Criteria and Reported Device Performance

The device, ScanDiags Ortho L-Spine MR-Q, uses machine learning (Deep Convolutional Neural Networks) for semi-automatic segmentation and quantitative measurements of lumbar spine anatomical structures from MRI images. Its performance was evaluated against ground truth established by expert radiologists.

Table of Acceptance Criteria and Reported Device Performance:

Note: The document states "The device successfully passed the primary ICC acceptance criteria," "The device successfully passes the secondary DICE acceptance criteria," and "The device successfully passes the co-secondary MAE acceptance criteria," implying that specific thresholds were met, though the exact numerical criteria are not explicitly stated in this summary.

Study Details Proving Acceptance Criteria

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 100 individual patient MRI studies.
   • Data Provenance: Retrospective, multicenter study. Data collected from two hospital groups in the United States: one in Missouri (18 patients from a rural hospital group) and one in North Carolina (82 patients from urban and rural hospital groups). Images were acquired from MRI systems from GE (40), Siemens Healthineers (42), and Philips (18).

2. Number of Experts and Qualifications:

   • Number of Experts: 3.
   • Qualifications: US board-certified MSK (Musculoskeletal) radiologists. (Specific years of experience are not mentioned, but board certification implies a certain level of expertise).

3. Adjudication Method for Ground Truth:

   • For Anatomic Structure Segmentation: Pixel-based majority opinion between the three radiologists.
   • For Area and Distance Measurements: Averaging the measurements of all three readers.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, a MRMC comparative effectiveness study was not done. The study focuses on the standalone performance of the algorithm against expert-derived ground truth. The device is described as an "adjunct tool" that requires human review and verification, but the provided performance data is for the algorithm itself, not human-AI collaboration.

5. Standalone Performance Study:

   • Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The "Machine Learning Performance Evaluation Summary" clearly outlines the results of the algorithm's performance (ICC, DSC, MAE) against the established ground truth.

6. Type of Ground Truth Used:

   • Expert Consensus: The ground truth was established by the consensus (majority opinion for segmentation, average for measurements) of three US board-certified MSK radiologists.

7. Training Set Sample Size:

   • The document states that images and cases used for verification and validation testing were "separate and carefully segregated from training datasets." However, the sample size for the training set is not provided in the excerpt. It mentions that the Deep Convolutional Neural Networks (DCNN) were developed by "applying well-established supervised deep learning methods on unstructured MRI scans (DICOM image format)."

8. How Ground Truth for Training Set was Established:

   • The document implies that the DCNN utilized "supervised deep learning methods." While it doesn't explicitly detail the ground truth establishment for the training set, it can be inferred that it involved labeled data, likely expert annotations, similar to how the test set ground truth was established, given the nature of supervised learning for medical imaging segmentation and measurement. However, the specific process (e.g., number of annotators, their qualifications, adjudication method) for the training set is not described in this provided text.

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The NUSONO Handheld Ultrasound Scanner is a portable and software-based ultrasound imaging system, indicated for diagnostic ultrasound imaging and fluid flow analysis in the following applications:

NUSONO-C35

Fetal, Abdominal, Pediatric, Urology, Gynecology, Lung

NUSONO-L75

Pediatric, Small Organ (Thyroid, Prostate, Scrotum, Breast), Musculoskeletal (Superficial and Conventional), Peripheral Vessel, Others (Carotid), Lung

NUSONO-P25

Fetal, Abdominal, Pediatric, Urology, Gynecology, Cardiac (adult and pediatric), Lung

The system provides diagnostic ultrasound imaging in B mode, M mode, Color Doppler mode, Power Doppler mode and combine mode (B+M. B+CD. B+PD), intended for use in environments where healthcare is provided by trained healthcare professionals. The environments where the system can be used include physician offices, clinics, hospitals, and clinical point of care for diagnosis of patients except environments where intensity of electromagnetic disturbances is high.

The NUSONO Handheld Ultrasound Scanner is a portable, software-controlled, diagnostic ultrasound system used to acquire and display high-resolution, real-time ultrasound data through an off-the-self (OTS) iOS 14, 15 and Android 12 or higher versions based mobile device. The system consists of a series of wireless transducers employing Wi-Fi-based technology to communicate with the NUSONO App on traditional smartphone/tablet devices via direct Wi-Fi. The NUSONO App provides the interface for mode/setting control and image display, acquisition, and storage functions. The 128-channel beamformer and image signal processing technology maximize the utility of all imaging transducer elements to enhance the diagnostic utility and confidence provided by the system.

The provided document describes the regulatory submission for the NUSONO Handheld Ultrasound Scanner, not a study evaluating its acceptance criteria. Therefore, most of the requested information cannot be extracted from the provided text.

Specifically:

• Acceptance Criteria and Reported Device Performance: The document does not list specific clinical acceptance criteria (e.g., sensitivity, specificity, accuracy for a particular diagnostic task) or the device's measured performance against such criteria. It notes non-clinical performance tests for compliance with general medical device standards.
• Sample size and data provenance for the test set: Not applicable as a clinical study proving acceptance criteria is not detailed.
• Number of experts and qualifications for ground truth: Not applicable.
• Adjudication method for the test set: Not applicable.
• Multi-reader multi-case (MRMC) comparative effectiveness study: Not mentioned.
• Standalone (algorithm only) performance: Not explicitly discussed in terms of a clinical study.
• Type of ground truth: Not applicable as no clinical study is presented.
• Sample size for the training set: Not applicable.
• How the ground truth for the training set was established: Not applicable.

The document focuses on demonstrating substantial equivalence to predicate devices through a comparison of intended use, technological characteristics, and compliance with non-clinical performance standards (electrical safety, EMC, software, biocompatibility, etc.). It explicitly states: "The NUSONO Handheld Ultrasound Scanner did not require clinical testing to establish substantial equivalence."

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The TiGEN Abutment, PMMA Abutment and Scan Healing Abutment are intended for use on endosseous dental implants in the edentulous or partially edentulous maxilla or mandible, as an aid in prosthetic rehabilitation.

The PMMA Abutment is indicated to be used pror to the final components to maintain, stabilize and shape the soft tissue during the healing plase. They must be placed out of occlusion and are for temporary use (3 months).

For TiGEN Abutment and PMMA Abutment, all digitally designed abutments for use with PMMA Abutment and TiGEN Abutment are intended to be sent to a MegaGen-validated milling center for manufacture.

The TiGEN Abutment is machined with the final prosthetic in accordance with the intraoral structure. It is machined by using dental CAD/CAM technology in accordance with customized patient's information in MegaGen-validated milling center. The TiGEN Abutment is made of Ti-6Al-4V ELI alloy. And It is provided with abutment screw. All TiGEN Abutment is provided non-sterile. The milled TiGEN Abutment must be sterilized by users prior to use.

The PMMA Abutment is a temporary prosthesis used until the final prosthesis is placed for up to three months. The PMMA Post is machined with the temporary prosthetic in accordance with the intraoral structure by using dental CAD/CAM technology. The PMMA Cuff is made of Ti-6Al-4V ELI alloy and available in various gingival heights. The PMMA Post is made of Polymethyl methacrylate and available in various diameters and lengths so that it can be used according to individual patient conditions. All PMMA Abutment is provided non-sterile with abutment screw. The milled PMMA Abutment must be sterilized by users prior to use.

The Scan Healing Abutment designed to aid in soft tissue contouring during the healing period after implant placement, creating an emergence profile for the final prosthesis. And they have the added design feature to be scannable an intraoral impression by digital scanner. The Scan Healing Abutment is provided with abutment screw and is provided gamma-sterile.

This document pertains to a 510(k) premarket notification for dental implant abutments and does not contain information about an AI/ML medical device. Therefore, a table of acceptance criteria and a study proving the device meets the criteria, as requested by the prompt, cannot be extracted from the provided text for an AI/ML context.

The document discusses the substantial equivalence of the "TiGEN Abutment, PMMA Abutment and Scan Healing Abutment" to already marketed predicate devices. The studies mentioned are primarily bench tests, biocompatibility evaluations, and sterilization/shelf-life validations, which are standard for physical medical devices. There is no mention of an AI/ML component, AI/ML device performance metrics, or related study methodologies like multi-reader multi-case (MRMC) studies.

Without information on an AI/ML component, the requested details such as sample size for test sets (for AI), data provenance, expert ground truth establishment, MRMC studies, standalone AI performance, and training set details are not applicable and cannot be provided.

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The Histolog® Scanner is a confocal laser system intended to allow imaging of the internal microstructure of tissues including, but not limited to, the identification of cells, vessels and their organization or architecture.

The Histolog® Scanner is a digital microscopy scanner for use on excised human tissue. Its operating principle is based on confocal fluorescence microscopy and uses non-ionizing, lowpower optical radiation (Class 1 laser product as per IEC 60825-1:2014-05). The Histolog® Scanner acquires digital images with high, micrometer-range resolution and enables the visualization of tissue microstructures down to the cellular level.

The Histolog® Scanner is based on a massively parallel signal acquisition and processing technology providing fast digital imaging over large areas. Image reconstruction does not involve any image stitching or any other similar image blending algorithms. Each pixel in the image is assigned an intensity value based on the light intensity collected by the detector for this particular position in the scan pattern.

The provided text does not contain the detailed information required to fulfill all aspects of the request regarding the device's acceptance criteria and the study proving it meets them. The document focuses on the regulatory submission and comparison to a predicate device, rather than a detailed clinical performance study.

Here's a breakdown of what can and cannot be extracted from the provided text:

What can be extracted:

• Acceptance Criteria for Non-Clinical Tests: The document lists acceptance criteria for various non-clinical performance and safety tests.
• Results for Non-Clinical Tests: The document states "PASS" for all listed internal validation tests.

What cannot be extracted (critical for a clinical performance study):

• Table of Acceptance Criteria and Reported Device Performance for Imaging Quality (Clinical): While "Imaging Quality" is listed as a test, the specific acceptance criteria (e.g., sensitivity, specificity, accuracy for a specific diagnostic task) and the actual reported performance values are not provided. The text only says "Histolog® Scanner system imaging requirements verification protocols. All requirements met." This is insufficient for a clinical performance study.
• Sample size used for the test set and data provenance: No information on the number of images/patients, or whether the data was retrospective/prospective or its origin.
• Number of experts used to establish ground truth and qualifications: No mention of experts or their qualifications.
• Adjudication method for the test set: No information.
• MRMC comparative effectiveness study details: No mention of human readers or AI assistance in a comparative study.
• Standalone (algorithm only) performance: While the device images tissue, there's no mention of an algorithm being evaluated in a standalone capacity against a ground truth. The device itself is the "scanner."
• Type of ground truth used: No mention of ground truth (e.g., pathology, outcomes).
• Sample size for the training set: The document discusses validation, not training.
• How the ground truth for the training set was established: Not applicable, as training data and ground truth establishment for AI are not mentioned.

Based on the provided text, here is the information that can be extracted and a clear indication of what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

For non-clinical safety and performance tests:

Missing: Specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy) and corresponding reported performance metrics for "Imaging Quality" related to the device's diagnostic capabilities or image interpretation for identifying specific microstructures (cells, vessels, organization). The document confirms internal verification protocols were met, but doesn't detail these protocols or their outcomes for clinical relevance.

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

Missing: No information regarding the sample size of any test set (e.g., number of tissue samples, patients, or images) used for evaluating the device's clinical performance or imaging quality related to microstructure identification. Data provenance (country of origin, retrospective/prospective) is also not provided.

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

Missing: The document does not describe any expert involvement in establishing ground truth for a test set.

4. Adjudication method for the test set

Missing: No information on an adjudication method.

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

Missing: No MRMC study is described. The device is a scanner intended for imaging the microstructure of tissues, not explicitly an AI-assisted diagnostic tool for human readers based on this submission.

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

Missing: While the device performs standalone imaging, the document doesn't describe a separate "algorithm only" performance evaluation that would assess, for example, automated detection or classification capabilities without human interpretation of the images. The device itself is the imaging system.

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

Missing: No information on the type of ground truth used for any clinical performance or imaging quality assessment.

8. The sample size for the training set

Missing: No information on a training set, as the document focuses on device performance validation rather than machine learning model development.

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

Missing: Not applicable, as detailed training data and its ground truth establishment are not mentioned.

Conclusion: The provided FDA submission letter and summary focus explicitly on demonstrating substantial equivalence to a predicate device primarily through non-clinical performance and safety data, and a high-level statement about meeting "imaging quality" requirements. It does not present a clinical performance study with detailed acceptance criteria, sample sizes, ground truth establishment, or human reader performance metrics that would be typical for an AI/CADe device or a device requiring such detailed clinical validation.

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MegaGen Dental Implant Abutment is intended to be surgically placed in the maxillary or mandibular areas for the purpose providing prosthetic support for dental restorations (Crown, bridges, and overdentures) in partially or fully edentulous individuals. It is used to restore a patients chewing function. All digitally designed abutments for use with ZrGEN Abutment are intended to be sent to a MegaGen validated milling center for manufacture.

The submission includes descriptions for the following devices: Scan Healing Abutment, Temporary Abutment, Temporary Cylinder, Comfort Cap, Healing Cap, Healing Cap Screw, Milling Abutment, EZ Post Abutment/Extra EZ Post Abutment, EZ Post Cylinder, ZrGEN Abutment, Multi-unit Abutment, Multi-unit Angled Abutment, AXA Abutment (Straight), AXA Abutment (Angled), Abutment Screw, Cylinder Screw, and Crown Screw. Each description details the intended use, material, surface treatment, sterilization, single use status, dimensions, and compatible implant systems.

The provided document, a 510(k) premarket notification from MegaGen Implant Co., Ltd. for their "MegaGen Dental Implant Abutment" device, focuses on demonstrating substantial equivalence to previously cleared predicate devices rather than proving that the device meets specific acceptance criteria through a standalone study.

For medical devices, especially those going through the 510(k) pathway, acceptance criteria are typically based on showing that the new device performs as safely and effectively as a legally marketed predicate device. The "study" proving this is primarily a non-clinical performance testing (bench testing) and a comparison to predicate devices. Clinical studies are often not required for 510(k) submissions, as explicitly stated in this document ("No clinical studies are submitted.").

Therefore, the acceptance criteria are implicitly defined by the performance characteristics of the predicate devices and general standards (like ISO 14801 for dental implants) as outlined in the "Summary of Non-Clinical Testing" section. The device performance is demonstrated through a comparative analysis to these predicates and the results of the bench testing.

Here's a breakdown of the information requested, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission, the "acceptance criteria" for the subject device are fundamentally its demonstration of substantial equivalence to predicate devices, meaning it performs as safely and effectively. The "reported device performance" is a direct comparison to the predicate devices and adherence to relevant standards.

The document provides extensive comparison tables for each component of the MegaGen Dental Implant Abutment system against its predicate and reference devices. Below is a representative excerpt from these tables, focusing on a few key components to illustrate the comparison:

Example: Scan Healing Abutment

Summary of Device Performance (Based on "Substantial Equivalence Discussion" sections for all components):

The subject device is deemed substantially equivalent to its predicate/reference devices across all listed components (Scan Healing Abutment, Temporary Abutment, Temporary Cylinder, Comfort Cap, Healing Cap, Healing Cap Screw, Milling Abutment, EZ Post Abutment/Extra EZ Post Abutment, EZ Post Cylinder, ZrGEN Abutment, Multi-unit Abutment, Multi-unit Angled Abutment, AXA Abutment (Straight), AXA Abutment (Angled), Abutment Screw, Cylinder Screw, Crown Screw).

Any identified differences in characteristics (e.g., specific dimensions like diameter, gingival height, post height, total length, or surface treatment for some components) are explicitly discussed and concluded not to affect substantial equivalence. This is often supported by arguing that the differences are minor, fall within the range of cleared devices, allow for more precise treatment, or are supported by bench testing (e.g., fatigue tests for worst-case scenarios).

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

The document does not detail specific sample sizes for test sets in the context of clinical data, as no clinical studies were submitted.

For non-clinical testing (bench tests), the document states:

• "The bench tests have been performed in accordance with 'ISO 14801' and the recommendations of 'Class II Special Controls Guidance Document: Root-form Endosseous Dental Implants and Endosseous Dental Implant Abutment' to evaluate the performance of the subject devices and the test results met the pre-set criteria."
• For ZrGEN Abutment, AXA Abutment (Angled type), and other potentially "worst-case" scenarios, fatigue tests were conducted to demonstrate performance and stability.

The data provenance is pre-market non-clinical testing data generated by the manufacturer. The country of origin for the manufacturing and testing is Republic of Korea (MegaGen Implant Co., Ltd. is based in Daegu, Republic of Korea). The data is prospective in the sense that it was specifically generated for this 510(k) submission to demonstrate equivalence.

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

This information is not applicable (N/A) as no clinical studies were conducted or submitted. For non-clinical bench testing, "ground truth" is established by adherence to recognized standards (like ISO 14801) and established testing methodologies, not typically by expert consensus of medical professionals on a test set.

4. Adjudication Method for the Test Set

This information is N/A as no clinical studies with human readers or image interpretation were conducted.

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

This information is N/A as no clinical studies were conducted, and certainly no MRMC studies involving human readers, as this is a physical dental implant component, not an AI or imaging device that would typically involve human-in-the-loop performance evaluation.

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

This information is N/A as the device is a physical dental implant abutment, not a software algorithm.

7. The Type of Ground Truth Used

For the purpose of this 510(k) submission, the "ground truth" for demonstrating substantial equivalence is:

• Performance specifications derived from legally marketed predicate devices.
• Adherence to recognized international standards (e.g., ISO 14801 for mechanical properties, ISO 10993-1 for biocompatibility, ISO 11137 for sterilization) for manufacturing and material properties.
• Results of non-clinical bench testing to confirm physical and mechanical performance characteristics.

There is no "expert consensus," "pathology," or "outcomes data" in the typical sense of a clinical study since none were performed.

8. The Sample Size for the Training Set

This information is N/A. The device is a physical product, not an AI/ML model that requires a "training set."

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

This information is N/A as there is no training set for a physical device.

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The Bladder Scanner is B-mode pulsed-echo ultrasound device adopts a 3D mechanical fan scanning probe for the ultrasonic scanning for bladder and measures the bladder volume from the abdominal surface of ultrasonic imaging. The Bladder Scanner is intended to be used only in hospital and by qualified medical professionals.

The Bladder Scanner (Model: AS-2) is a hand-held battery-operated device, it provides non-invasive bladder volume measurement utilizing real-time ultrasound imaging. The proposed device consists of the main unit (include 3D probe), battery power adapter and USB charging cable.

This FDA 510(k) summary provides information for a device called "Bladder Scanner" (Model: AS-2) by Avantsonic Technology Co., Ltd. The document primarily focuses on establishing substantial equivalence to a predicate device (K201316 Bladder Scanner by Suzhou Peaksonic Medical Technology Co., Ltd.) rather than detailing an independent study against acceptance criteria. However, it does state the acceptance criteria for volume measurement accuracy and reports whether the device met these criteria through non-clinical testing.

Here's a breakdown of the requested information based on the provided text:

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

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